Kjell Gunnar Robbersmyr

• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Show all 7 contributors for this article] (2023). Motor Bearing Fault Classification using Laser Sensor and Light Weight CNN, 2023 IEEE International Symposium on Smart Electronic Systems (iSES). IEEE conference proceedings. ISSN 979-8-3503-8324-9. p. 27–31. doi: 10.1109/iSES58672.2023.00017.
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2023). Development and Extended Validation of a Lumped Parameter Prediction Model for Analysing Injury Parameters in a Vehicle Crash. In Wagner, Gerd; Werner, Frank; Tuncer, Oren & De Rango, Floriano (Ed.), Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2021. Springer. ISSN 978-3-031-23149-0. doi: 10.1007/978-3-031-23149-0_1.
• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates, 2023 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE conference proceedings. ISSN 979-8-3503-9899-1. doi: 10.1109/IEMDC55163.2023.10238950.
• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2023). Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). ISSN 2644-1284. 4, p. 762–772. doi: 10.1109/OJIES.2024.3350443. Full text in Research Archive
• Nguyen, Hoang Nam Du; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2023). Early detection of rolling bearing faults using long short-term memory, 2023 6th International Conference on Control, Robotics and Informatics. IEEE conference proceedings. ISSN 979-8-3503-2328-3. p. 46–51. doi: 10.1109/ICCRI58865.2023.00016.
• Skår, Eirik Magnus; Kloumann, Jon-Erick; Robbersmyr, Kjell Gunnar & Løvåsen, Torfinn (2023). Anomaly Detection in Robotic Welds - Investigation of LSTM Autoencoder Model Performance. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. p. 265–270. doi: 10.1109/ICCMA59762.2023.10374923.
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Vysochinskiy, Dmitry & Robbersmyr, Kjell Gunnar (2023). A Numerical Comparison of Lagrange and Kane’s Method for Modeling and Crashworthiness Assessment of a Modified Vehicle. In Wagner, Gerd; Werner, Frank & De Rango, Floriano (Ed.), Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2022. Springer. ISSN 978-3-031-43823-3. p. 156–176. doi: 10.1007/978-3-031-43824-0_9.
• Nguyen, Nam Du Hoang; Huynh, Khang & Robbersmyr, Kjell Gunnar (2023). Current and Stray Flux Sensors for Anomaly Detection in PMSM Drive based on Gradient Boosting Machine. Proceedings of IEEE Sensors. ISSN 1930-0395.
• Salata, Gianluca; Cenkeramaddi, Linga Reddy; Huynh, Khang; Robbersmyr, Kjell Gunnar & Jha, Ajit (2023). Time-Frequency Analysis and Fault Prediction of Motor Bearings Using Millimeter-Wave Radar. IEEE Sensors Journal. ISSN 1530-437X. 23(16), p. 18718–18728. doi: 10.1109/JSEN.2023.3292899. Show summary

  Industrial motors are ubiquitous and used extensively in various industrial applications. The aim of increasing productivity, efficiency, lowering down, and mean time between failures while maintaining safety necessitates the continuous condition monitoring of abnormalities, faults in motors, and even predict them as early as possible to maintain their smooth functioning. In this article, we propose and experimentally demonstrate the use of millimeter-wave (mmW) radar for noncontact, nondestructive detection, characterization, and finally prediction of the probability of failure by interrogating its vibrating shaft at a given set of bearing conditions. Precisely, we obtain experimental raw 1-D time series data from mmW radar and transform them into time–frequency representations using wavelet transformation. Then, the features are extracted from the time–frequency representations, specifically the instantaneous velocity trajectory that distinguishes the vibrating shaft due to faulty bearing from healthy bearing. Furthermore, using maximum likelihood estimation (MLE), the velocity profile of the vibrating shaft follows Gaussian distribution from collected data at different motor shaft rotations per second (rps) (5, 15, and 25 Hz) with healthy and faulty bearings. While the mean of distribution for all the cases remains constant, the variance of latter increases with the increase in rps. Finally, the probabilistic approach and the model parameters are exploited to quantify the probability of motor failure in terms of complementary cumulative distribution function (CCDF). It is observed that the CCDF for the faulty case is greater than that of healthy case for all velocities and rps.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Vysochinskiy, Dmitry & Robbersmyr, Kjell Gunnar (2022). Modeling of Modified Vehicle Crashworthiness using a Double Compound Pendulum. In Wagner, Gerd; Werner, Frank & De Rango, Floriano (Ed.), Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress. ISSN 978-989-758-578-4. p. 102–111. doi: https:/doi.org/10.5220/0011306100003274. Full text in Research Archive Show summary

  Vehicle crash modeling has been a challenge for researchers for several decades. Occupant injury prevention and prediction is a critical area within vehicle safety design. The modeling of material failure in structural members during a full frontal crash has been presented in this paper. This study presents a Lumped Parameter Model (LPM) with an elastic double compound pendulum replicating the impact kinematics. The model defined using Lagrangian formulation; presents a novel methodology to represent material fracture caused due to heat affected zones or welding in Ultra High Strength Steels (UHSS) in a non-linear crash event. The material fracture leads to rotation of the vehicle; presented in the form of torsional springs in the LPM developed in this study. The Simulink model has been validated with a finite element simulation and shows good correlation to predict parameters crucial to design for occupant protection in a vehicle crash.

