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

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

A hybrid intelligent technique for induction motor condition monitoring

The objective of this research is to advance the field of condition monitoring and fault diagnosis for induction motors. This involves processing the signals produced by induction motors, classifying the types and estimating the severity of induction motors faults. A typical process of condition monitoring and fault diagnosis for induction motors consists of four steps: data acquisition, signal analysis, fault detection and post-processing. A description of various kinds of faults that can occur in induction motors is presented. The features reflecting faults are usually embedded in transient motor signals. The signal analysis is a very important step in the motor fault diagnosis process, which is to extract features which are related to specific fault modes. The signal analysis methods available in feature extraction for motor signals are discussed. The wavelet packet decomposition results consist of the time-frequency representation of a signal in the same time, which is inherently suited to the transient events in the motor fault signals. The wavelet packet transform-based analysis method is proposed to extract the features of motor signals. Fault detection has to establish a relationship between the motor symptoms and the condition. Classifying motor condition and estimating the severity of faults from the motor signals have never been easy tasks and they are affected by many factors. AI techniques, such as expert system (ES), fuzzy logic system (FLS), artificial neural network (ANN) and support vector machine (SVM), have been applied in fault diagnosis of very complex system, where accurate mathematical models are difficult to be built. These techniques use association, reasoning and decision making processes as would the human brain in solving diagnostic problems. ANN is a computation and information processing method that mimics the process found in biological neurons. But when ANN-based methods are used for fault diagnosis, local minimums caused by the traditional training algorithms often result in large approximation error that may destroy their reliability. In this research, a novel method of condition monitoring and fault diagnosis for induction motor is proposed using hybrid intelligent techniques based on WPT. ANN is trained by improved genetic algorithm (IGA). WPT is used to decompose motor signals to extract the feature parameters. The extracted features with different frequency resolutions are used as the input of ANN for the fault diagnosis. Finally, the proposed method is tested in 1.5 kW and 3.7 kW induction motor rigs. The experimental results demonstrate that the proposed method improves the sensitivity and accuracy of the ANN-based methods of condition monitoring and fault diagnosis for induction motors.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Faults Identification in Three-Phase Induction Motors Using Support Vector Machines

Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques for fast and reliable operation. Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) have been applied in induction motor relays. Though the ANN based methods are reliable, the selection of the ANN structures and training is time consuming. Recently it is observed that the AI techniques such as Support Vector Machines (SVM) are alleviating some of the limitations of ANN method. The objectives of this study are to develop a SVM based induction motor external faults identifier and study its performance with real-time induction motor faults data. Data collected from a 1/3 hp, 208 V three-phase squirrel cage induction motor is used in this project. Radial Bases Function Kernel is used to train and test the SVM, though the effect of other Kernel functions was also studied. The proposed SVM method uses RMS values of three-phase voltages and currents as inputs. The testing results showed the efficacy of the SVM based method for identifying the external faults experienced by 3-phase induction motors. It is observed that the performance of the SVM based method is better than the ANN based method both in model creation and testing accuracy

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

An investigation into current and vibration signatures of three phase induction motors

This research aimed at investigating the relationship between three phase induction motors vibration (MVS) and current signatures (MCS). This is essential due to the cost of vibration measuring equipment and in cases where vibration of interest point is not accessible; such as electrical submersible pumps (ESP) used in oil industry. A mathematical model was developed to understand the effects of two types of induction motors common faults; rotor bar imperfections and phase imbalance on the motor vibration and current signatures. An automated test facility was developed in which 1.1 kW three phase motor could be tested under varying shaft rotation speeds and loads for validating the developed model. Time and frequency domains statistical parameters of the measured signals were calculated for fault detection and assessing its severity. The measured signals were also processed using the short time Fourier transform (STFT), the Wigner-Ville distribution (WVD), the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) and wavelet multi-resolution analysis (MRA). The non-stationary components, representing faults within induction motor measured vibration and current signals, were successfully detected using wavelet decomposition technique. An effective alternative to direct vibration measurement scheme, based on radial basis function networks, was developed to the reconstruction of motor vibration using measurements of one phase of the motor current. It was found that this method captured the features of induction motor faults with reasonable degrees of accuracy. Another method was also developed for the early detection and diagnosis of faults using an enhanced power factor method. Experimental results confirmed that the power factor can be used successfully for induction motor fault diagnosis and is also promising in assessing fault severity. The suggested two methods offer inexpensive, reliable and non-intrusive condition monitoring tools that suits real-time applications. Directions for further work were also outlined

Induction motor bearing fault detection using a sensorless approach

Continuous condition assessment of induction motors is very important due to its potential to reduce down-time and manpower needed in industry. Rolling element bearing faults result in more than 40% of all induction motor failures. Vibration analysis has been utilized to detect bearing faults for years. However, vibration sensors and expert vibration interpretation are expensive. This limitation prevents widespread monitoring of continuous bearing conditions in induction motors, which provides better performance compared to periodic monitoring, a typical practice for motor bearing maintenance in industry. A strong motivation exists for finding a costeffective approach for the detection of bearing faults. Motor terminal signals have attracted much attention. However, not many papers in the literature address this issue as it relates to bearing faults, because of the difficulties in effective detection. In this research, an incipient bearing fault detection method for induction motors is proposed based on the analysis of motor terminal voltages and currents. The basic idea of this method is to detect changes in amplitude modulation between the spatial harmonics caused by bearing faults and the supply fundamental frequency. This amplitude modulation relationship can be isolated using the phase coupling property. An Amplitude Modulation Detector (AMD), developed from higher order spectrum estimation, correctly captures the phase coupling and isolates these modulation relationships. In this research, in-situ bearing damage experiments are conducted so that the accelerated life span of the bearing can be recorded and investigated. Experimental results shown in this dissertation are based on different power supplies, load levels, VSI control schemes, and motor operating conditions. Taking the mechanical vibration indicator as a reference for fault detection, the proposed method is demonstrated to be effective in detecting incipient bearing faults in induction motors. If motors are operating at near steady state conditions, then experimental results show that the bearing fault detection rate of the proposed approach is 100%, while no false alarms are recorded

Robust condition monitoring for modern power conversion

The entire US electrical grid contains assets valued at approximately $800 billion, and many of these assets are nearing the end of their design lifetimes. In addition, there is a growing dependence upon power electronics in mission-critical assets (i.e. for drives in power plants and naval ships, wind farms, and within the oil and natural-gas industries). These assets must be monitored. Diagnostic algorithms have been developed to use certain key performance indicators (KPI) to detect incipient failures in electric machines and drives. This work was designed to be operated in real-time on operational machines and drives. For example the technique can detect impending failures in both mechanical and electrical components of a motor as well as semiconductor switches in power electronic drives. When monitoring power electronic drives, one is typically interested in the failure of power semiconductors and capacitors. To detect incipient faults in IGBTs, for instance, one must be able to track KPIs such as the on-state voltage and gate charge. This is particularly challenging in drives where one must measure voltages on the order of one or two volts in the presence of significant EMI. Sensing techniques have been developed to allow these signals to be reliably acquired and transmitted to the controller. This dissertation proposes a conservative approach for condition monitoring that uses communications and cloud-based analytics for condition monitoring of power conversion assets. Some of the potential benefits include lifetime extension of assets, improved efficiency and controllability, and reductions in operating costs especially with remotely located equipment

Faults Detection for Power Systems

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