Fault detection and fault accommodation as well as fault tolerance are important issues in closed-loop controlled three-phase converters. For such systems, power semiconductors and sensors are the most affected components by the occurrence of failures. Therefore, fault diagnosis and remedial operating strategies involving such components are highly required. In this work we propose a new reliable approach which aims to diagnosis current sensors and power semiconductors open-circuit faults in a permanent magnet synchronous motors (PMSM) drives. The proposed FDI scheme uses only the measured currents, which makes it suitable for real time implementation. The performances of the proposed technique are analyzed through experiments.

A new approach for current sensor fault detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) drives is presented. Contrary to the classical approaches for sensors fault diagnosis, based on residual generation through observers or parity equations, the proposed technique uses the average normalised machine‐phase currents. The main advantages of this approach are that it does not need any information about the PMSM or inverter models, and it involves low tuning efforts, that make it suitable for real‐time implementation with good reliability. The good performance and the robustness of the proposed FDI approach are illustrated through experimental results.

In critical industrial applications fault diagnosis and fault tolerance are considered key features, in order to ensure the required reliability and availability levels. In this context, this paper proposes a new and effective diagnostic algorithm for power semiconductors open‐circuit faults, in three‐phase, two‐level, voltage‐source inverter‐fed permanent magnet synchronous machine (PMSM). The proposed method is based on the analysis of the errors between the reference currents and the PMSM stator predictive currents. Hence, for each phase of the PMSM two fault diagnostic variables have been defined, which allow the diagnosis of both single and multiple open‐circuit faults. The fuzzy logic approach is applied to the fault diagnostic variables in order to identify the faulty power switches. The method is experimentally validated on a model predictive controlled (MPC) permanent magnet synchronous motor (PMSM) drive, which shows the effectiveness of the fault diagnosis algorithm with a high robustness regarding the operating point and parameter variations of the PMSM drive system.

Fault diagnosis and localization are critical for maintaining the integrity of motor drive systems and optimizing maintenance interventions. This paper introduces a novel methodology for the rapid diagnosis and localization of semiconductor open-switch faults in power converters that drive synchronous reluctance motor drives. By exploring innovative diagnostic techniques, the effectiveness of a signal-based approach for accurately identifying faults is emphasized. A new algorithm is proposed to detect open-switch faults using a power-based diagnostic approach. The method achieves both accurate fast diagnosis and fault localization. The performance of the technique is evaluated through simulations and experimental data, demonstrating its effectiveness in enhancing the reliability of motor drive systems.

This paper deals with the freeRTOS-based permanent magnets synchronous motor (PMSM) control algorithm developed by ST microelectronics and the optimal diagnostic tasks implementation in a freeRTOS-based e-bike control firmware. The purpose of this work is to investigate the STM32 freeRTOS-based firmware, to develop and implement optimally the diagnostic tasks of an e-bike PMSM. The implementation of the diagnostic tasks is achieved after a detailed and insightful analysis of the STM32 PMSM control firmware and realized through the utilization of a feature provided via freeRTOS which is: task notification and deferred interrupt handling.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. However, such systems, are suffering from several type of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnosis multiple open-circuit fault in three phase two level voltage source inverter for induction motor drive applications. The proposed approach uses fault diagnosis variables obtained from sigmoid transformation of the motor stator currents. The open-circuit fault diagnosis variables are then introduced to a Bidirectional Long Short-Term Memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault diagnosis algorithm effectiveness and robustness.