• Sanfilippo, Filippo; Thorstensen, Rein Terje; Jha, Ajit; Jiang, Zhiyu & Robbersmyr, Kjell Gunnar (2022). A Perspective Review on Digital Twins for Roads, Bridges, and Civil Infrastructures. In -, IEEE (Eds.), IEEE 2022 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-7095-7. doi: 10.1109/ICECCME55909.2022.9988693.
• Attestog, Sveinung; Senanayaka, Jagath Sri Lal; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2022). Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 19(9), p. 9291–9301. doi: 10.1109/TII.2022.3227628. Full text in Research Archive
• Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2022). RMS Based Health Indicators for Remaining Useful Lifetime Estimation of Bearings. MIC Journal: Modeling, Identification and Control. ISSN 0332-7353. 43(1), p. 21–38. doi: 10.4173/mic.2022.1.3. Full text in Research Archive Show summary

  Estimating the remaining useful life (RUL) of bearings from healthy to faulty is important for predictive maintenance. The bearing fault severity can be estimated based on the energy or root mean square (RMS) of vibration signals, and a stopping criterion can be set based on a threshold given by an ISO standard. However, the vibration RMS is often not monotonically increasing with damage, which renders a challenge for predicting the RUL. This study proposes a novel method for splitting the vibration signal into multiple frequency bands before RMS calculations to generate multiple health indicators. Monotonic health indicators are identified using the Spearman coefficient, and the RUL is afterward estimated for each indicator using a suitable model and parameter update scheme. Historical failure data is not required to set any parameters. The proposed method is tested with the Paris' law, where parameters are updated by particle filters. Experimental results from two test rigs validate the performance of the proposed method.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2022). Vehicle crashworthiness performance in frontal impact: Mathematical model using elastic pendulum. Mechanics research communications. ISSN 0093-6413. 124. doi: 10.1016/j.mechrescom.2022.103954. Full text in Research Archive Show summary

  Vehicle occupant injuries due to collisions cause many fatalities every year. Safe vehicle design plays a critical role in averting serious injuries to occupants and vulnerable road users in the event of a crash. In this paper we study a full frontal vehicle crash against a rigid barrier introducing a Lumped Parameter Model (LPM) inspired by the elastic pendulum motion. The model uses polar coordinates to simplify the problem and the governing equations have been defined using Lagrangian formulation. The Simulink model has been validated against Finite Element (FE) data demonstrating good correlation with pitching angle and maximum crush of the vehicle. These parameters are crucial for designing vehicles which efficiently protect occupants.

• Nyberg, Svein Olav Glesaaen; Robbersmyr, Kjell Gunnar; Holm, Jan Andreas & Sanfilippo, Filippo (2022). Tensile Experiments on Adhesion Between Aluminium Profiles and Glass. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 407–418. doi: 10.1007/978-3-031-10525-8_32. Show summary

  In this work, the effects of the adhesion between aluminium profiles and glass are studied from a static tensile perspective. A series of stretch curves are analysed from their derivatives to find their points of float and maximum load bearing. The variable factors are glass type, and type of connection: i.e., edge adhesive, side (fugue) adhesive, and excessive fugue adhesive, for short named fugue-edge. The stretch data imply four quantities to analyse and compare: displacement and load respectively at float and at max load. The results are first compared factor group against factor group. With this tool, only a few significant conclusions may be found. The second comparison is by means of the more robust statistical tools of linear regression and analysis of variance (ANOVA), with conclusions about which factors are significant, and then about the size of the effect on the four variables under study. This forms the basis for a recommendations for how to obtain the strongest possible glass-frame system.

• Husebø, Arild Bergesen; Huynh, Khang; Robbersmyr, Kjell Gunnar & Klausen, Andreas (2022). Classification of Mechanical Fault-Excited Events Based on Frequency. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 380–392. doi: 10.1007/978-3-031-10525-8_30. Show summary

  We propose a method for classifying periodic events generated at one or multiple frequencies on any one-dimensional space. This is very useful in problems where you need to find the type of event based on observations of location, e.g. in time. For each frequency, all events are mapped into periodic axes, which are represented independently of each other. Using an expectation-maximization algorithm, we can fit distributions to the events and classify them using maximum likelihood. The proposed method is applied to two mechanical faulty cases: a defect rolling-element bearing, and a gearbox with defect teeth. We show very good classification results in cases of multiple event types of similar frequency, multiple event types of different frequencies, and combinations of the two for artificially generated events.

• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2022). An Overview of Data Based Predictive Modeling Techniques Used in Analysis of Vehicle Crash Severity. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 355–379. doi: 10.1007/978-3-031-10525-8_28. Show summary

  Accident injury prediction is a crucial constituent to reducing fatalities linked to vehicle crashes. The vehicle development process and road safety planning includes also the injury prediction for occupants and Vulnerable Road Users (VRUs) in a vehicle crash and the identification of the factors responsible for increased traffic collision injuries. This paper reviews the different data-based prediction techniques to modeling a vehicle crash event, crash frequency and crash severity. Machine learning (ML) is a research field which has gained impetus in the recent years and is widely used in different engineering applications; including injury prediction in vehicle collisions. The paper is divided into two major sections; the first section presents an overview of the existing predictive models for estimating injury severity in a crash event to occupants and VRUs and the second section describes the applications of data-based modeling techniques to predict crash frequency in different traffic scenarios. We also discuss possible future applications of data-based modeling techniques in this domain.

• Knausgård, Kristian Muri; Skar, Siv; Sanfilippo, Filippo; Buldenko, Albert; Lindheim, Henning & Lunde, Jakob E. [Show all 8 contributors for this article] (2022). Branch-Manoeuvring Capable Pipe Cleaning Robot for Aquaponic Systems. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 107–118. doi: 10.1007/978-3-031-10525-8_9. Show summary

  Aquaponic systems are engineered ecosystems combining aquaculture and plant production. Nutrient rich water is continuously circulating through the system from aquaculture tanks. A biofilter with nitrifying bacteria breaks down fish metabolism ammonia into nitrite and nitrate, which plants and makes the aquaculture wastewater into valued organic fertiliser for the plants, containing essential macro and micro elements. At the same time, the plants are cleaning the water by absorbing ammonia from the fish tanks before it reaches dangerous levels for the aquatic animals. In principle, the only external input is energy, mainly in the form of light and heat, but fish food is also commonly provided. Growing fish food is potentially feasible in a closed loop system, hence aquaponic systems can possibly be an important source of proteins and other important nutrition when, for example, colonising other planets in the future. Fully autonomous aquaponic systems are currently not available. This work aims at minimising manual labour related to cleaning pipes for water transport. The cleaning process must be friendly to both plants and aquatic animals. Hence, in this work, pure mechanical cleaning is adopted. A novel belt-driven continuum robot capable of travelling through small/medium diameter pipes and manoeuvring branches and bends, is designed and tested. The robot is modular and can be extended with different cleaning modules through an interface providing CAN-bus network and electric power. The flexible continuum modules of the robot are characterised. Experimental results demonstrate that the robot is able to travel through pipes with diameters varying from 50 mm to 75 mm, and also capable of handling T-branches of up to 90∘.

• Ali, Ahmed Mohamed Salem; Huynh, Van Khang; Jiya, Immanuel Ninma & Robbersmyr, Kjell Gunnar (2022). Hybrid Three-Phase Transformer-Based Multilevel Inverter with Reduced Component Count. IEEE Access. ISSN 2169-3536. 10, p. 47754–47763. doi: 10.1109/ACCESS.2022.3171849. Full text in Research Archive
• Attestog, Sveinung; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2022). Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors. Sensors. ISSN 1424-8220. 22(3). doi: 10.3390/s22031216. Full text in Research Archive Show summary

  This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.

