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Motor current signature analysis (MCSA) is a well-proven technique for electrical and mechanical faults detection in induction motors. The appearance of stator current components or increase of magnitude of some components at characteristic frequencies indicates a motor fault condition. In this paper, we propose a new MCSA fault detector based on a matched subspace technique. The proposed detector consists of three steps. First, the influence of the fundamental supply frequency is removed from the current signal using an interference cancellation technique based on oblique projection. Then, the fault-related frequency is estimated from the interference-free signal using the maximum likelihood principle. Finally, the fault detection is performed using a generalized likelihood ratio test. Simulation and experimental results illustrate the effectiveness of the proposed approach for eccentricity fault, bearing faults, and broken rotor bars detection. [ABSTRACT FROM AUTHOR]

This paper describes a sensor fault-tolerant control (FTC) for electric-vehicle (EV) powertrains. The proposed strategy deals with speed sensor failure detection and isolation within a reconfigurable induction-motor direct torque control (DTC) scheme. To increase the vehicle powertrain reliability regarding speed sensor failures, a maximum-likelihood voting (MLV) algorithm is adopted. It uses two virtual sensors [extended Kalman filter (EKF) and a Luenberger observer (LO)] and a speed sensor. Experiments on an induction-motor drive and simulations on an EV are carried out using a European urban and extraurban driving cycle to show that the proposed sensor FTC approach is effective and provides a simple configuration with high performance in terms of speed and torque responses. [ABSTRACT FROM PUBLISHER]

This paper deals with experimental validation of a reconfiguration strategy for sensor fault-tolerant control (FTC) in induction-motor-based electric vehicles (EVs). The proposed active FTC system is illustrated using two control techniques: indirect field-oriented control (IFOC) in the case of healthy sensors and speed control with slip regulation (SCSR) in the case of failed current sensors. The main objective behind the reconfiguration strategy is to achieve a short and smooth transition when switching from a controller using a healthy sensor to another sensorless controller in the case of a sensor failure. The proposed FTC approach performances are experimentally evaluated on a 7.5-kW induction motor drive. [ABSTRACT FROM PUBLISHER]

Earlier studies have pointed out the limitations of conventional inverters, especially in high-voltage and high-power applications. In recent years, multilevel inverters are becoming increasingly popular for high-power applications due to their improved harmonic profile and increased power ratings. Several studies have been reported in the literature on multilevel inverters topologies, control techniques, and applications. However, there are few studies that actually discuss or evaluate the performance of induction motor drives associated with three-phase multilevel inverter. This paper presents then a comparison study for a cascaded H-bridge multilevel direct torque control (DTC) induction motor drive. In this case, symmetrical and asymmetrical arrangements of five- and seven-level H-bridge inverters are compared in order to find an optimum arrangement with lower switching losses and optimized output voltage quality. The carried out experiments show that an asymmetrical configuration provides nearly sinusoidal voltages with very low distortion, using less switching devices. Moreover, torque ripples are greatly reduced. [ABSTRACT FROM PUBLISHER]

This paper presents a hybrid cascaded H-bridge multilevel motor drive direct torque control (DTC) scheme for electric vehicles (EVs) or hybrid EVs. The control method is based on DTC operating principles. The stator voltage vector reference is computed from the stator flux and torque errors imposed by the flux and torque controllers. This voltage reference is then generated using a hybrid cascaded H-bridge multilevel inverter, where each phase of the inverter can be implemented using a dc source, which would be available from fuel cells, batteries, or ultracapacitors. This inverter provides nearly sinusoidal voltages with very low distortion, even without filtering, using fewer switching devices. In addition, the multilevel inverter can generate a high and fixed switching frequency output voltage with fewer switching losses, since only the small power cells of the inverter operate at a high switching rate. Therefore, a high performance and also efficient torque and flux controllers are obtained, enabling a DTC solution for multilevel-inverter-powered motor drives. [ABSTRACT FROM AUTHOR]

This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results On a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a sensor-based technique using the mains current measurement. A localization domain made with seven patterns is built with the stator Concordia mean current vector. One is dedicated to the healthy domain and the last six are to each inverter switch. A probabilistic approach for the definition of the boundaries increases the robustness of the method against the uncertainties due to measurements and to the PWM. In high-power equipment where it is crucial to detect and diagnose the inverter faulty switch, a simple algorithm compares the patterns and generates a Boolean indicating the faulty device. In low-power applications (less than 1 kW) where only fault detection is required, a radial basis function (RBF) evolving architecture neural network is used to build the healthy operation area. Simulated experimental results on 0.3- and 1.5-kW induction motor drives show the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns. [ABSTRACT FROM AUTHOR]