Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations

Paper VI is excluded from the dissertation until the article will be published.Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.publishedVersio

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

Computer Simulation of PMSM Motor with Five Phase Inverter Control using Signal Processing Techniques

The signal processing techniques and computer simulation play an important role in the fault diagnosis and tolerance of all types of machines in the first step of design. Permanent magnet synchronous motor (PMSM) and five phase inverter with sine wave pulse width modulation (SPWM) strategy is developed. The PMSM speed is controlled by vector control. In this work, a fault tolerant control (FTC) system in the PMSM using wavelet switching is introduced. The feature extraction property of wavelet analysis used the error as obtained by the wavelet de-noised signal as input to the mechanism unit to decide the healthy system. The diagnosis algorithm, which depends on both wavelet and vector control to generate PWM as current based manage any parameter variation. An open-end phase PMSM has a larger range of speed regulation than normal PMSM. Simulation results confirm the validity and effectiveness of the switching strategy

Modelling of a Line-Start Permanent Magnet Synchronous Motor, Using Empirical Parameters

Line-started permanent magnet synchronous motors emerged in the market in response to strict efficiency goals. Despite being a synchronous motor, the rotor of a line-started permanent magnet synchronous motor contains a squirrel cage and, consequently, the behaviour under transient periods and/or faulty operation is not the same as for a conventional synchronous motor. In order to study this kind of electrical machine, it is proposed in this paper an equivalent circuit model and a set of experimental tests to extract the parameters of the equivalent circuit of a line-started permanent magnet synchronous motor. To validate the presented approach, a computer model of the machine, based on the obtained parameters, was developed, and the simulation results were compared with the experimental motor performance.SFRH/BSAB/118741/2016info:eu-repo/semantics/publishedVersio

Hybrid Control Using Adaptive Fuzzy Sliding Mode for Diagnosis of Stator Fault in PMSM

In nonlinear control systems when we have a non-constant parameters, conventional control laws may be insufficient because they are not robust especially when the requirement on accuracy and other characteristics dynamic systems are strict. We must use control laws insensitive against to parameter variations, disturbance and nonlinearities. For this purpose, several tools are proposed in the literature, which is quoted a hybrid fuzzy logic and variable structure control (Fl_VSC). This per presents an application of the fuzzy logic scheme to control the speed of PMSM by taking account of the presence of interturn short circuit fault. We were interested in the sliding mode control (SMC) of the PMSM using controller’s fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC). The combination of these two theories has given great performance with fast dynamic response without overshoot. As it has a very robust control, insensitive against to parameters variation and external disturbances. Simulation results confirm the choice of hybrid controllers compared with the conventional controllers and grants a robust performance and precise response to the reference model regardless of load disturbance, stator faults and PMSM parameter uncertainties

Detection of inter-turns short circuits in permanent magnet synchronous motors operating under transient conditions by means of the zero sequence voltage

This work proposes the zero sequence voltage component (ZSVC) of the stator three-phase voltages as a method for detecting winding inter-turns short circuits in permanent magnet synchronous motors PMSM operating under transient conditions. Additionally it proves the linear relationship between the ZSVC and speed, which is effectively used as a fault severity index. The acquired ZSVC temporal signal is processed by means of the Hilbert-Huang transform (HHT). Experimental results presented in this work show the advantages of the method to provide helpful data for online diagnosis of stator winding inter-turn faults.Peer ReviewedPostprint (author’s final draft