An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems

Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions

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

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

Evaluation of stochastic optimisation algorithms for induction machine winding fault identification

PhD ThesisThis thesis is concerned with parameters identification and winding fault detection in induction motors using three different stochastic optimisation algorithms, namely genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Although induction motors are highly reliable, require low maintenance and have relatively high efficiency, they are subject to many electrical and mechanical types of faults. Undetected faults can lead to serious machine failures. Fault identification is, therefore, essential in order to detect and diagnose potential failures in electrical motors. Conventional methods of fault detection usually involve embedding sensors in the machines, but these are very expensive. The condition monitoring technique proposed in this thesis flags the presence of a winding fault and provides information about its nature and location by using an optimisation stochastic algorithm in conjunction with measured time domain voltage, stator current data and rotor speed data. This technique requires a mathematical ABCabc model of the three-phase induction motor. The performance of the three stochastic search methods is evaluated in this thesis for their use to identify open-circuit faults in the stator and rotor windings of a three-phase induction motor. The proposed fault detection technique is validated through the use of experimental data collected under steady-state operating conditions. Time domain terminal voltages and the rotor speed are used as input data for the induction motor model while the outputs are the calculated stator currents. These calculated currents are compared to the measured currents to produce a set of current errors that are integrated and summed to give an overall error function. Fault identification is achieved by adjusting the model parameters off-line using the stochastic search method to minimise this error function. The estimate values for the winding parameters give the best possible match between the performance of the faulty experimental machine and its mathematical ABCabc model. These estimates of the values of the motor winding parameters are used in the detection of the development of faults by identifying both the location and the nature of the winding fault. The effectiveness of the three stochastic methods to identify stator and rotor winding faults are compared in terms of the required computation resources and their success rates in converging to a solution.Ministry of Higher Education of Libyan Government

Motor Fault Diagnosis Using Higher Order Statistical Analysis of Motor Power Supply Parameters

Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

Condition monitoring of broken rotor bars using a hybrid FMM-GA model

A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors