Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.

In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented.

The paper describes a vector-controlled fault tolerant control (FTC) structure for permanent magnet synchronous motor (PMSM) drives. As a control algorithm, the classical field oriented control was applied. For the proper operation of this drive, minimum two current sensors are necessary, however, in the FTC drives additional redundant transducers are applied. Each measuring sensor, including current sensors, are susceptible to damage and can lead to unstable operation of the drive. The presented control structure, with a diagnostic and compensation system, is robust to the typical current sensor faults—lack of signal, intermittent signal, variable gain, signal noise and signal saturation. The fault detection algorithm is based on the signal method. The fault diagnostic system is tested in two control algorithms—the scalar control and vector control ones—to demonstrate the transient of the faulted signals, detection signals and detection time. After current sensor fault appearance, its influence on the control structure, especially speed transient, is compensated using non-sensitive components. The analysis is presented for all the abovementioned faults for different speed conditions.

The fault-tolerant control system (FTCS) for a permanent magnet synchronous motor (PMSM) is presented and an analysis of speed sensor faults is proposed in this paper. The detection and compensation of speed sensor faults is based on the simple comparison of the measured signals with the signals estimated by reduced-order sliding mode observer (SMO). The focus of the work is on the presentation of simulation and experimental research for the most common speed sensor failure types: complete signal loss, gain error, and measurement noise. Additionally, an analysis of different operating conditions of the PMSM drive system is presented, which highlights the system’s behaviour under various conditions. The presented algorithm enables the fast detection and compensation of failures with very short detection times, making it a versatile solution for a PMSM drive system and improving its overall reliability.

In this paper the current sensor fault detector for the permanent-magnet synchronous motor drive system has been presented. The solution is a known method used for induction motor drive systems, tested by authors in simulation for the PMSM drive system. The application is based on the current markers, which enable not only failure detection, but also the location of said failures. Detector operation is based only on the analysis of measurements from current sensors and does not require additional information about other state variables. The aim of the work is to present simulation and experimental studies in field-oriented control (FOC) for the tested current sensor fault detector for various operating conditions of the drive system—variable speed and load.