• Wæhle, Andreas; Nilsen, Jørgen; Tollevik, Sigurd; Rogne, Nora Kvingedal; Westbye, Stian & Robbersmyr, Kjell Gunnar (2021). The culture of product development in student organisations . Nordic Journal of STEM Education. ISSN 2535-4574. 5(1). doi: 10.5324/njsteme.v5i1.3920.
• Wæhle, Andreas; Rogne, Nora Kvingedal; Westbye, Stian; Nilsen, Jørgen; Tollevik, Sigurd & Robbersmyr, Kjell Gunnar (2021). How to succeed with volunteers on the basis of commitment and ownership. Nordic Journal of STEM Education. ISSN 2535-4574. 5(1). doi: 10.5324/njsteme.v5i1.3906.
• Jakobsen, Mette Mo; Tørdal, Sondre Sanden; Klausen, Andreas; Robbersmyr, Kjell Gunnar & Tyapin, Ilya (2021). Product development and project-based learning in Mechatronics, in the context of digitization and sustainability. Nordic Journal of STEM Education. ISSN 2535-4574. 5(1). doi: 10.5324/njsteme.v5i1.3946.
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2021). A Novel Technique for Modeling Vehicle Crash using Lumped Parameter Models, 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021). SciTePress. ISSN 978-989-758-528-9. p. 62–70. doi: 10.5220/0010529200620070. Full text in Research Archive
• Noorsumar, Gulshan; Vysochinskiy, Dmitry; Englund, Even; Robbersmyr, Kjell Gunnar & Rogovchenko, Svitlana (2021). Effect of welding and heat treatment on the properties of UHSS used in automotive industry. The European Physical Journal Conferences. ISSN 2101-6275. 250. doi: 10.1051/epjconf/202125005015. Full text in Research Archive
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2021). Mathematical models for assessment of vehicle crashworthiness: a review. International Journal of Crashworthiness. ISSN 1358-8265. doi: 10.1080/13588265.2021.1929760. Full text in Research Archive
• Husebø, Arild Bergesen; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). A Simple Time Domain-Based Method for Estimating the Resonance Frequency of a Bearing, 2021 24th International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-8-9865-1021-8. doi: 10.23919/ICEMS52562.2021.9634452.
• Ali, Ahmed Mohamed Salem; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2021). Novel Transformer-Based Six-Level Inverter with Low Component Count. In Bossche, Alex (Eds.), 2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe). IEEE conference proceedings. ISSN 978-9-0758-1537-5.
• Senanayaka, Jagath Sri Lal; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2021). CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features, IEEE 30th International Symposium on Industrial Electronics (ISIE). IEEE conference proceedings. ISSN 978-1-7281-5635-4. doi: 10.1109/ISIE45552.2021.9576257.
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Field reconstruction for modeling multiple faults in permanent magnet synchronous motors in transient states. IEEE Access. ISSN 2169-3536. 9, p. 127131–127140. doi: 10.1109/ACCESS.2021.3112224. Full text in Research Archive
• Salem, Ahmed; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Four-level three-phase inverter with reduced component count for low and medium voltage applications. IEEE Access. ISSN 2169-3536. 9, p. 35151–35163. doi: 10.1109/ACCESS.2021.3062110. Full text in Research Archive
• Gåsvik, Kjell; Robbersmyr, Kjell Gunnar & Vadseth, Trond Kjell (2020). Phase error analysis of clipped waveforms in surface topography measurement using projected fringes. Optics Communications. ISSN 0030-4018. 483. doi: 10.1016/j.optcom.2020.126601. Full text in Research Archive
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Improved Quadratic Time-frequency Distributions for Detecting Inter-turn Short Circuits of PMSMs in Transient States. In Liu, Yujing (Eds.), Proc. 2020 International Conference on Electrical Machines (ICEM). IEEE conference proceedings. ISSN 978-1-7281-9945-0. p. 1461–1467. doi: 10.1109/ICEM49940.2020.9271050. Full text in Research Archive
• Husebø, Arild Bergesen; Kandukuri, Surya Teja; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). ISSN 2325-016X. 5(1). Full text in Research Archive
• Klausen, Andreas; Kalaoja, Johannes; Kandukuri, Surya Teja & Robbersmyr, Kjell Gunnar (2020). Sensitivity Analysis Of Online Oil Quality Monitoring For Early Detection Of Water Ingress In Marine Propulsion Systems. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). ISSN 2325-016X. 5(1).
• Hemmer, Martin; Robbersmyr, Kjell Gunnar; Waag, Tor Inge; Albrigtsen, Rolf; Pedersen, Torfinn & Meyer, Thomas [Show all 7 contributors for this article] (2020). Rib-roller wear in tapered rolling element bearings: Analysis and development of test rig for condition monitoring. Lecture Notes in Mechanical Engineering. ISSN 2195-4356. p. 750–757. doi: 10.1007/978-3-030-48021-9_83. Show summary

  Rolling Element Bearings (REBs) are present in virtually all machines with moving or rotating parts, and are vital for proper performance and safe operation. Condition Monitoring (CM) of bearings often receive particular interest, as this component group rarely reach design lifetime and hence is responsible for unplanned machine downtime. Unplanned maintenance can represent a large cost which motivates development of improved CM methods for implementation of advanced maintenance regimes. Based on observations of a used bearing from an offshore drilling machine, wear on roller ends in the rib-roller contact area was identified as an area of interest for future research. A test rig for creating and observing accelerated roller end damage is developed, intended for use with vibration and Acoustic Emission (AE) sensors. In addition to normal continuous rotation of the bearing, the test rig is also designed with performing oscillation motion tests in mind. This mode of operation is of interest to manufacturers and end users of cranes and winches with heave compensation. Plans and challenges for future work are also discussed, in conjunction with the experimental setup.

• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Towards Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. doi: 10.1109/TII.2020.3014422. Show summary

  This paper proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labelled training data. The proposed method combines one-class support vector machine (SVM)-based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the online diagnosis scheme to learn features based on the latest data. The effectiveness of the scheme is validated via a comparison study using experimental data from an in-house test setup. Finally, the online implementation of the proposed scheme on the test setup is briefly introduced.

• Gåsvik, Kjell; Robbersmyr, Kjell G. & Vadseth, Trond Kjell (2020). Online dimensional control of rolled steel profiles using projected fringes. The International Journal of Advanced Manufacturing Technology. ISSN 0268-3768. 107(3-4), p. 1725–1730. doi: 10.1007/s00170-020-05078-3. Full text in Research Archive Show summary

  Fringe projection is a versatile method for mapping the topography of surfaces. In this paper, it is used to measure the defects on the head of railroad rails while the rails are moving. Railroad rails are made by hot rolling. The quality of the finished product is generally good, but surface texture will deteriorate with increasing temperature. A method for online inspection therefore is very desirable. In the present experiment, dimensional inspection of the railroad rails was made online while moving at a speed of 1–2 m/s. Therefore, it is important to minimize the registration time. To achieve this, we apply a method of fringe location with sub-pixel accuracy that requires only a single exposure for each registration.

• Salem, Ahmed; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodríguez, José (2020). Voltage Source Multilevel Inverters with Reduced Device Count: Topological Review and Novel Comparative Factors. IEEE transactions on power electronics. ISSN 0885-8993. 36(3), p. 2720–2747. doi: 10.1109/TPEL.2020.3011908. Show summary

  Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems due to their features of high-quality produced waveforms, modularity, transformerless operation, voltage, and current scalability, and fault-tolerant operation. However, these merits usually come with the cost of a high number of components. Over the past few years, proposing new MLIs with a lower component count has been one of the most active topics in power electronics. The first aim of this article is to update and summarize the recently developed multilevel topologies with a reduced component count, based on their advantages, disadvantages, construction, and specific applications. Within the framework, both single-phase and three-phase topologies with symmetrical and asymmetrical operations are taken into consideration via a detailed comparison in terms of the used component count and type. The second objective is to propose a comparative method with novel factors to take component ratings into account. The effectiveness of the proposed method is verified by a comparative study.

• Salem, Ahmed; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodriguez, Jose (2020). Novel Three-Phase Multilevel Inverter with Reduced Components for Low- and High-Voltage Applications. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. doi: 10.1109/TIE.2020.2998752.
• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2020). Crash Response of a Repaired Vehicle-Influence of Welding UHSS Members. SAE technical paper series. ISSN 0148-7191. 1- 2020(April), p. 1–5. doi: 10.4271/2020-01-0197. Full text in Research Archive
• Hemmer, Martin; Klausen, Andreas; Huynh, Khang; Robbersmyr, Kjell Gunnar & Waag, Tor Inge (2020). Health Indicator for Low-speed Axial Bearings using Variational Autoencoders. IEEE Access. ISSN 2169-3536. 8, p. 35842–35852. doi: 10.1109/ACCESS.2020.2974942. Full text in Research Archive Show summary

  This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds.

• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Modelling Demagnetized Permanent Magnet Synchronous Generators using Permeance Network Model with Variable Flux Sources, 2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG). IEEE conference proceedings. ISSN 978-1-7281-5592-0. doi: 10.1109/COMPUMAG45669.2019.9032791. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework, 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE conference proceedings. ISSN 978-1-7281-1832-1. p. 264–270. doi: 10.1109/DEMPED.2019.8864889.
• Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Multi-band identification for enhancing bearing fault detection in variable speed conditions. Mechanical systems and signal processing. ISSN 0888-3270. 139. doi: 10.1016/j.ymssp.2019.106422.
• Hemmer, Martin; Klausen, Andreas; Huynh, Khang; Robbersmyr, Kjell Gunnar & Waag, Tor Inge (2019). Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data. International Journal of Prognostics and Health Management. ISSN 2153-2648. 10. Full text in Research Archive
• Ali, Ahmed Mohamed Salem; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodriguez, Jose (2019). Novel Three-Phase Multi-Level Inverter with Reduced Components, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE conference proceedings. ISSN 978-1-7281-4878-6. p. 6501–6506. doi: 10.1109/IECON.2019.8927732.
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE conference proceedings. ISSN 978-1-7281-4878-6. p. 5992–5997. doi: 10.1109/IECON.2019.8926983. Show summary

  Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under arbitrary load and speed variations in a laboratory setup of a pitch drive.

• Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). New Multilevel Inverter Topology with Reduced Component Count, EPE'19 ECCE Europe. IEEE conference proceedings. ISSN 978-9-0758-1531-3. doi: 10.23919/EPE.2019.8915400.
• Skylvik, Audun; Robbersmyr, Kjell Gunnar & Huynh, Khang (2019). Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network, 22nd International Conference on Electrical Machines and Systems (ICEMS) 2019. IEEE conference proceedings. ISSN 978-1-7281-3398-0. doi: 10.1109/ICEMS.2019.8921738. Show summary

  Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machine learning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density estimation. Further, several deep learning features are extracted using stacked autoencoder networks. The proposed scheme can predict the faults at high accuracy while reducing expertise demand. The proposed method is validated on an in-house laboratory setup under variable speed and load conditions, showing a classification accuracy over 95%. Furthermore, multiple support vector machine and k-nearest neighbor classifiers are tested with the same data for a comparative study. Finally, the performance of different fault classifiers are evaluated based on accuracy, computational burden and classification consistency.

• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Detecting Eccentricity and Demagnetization Fault of Permanent Magnet Synchronous Generators in Transient State, 22nd International Conference on Electrical Machines and Systems (ICEMS) 2019. IEEE conference proceedings. ISSN 978-1-7281-3398-0. doi: 10.1109/ICEMS.2019.8921753. Full text in Research Archive
• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine. IEEE transactions on industry applications. ISSN 0093-9994. 55(5), p. 5109–5118. doi: 10.1109/TIA.2019.2924866. Show summary

  Pitch systems are one of the components with the most frequent failure in wind turbines. This article presents a two-stage fault detection and classification scheme, for electric motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms, with electric pitch systems driven by induction motors as well as permanent magnet synchronous motors. The adopted strategy utilizes three-phase motor current sensing at the pitch drives for fault detection and only when a fault condition is detected at this stage, features extracted from the current signals are transmitted to a support vector machine classifier located centrally to the wind farm. The proposed method is validated in a laboratory setup of the pitch drive.

• Klausen, Andreas & Robbersmyr, Kjell Gunnar (2019). Cross-correlation of whitened vibration signals for low-speed bearing diagnostics. Mechanical systems and signal processing. ISSN 0888-3270. 118(1), p. 226–244. doi: 10.1016/j.ymssp.2018.08.048.
• Vangi, Dario; Cialdai, Carlo; Gulino, Michelangelo-Santo & Robbersmyr, Kjell Gunnar (2018). Vehicle accident databases: Correctness checks for accident kinematic data. Designs. ISSN 2411-9660. 2(1), p. 1–11. doi: 10.3390/designs2010004.
• Kandukuri, Surya Teja; Robbersmyr, Kjell Gunnar & Karimi, Hamid Reza (2018). Toward farm-level health management of wind turbine systems: status and scope for improvements. In Karimi, Hamid Reza (Eds.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. p. 149–168. doi: 10.1049/PBPO117E_ch6.
• Hemmer, Martin; Huynh, Khang; Robbersmyr, Kjell Gunnar; Waag, Tor Inge & Meyer, Thomas (2018). Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network. Designs. ISSN 2411-9660. doi: 10.3390/designs2040056.
• Kandukuri, Surya Teja; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2018). Fault diagnostics for electrically operated pitch systems in offshore wind turbines. In Karimi, Hamid Reza (Eds.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. p. 193–211. doi: 10.1049/pbpo117e_ch8.
• Huynh, Khang; Kandukuri, Surya Teja; Pawlus, Witold & Robbersmyr, Kjell Gunnar (2018). Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines, 2018 Eighth International Conference on Information Science and Technology (ICIST). IEEE conference proceedings. ISSN 978-1-5386-3782-1. p. 200–205. doi: 10.1109/ICIST.2018.8426188.
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. p. 1002–1007. doi: 10.23919/ICEMS.2018.8549293.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. p. 531–536. doi: 10.23919/ICEMS.2018.8549064.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. p. 537 –542. doi: 10.23919/ICEMS.2018.8549006.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 15(8), p. 4569–4579. doi: 10.1109/TII.2018.2883357. Show summary

  Detection and isolation of single and mixed-faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of Multilayer Perceptron (MLP)- and Convolutional Neural Network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional methods. The comparative study shows that accuracies and robustness of the individual MLP- and CNN-algorithms are better than those of the compared methods and can be significantly improved using data fusion at the feature level. Furthermore, the robustness of the algorithm is secured under noises by combining the results of individual classifiers.

• Mazurkiewicz, Lukasz; Baranowski, Pawel; Karimi, Hamid Reza; Damaziak, Krzysztof; Malachowski, Jerzy & Muszynski, Artur [Show all 9 contributors for this article] (2018). Improved child-resistant system for better side impact protection. The International Journal of Advanced Manufacturing Technology. ISSN 0268-3768. 97(9-12), p. 3925–3935. doi: 10.1007/s00170-018-2236-y.
• Munyazikwiye, Bernard; Vysochinskiy, Dmitry; Kdadyko, Mikhail & Robbersmyr, Kjell Gunnar (2018). Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models. Designs. ISSN 2411-9660. 2. doi: 10.3390/designs2040043. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning, Proceedings 2018 XIII International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2477-7. p. 1932–1938. doi: 10.1109/ICELMACH.2018.8507171.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks, Proceedings 2018 XIII International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2477-7. p. 1900 –1905. doi: 10.1109/ICELMACH.2018.8507096.
• Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Novel Threshold Calculations for Remaining Useful Lifetime Estimation of Rolling Element Bearings, Proceedings 2018 XIII International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2477-7. p. 1912–1918. doi: 10.1109/ICELMACH.2018.8507056.
• Bukala, Jakub; Damaziak, Krzysztof; Karimi, Hamid Reza; Malachowski, Jerzy & Robbersmyr, Kjell Gunnar (2018). Evolutionary computing methodology for small wind turbine supporting structures. The International Journal of Advanced Manufacturing Technology. ISSN 0268-3768. 100(9-12), p. 2741–2752. doi: 10.1007/s00170-018-2860-6.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Fault detection and classification of permanent magnet synchronous motor in variable load and speed conditions using order tracking and machine learning. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1037(3). doi: 10.1088/1742-6596/1037/3/032028.

