The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSMs) are currently very relevant because of the increasing use of these drives in safety-critical devices. Every year, more and more articles are published on this subject. Therefore, the objective of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, and rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods that use the analysis of signals of the control structure for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of failures of PMSM drives. Based on the analyses presented in the literature, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

This paper deals with monitoring of interturn short circuits of induction-motor (IM) stator windings operating under the direct field-oriented control (DFOC). Time transients are presented to illustrate the performance of the field-oriented speed control under the stator-winding damage. Novel detection algorithm is introduced, based on an analysis of the internal signals from the control structure: controller outputs and control path decoupling variables. The spectral analysis of these signals is used to detect the stator-winding incipient damages. Significant changes of the constant component f _dc and the double-fundamental harmonic component 2f _s observed in these signals' spectra under the stator-winding failure become the basis for the failure diagnostics in various operating conditions of the DFOC IM drive. The superiority of the proposed detection algorithm over the classical motor current signature analysis (MCSA) method is described. The online monitoring process is shown. Simulation and experimental results are presented to validate the proposed detection algorithm.

The diagnosis of permanent magnet synchronous motor (PMSM) faults has been the subject of much research in recent years, due to the growing reliability and safety requirements for drive systems. This article concerns PMSM stator winding fault detection and classification. A novel intelligent diagnosis approach is proposed, based on the bispectrum analysis of a stator phase current and the convolutional neural network (CNN). Rather than using raw phase current signals, bispectrum is applied for symptom extraction and utilized as the input for a pretrained CNN model. The CNN model is used for automatic inference on the winding condition of the PMSM stator. Experimental results are presented to validate the proposed algorithm. The classification effectiveness of the developed CNN is as high as 99.4%. This article also presents the possibility of improving the accuracy of the CNN model and reducing the training time by properly tuning the training parameters. The CNN model learning time is only about one minute. The fault classifier model is developed in Python programming language, avoiding the cost of purchasing additional software.

Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.