Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an abc frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.publishedVersio

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Split converter-fed SRM drive for flexible charging in EV/HEV applications

Electric vehicles (EVs) and hybrid EVs are the way forward for green transportation and for establishing low-carbon economy. This paper presents a split converter-fed four-phase switched reluctance motor (SRM) drive to realize flexible integrated charging functions (dc and ac sources). The machine is featured with a central-tapped winding node, eight stator slots, and six rotor poles (8/6). In the driving mode, the developed topology has the same characteristics as the traditional asymmetric bridge topology but better fault tolerance. The proposed system supports battery energy balance and on-board dc and ac charging. When connecting with an ac power grid, the proposed topology has a merit of the multilevel converter; the charging current control can be achieved by the improved hysteresis control. The energy flow between the two batteries is balanced by the hysteresis control based on their state-of-charge conditions. Simulation results in MATLAB/Simulink and experiments on a 150-W prototype SRM validate the effectiveness of the proposed technologies, which may provide a solution to EV charging issues associated with significant infrastructure requirements

A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030

As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world's energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country's 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China

Traction motors for electric vehicles: Maximization of mechanical efficiency – A review

With the accelerating electrification revolution, new challenges and opportunities are yet emerging, despite range anxiety is still one of the biggest obstacles. Battery has been in the spotlight for resolving this problem, but other critical vehicle components such as traction motors are the key to efficient propulsion. Traction motor design involves a multidisciplinary approach, with still significant room for improvement in terms of efficiency. Therefore, this paper provides a comprehensive review of scientific literature looking at various aspects of traction motors to maximize mechanical efficiency for the application to high-performance Battery Electric Vehicles. At first, and overview on the mechanical design of electric motors is presented, focusing on topology selection, efficiency, transmission systems, and vehicle layouts; Special attention is then paid to the thermal management, as it is one of the main aspects that affects the global efficiency of such machines; thirdly, the paper presents a discussion on possible future trends to tackle ongoing challenges and to further enhance the performance of traction motors

Condition monitoring system and faults detection for impedance bonds from railway infrastructure

Nowadays, sensors and condition monitoring systems are expanding rapidly and becoming cheaper. This contributes to increasing developments in condition monitoring in railway transport infrastructure. A condition monitoring system that uses an online device and sensors to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and diagnosis of impedance bonds. The impedance bond condition is monitored in real-time using current and temperature sensors, providing early warning if predefined thresholds are exceeded in terms of currents, imbalance currents, and temperatures. The proposed method and the developed monitoring device have been validated in the railway laboratory to confirm its capability to detect defects. The acquired parameters from impedance bonds are used to extract thermal stresses and technical conditions of this equipment. Experimental results and appropriate data analysis are included in the article.info:eu-repo/semantics/publishedVersio

Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles

With the increase in usage of electric vehicles (EVs), the demand for lithium ion (Li-ion) batteries is also on the rise. A Li-ion battery pack in an EV consists of hundreds of cells and requires a battery management system (BMS). The BMS plays an important role in ensuring the safe and reliable operation of the battery in EVs. Its performance relies on the measurements of voltage, current and temperature from the cells through sensors. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods are mostly state-observer-based. State observer methods work under the assumption that the model parameters remain constant during operation. Through experimental results, this thesis shows that degradation can affect the long-term performance of the battery and its model parameters, hence it can cause false fault detection in state observer FDI schemes. This thesis also presents a novel model-based sensor FDI scheme for a Li-ion cell, that takes into consideration battery degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real-time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage sensor faults are isolated based on the responsiveness of the parameters when each fault occurs. Finally, the proposed FDI scheme is validated by conducting a series of experiments and simulations. Various injection times, fault sizes, fault types and cell capacities are considered. The results show that the proposed scheme consistently detects and isolates voltage and current sensor faults at different cell capacities in a reasonable time, with no false or missed detection. The preliminary findings are promising, but in order for the proposed FDI scheme to be utilized in practical settings, more work is needed to be done. The scheme should be expanded to include FDI for temperature sensors. In addition, other battery models as well as other fault diagnosis methods, specifically knowledge-based ones, should be investigated. Furthermore, additional experiments, including longer test cycles and extension to modules and packs testing, need to be conducted to obtain more data to improve the reliability of the FDI scheme