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• Nguyen, Hoang Nam Du; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2023). Current and stray flux sensors for anomaly detection in PMSM drive based on gradient boosting machine.
• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Show all 7 contributors for this article] (2023). Motor Bearing Fault Classification using Laser Sensor and Light Weight CNN.
• Eid, Mahmoud Sayed Mahmoud; Huynh, van Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates.
• Kandukuri, Surya Teja & Robbersmyr, Kjell Gunnar (2021). Analytics for asset integrity management in wind farms (AIMWind).
• Nyberg, Svein Olav Glesaaen; Robbersmyr, Kjell Gunnar; Holm, Jan Andreas & Sanfilippo, Filippo (2021). Effects of Material Selection on Adhesion between glass and aluminium pre-recorded video for digital attendees.
• Wæhle, Andreas; Nilsen, Jørgen; Tollevik, Sigurd; Rogne, Nora; Westbye, Stian & Robbersmyr, Kjell Gunnar (2021). The culture of product development in student organizations.
• Jakobsen, Mette Mo; Tørdal, Sondre Sanden; Klausen, Andreas; Robbersmyr, Kjell Gunnar & Tyapin, Ilya (2021). Product development and project-based learning in Mechatronics, in the context of digitization and sustainability.
• Wæhle, Andreas Otter; Rogne, Nora; Westbye, Stian; Nilsen, Jørgen; Tollevik, Sigurd & Robbersmyr, Kjell Gunnar (2021). How to succeed with volunteers on the basis of commitment and ownership.
• Husebø, Arild Bergesen; Huynh, Khang; Robbersmyr, Kjell Gunnar & Klausen, Andreas (2021). Classification of Mechanical Fault-Excited Events based on Frequency.
• Nyberg, Svein Olav Glesaaen; Robbersmyr, Kjell Gunnar; Holm, Jan Andreas & Sanfilippo, Filippo (2021). Static Tensile Experiments on Adhesion between Aluminium Profiles and Glass. Show summary

  In this work, we study the effects of the adhesion between aluminium profiles and glass from a static tensile perspective. A series of stretch curves from a 2016 experiment are analysed from their derivatives to find their points of float and maximum load bearing. The variable factors in this experiment are glass type, and type of connection: edge gluing, side (fugue) gluing, and excessive fugue gluing, which we have named fugue-edge. The stretch data gives us four quantities to analyse and compare: displacement and load at respectively float and at max load. We first display the results by simply comparing factor group against factor group. Whit this tool, only a few significant conclusions may be found. We then look at the more powerful statistical tools of linear regression and Anova (analysis of variance), explain these tools, and then draw conclusions about which factors are significant, and then about the size of the effect on the four variables under study. This forms the basis for our recommendation to window producers on where to focus their attention for the strongest possible glass-frame system.

• Knausgård, Kristian Muri; Skar, Siv; Sanfilippo, Filippo; Buldenko, Albert; Lindheim, Henning & Lunde, Jakob [Show all 8 contributors for this article] (2021). Branch-Manoeuvring Capable Pipe Cleaning Robot for Aquaponic Systems. Show summary

  Aquaponic systems are engineered ecosystems combining aquaculture and plant production. Nutrient rich water is continuously circulating through the system from aquaculture tanks. A biofilter with nitrifying bacteria breaks down fish metabolism ammonia into nitrite and nitrate, which plants and makes the aquaculture wastewater into valued organic fertiliser for the plants, contenting essential macro and micro elements. At the same time, plants are cleaning the water by absorbing ammonia from the fish tanks before it reaches dangerous levels for the aquatic animals. In principle, the only external input is energy, mainly in the form of light and heat, but fish food is also commonly provided. Growing fish food is potentially feasible in a closed loop system, hence aquaponic systems can possibly be an important source of proteins and other important nutrition when, for example, colonising other planets in the future. Fully autonomous aquaponic systems are currently not available. This work aims at minimising manual labour related to cleaning pipes for water transport. The cleaning process must be friendly to both plants and aquatic animals. Hence, in this work, pure mechanical cleaning is adopted. A novel belt-driven continuum robot capable of travelling through small/medium diameter pipes and manoeuvring branches and bends, is designed and tested. The robot is modular and can be extended with different cleaning modules through an interface providing CAN-bus network and electric power. The flexible continuum modules of the robot are characterised. Experimental results demonstrate that the robot is able to travel through pipes of diameter 50 mm to 75 mm, and also capable of handling T-branches of up to 90 degrees.

• Sanfilippo, Filippo; Thorstensen, Rein Terje; Jha, Ajit; Jiang, Zhiyu; Knausgård, Kristian Muri & Robbersmyr, Kjell Gunnar (2021). A Perspective of Road Infrastructure and Bridges Digital Twins.
• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2021). An overview of Data Based Predictive Modeling Techniques used in Analysis of Vehicle Crash Severity.
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2021). A Novel Technique for Modeling Vehicle Crash using Lumped Parameter Models.
• Attestog, Sveinung; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2020). Improved Quadratic Time-Frequency Distributions for Fault Detection in Transient States. Show summary

  This paper aims to improve quadratic time-frequency distributions to adapt condition monitoring of electrical machines in transient states. Short-Time Fourier transform (STFT) has been a baseline signal processing technique for detecting fault characteristic frequencies. However, limits of window sizes due to loss of frequency- or time-resolution, make it hard to capture rapid changes in frequencies. Within this study, Choi-Williams and Wigner-Ville distributions are proposed to effectively detect peaks at characteristic frequencies while still maintaining low computation time. The improved quadratic time-frequency distributions allow for generating spectrograms of a longer lasting data signal and capturing multi-component signals with a better separation of the components than STFT. Further, the time resolution of the spectrograms generated by the proposed method is not affected by the window size. The effectiveness of the proposed methods is numerically verified from the data of an in-house test setup.

• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Electromagnetic and Vibration Modelling of an Electric Powertrain for a Simulation-driven Fault Diagnosis System.
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Modelling Demagnetized Permanent Magnet Synchronous Generator for Fault Diagnosis . Show summary

  Prediction of the lifetime for transformers is very important for maintenance and asset management. Finite element analysis was performed on a 5 MVA distribution transformers with aluminium foil-type windings and voltage rating 6600 V/23000 V. Electromagnetic modelling is implemented on the full three-phase transformer to calculate distributed losses, taking the skin effect into account. To reduce the computational burden, the distributed losses in one phase are used to analyse temperature rise in one phase of the transformer. The temperature rise results were used to determine the ageing rate of the transformer. Further, the influence of ambient temperature and cooling on the temperature rise and ageing rate of the transformer is numerically investigated.

• Hemmer, Martin; Robbersmyr, Kjell Gunnar; Waag, Tor Inge; Meyer, Thomas J. J.; Albrigtsen, Rolf & Pedersen, Torfinn [Show all 7 contributors for this article] (2018). Rib-Roller Wear in Tapered Rolling Element Bearings: Analysis and Development of Test Rig for Condition Monitoring.
• Robbersmyr, Kjell Gunnar & Knausgård, Kristian Muri (2018). Studentprosjekter 2018.
• Robbersmyr, Kjell Gunnar & Ottestad, Morten (2018). Mekatronikk.
• Robbersmyr, Kjell Gunnar; Choux, Martin Marie Hubert; Zhou, Jing & Knausgård, Kristian Muri (2018). Mekatronikk.
• Robbersmyr, Kjell Gunnar & Knausgård, Kristian Muri (2018). Mekatronikk.
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2023). Modeling, Simulation and Prediction of Vehicle Crashworthiness in Full Frontal Impact. University of Agder. ISSN 978-82-8427-113-2. Full text in Research Archive
• Attestog, Sveinung; Robbersmyr, Kjell Gunnar & Huynh, Khang (2022). Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations. University of Agder. ISSN 978-82-8427-095-1. Full text in Research Archive
• Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Multilevel Inverters with Reduced Component Count for Energy Systems. Universitetet i Agder. ISSN 978-82-8427-045-6. Full text in Research Archive Show summary

  Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems with a wide range of voltage levels, due to their attractive features of low harmonic contents, low dv/dt stress, low filtering requirements, low switching frequency, low electromagnetic interference (EMI), and employing low-rated semiconductor devices for producing high voltages. Further, some MLIs have a modularity feature, facilitating voltage and current scalability with high redundancy in switching states, allowing for fault-tolerant operations. The mentioned features are directly related to the number of generated voltage levels. However, enlarging level count renders challenges of requiring massive component counts, including DC sources, capacitors, power diodes, switches, inductors, and transformers. The high count of components negatively affects the size, cost, efficiency, lifespan, reliability, complexity of MLI-based energy conversion systems. Thus, proposing novel MLIs, which can enlarge the voltage level number with a low component count, is currently one of the most attractive topics in this research theme. In this dissertation, four three-phase topologies are proposed to mitigate the aforementioned shortcomings. Two transformerless topologies are proposed for low- and medium voltage applications, while two transformer-based topologies are intended for medium- and high-voltage applications. The proposed topologies have the key features of being capacitor-, diode-free, and low counts of DC sources, switches, and transformers. Further, three of them have a high modularity degree, allowing for higher voltage operations without increasing the voltage stress across the switches. The working principle of the proposed topologies are theoretically demonstrated, numerically verified, and experimentally validated through an in-house experimental setup. The effectiveness of the proposed topologies is proven through detailed comparative studies regarding component counts and voltage ratings. The proposed topologies are briefly described in this dissertation, while detailed explanations and results can be found in the appended papers.

• Munyazikwiye, Bernard & Robbersmyr, Kjell Gunnar (2020). Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models. 07 Media. ISSN 978-82-7117-961-8. Full text in Research Archive
• Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2020). Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion. 07 Media. ISSN 978-82-7117-971-7. Full text in Research Archive
• Hemmer, Martin & Robbersmyr, Kjell Gunnar (2020). Condition Monitoring Methods for Large, Low-speed Bearings. Universitetet i Agder. ISSN 978-82-7117-960-1. Full text in Research Archive
• Klausen, Andreas & Robbersmyr, Kjell Gunnar (2019). Condition monitoring of rolling element bearings during low and variable speed conditions. Universitetet i Agder. ISSN 978-82-7117-926-7. Full text in Research Archive
• Kandukuri, Surya Teja; Robbersmyr, Kjell Gunnar & Huynh, Khang (2018). Health Monitoring of Pitch and Yaw Systems in Offshore Wind Farms. Universitetet i Agder. ISSN 978-82-7117-891-8.
• Knausgård, Kristian Muri & Robbersmyr, Kjell Gunnar (2018). Studentprosjekter - innhenting og valg. Universitetet i Agder.

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