Shahin Hedayati Kia

A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation

The aim of this paper is to present a wavelet-based method for broken bar fault detection in induction machines. The frequency-domain methods which are commonly used need speed information or accurate slip estimation for frequency components localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of this known bandwidth with time-domain analysis using the discrete wavelet transform (DWT). Then, it has been applied to the stator current space vector magnitude and the instantaneous magnitude of the stator current signal for different broken bar fault severities and load levels.

Torsional Vibration Effects on Induction Machine Current and Torque Signatures in Gearbox-Based Electromechanical System

The monitoring of heavy-duty electromechanical systems is crucial for their preventive maintenance planning. In these systems, the mechanical anomalies such as load troubles, great torque dynamic variations, and torsional oscillations lead to shaft fatigue and aging of other mechanical parts such as bearings and gearboxes. In this paper, a gearbox-based electromechanical system is investigated. Initially, a simple gearbox dynamic model is used to show the effects of rotating input, output, and mesh frequency components on the electromagnetic torque and consequently on the stator current signature. By this model, the influence of transmission error, eccentricities of pinion/wheel, and teeth contact stiffness variation is demonstrated for a healthy gearbox. Then, it is shown that the electrical machine can be considered as a torque sensor through electromagnetic torque estimation for torsional vibration monitoring without any extra mechanical sensor. A test-rig based on a 5.5-kW three-phase squirrel-cage induction motor connected to a wound-rotor 4-kW induction generator via a one-stage gearbox has been used to validate the proposed method.

Torsional-Vibration Assessment and Gear-Fault Diagnosis in Railway Traction System

The diagnosis of mechanical faults in railway traction systems (RTSs) has a significant importance on both safety and reliability, which can avoid train crashes. This paper deals with torsional-vibration assessment and gear-fault diagnosis in the mechanical transmission of a high-speed RTS by a fully noninvasive technique. Previous studies on a simple gearbox-based electromechanical system have shown that the influence of gearbox torsional vibrations on the torque and on the stator-current signatures are obvious. The aim of this paper is to demonstrate that the traction motor can be considered as a torque sensor through its electromagnetic-torque estimation for torsional-vibration monitoring without any extra mechanical sensor. The effects of both tooth-damage and surface-wear faults at the output wheel on the stator current and on the estimated electromagnetic torque have been investigated. The results of the estimation are compared with the measured mechanical torque and validated through a reduced-scale RTS in both stationary and nonstationary conditions.

Torsional Vibration Assessment Using Induction Machine Electromagnetic Torque Estimation

Mechanical anomalies such as load troubles, great torque dynamic variations, and torsional oscillations result in the shaft fatigue of electrical machine and other mechanical parts such as bearings and gearboxes. Particularly, the torsional vibration may attain a significant level at resonant frequencies which damage or cause additional lifetime consumption of mechanical parts. In this way, this paper proposes a noninvasive technique through the electromagnetic torque estimation of driving induction machine as a mean of mechanical torsional stresses monitoring. The lubrication loss is considered as a gear failure to demonstrate its influence on the vibration and on the electromagnetic estimated torque signatures. Then, it is shown that the information in the electromagnetic torque can be decomposed into high- and low-frequency bandwidths which are associated to induction machine and gearbox mechanical-related frequencies, respectively. A setup based on a 5.5-kW three-phase squirrel-cage induction motor connected to a 4-kW wound-rotor induction generator via a one-stage gearbox has been used to validate the proposed method in both stationary and nonstationary conditions.

Analytical and Experimental Study of Gearbox Mechanical Effect on the Induction Machine Stator Current Signature

The aim of this paper is the analytical and experimental study of a gearbox by using the stator current signature analysis in the driving induction machines. The mechanical study using the vibration signal analysis has been well studied before. Recently, some works have been performed to observe the vibration components of the mechanical part in the stator current spectrum for an induction machine but without any theoretical development. This paper proposes a theoretical framework based on torque oscillations due to the characteristic torsional vibrations in a gearbox, and the source of mesh and rotating frequency components in the stator current is presented. To verify the theoretical development, a test bed based on a 5.5-kW three-phase squirrel-cage machine connected to a gearbox has been used.

Gearbox Monitoring Using Induction Machine Stator Current Analysis

The aim of this paper is the monitoring of a gearbox by using the stator current signature analysis in the driving induction machines. The detection of mechanical faults using the vibration signal analysis has been well studied before. Recently, some works have been performed in order to observe the vibration components of the mechanical part in the stator current spectrum for an induction machine but without any theoretical detail. This paper proposes a theoretical framework based on torque oscillations due to the characteristic torsional vibrations in a gearbox and the source of mesh and rotating frequency components in the stator current is presented. In order to verify the theoretical development, a test-bed based on a 5.5 kW three-phase squirrel-cage machine connected to a gearbox has been used.

Gear Tooth Surface Damage Fault Detection Using Induction Machine Stator Current Space Vector Analysis

A noninvasive technique for the diagnosis of gear tooth surface damage faults based upon the stator current space vector analysis is presented. The torque oscillation profile produced by the gear tooth surface damage fault in the mechanical torque experimented by the driven electrical machine is primarily investigated. This profile consists of a mechanical impact generated by the fault followed by a damped oscillation that can be identified through the mechanical system torsional natural frequency and damping factor. Through theoretical developments, it is shown that the periodic behavior of this particular shape produces fault-related frequencies in the stator current and harmonics integer multiple of the rotation frequency in the stator current space vector instantaneous frequency. The fault signature related to the gear tooth surface damage fault is predicted through the numerical simulation. The simulation results are validated through experimental tests, illustrating a possible noninvasive gear tooth surface damage fault detection with a fault sensitivity comparable to invasive methods. A dedicated experimental setup, which is based on a 250-W squirrel-cage three-phase induction machine that is shaft connected to a single-stage gear, has been used for this purpose.

Efficient digital signal processing techniques for induction machines fault diagnosis

This paper investigates recent advances on modern digital signal processing techniques for induction machines fault diagnosis. An intensive research has been performed in order to improve performances of fault diagnosis techniques by applying enhanced signal processing methods during past few years. Since non-invasive sensors offer relatively simple and cost effective fault diagnosis capabilities, more emphasis is given to stator current analysis rather than vibration or acoustic analysis for electrical machines. Here, further interests have been paid on modern signal processing techniques with a special attention to their performances in time domain, frequency domain and time-frequency domain. A comprehensive review is done on recently developed methods which are applied to the stator current collected from induction machine based test-rigs with electrical and/or mechanical faults. It will be demonstrated that numerous techniques have been adapted to induction machines diagnosis. They have been developed primarily based upon basic digital signal processing techniques in order to achieve a more reliable identification and quantification of fault indexes.

Some digital signal processing techniques for induction machines diagnosis

This paper investigates the recent advances on digital signal processing techniques for induction machines diagnosis. Since non-invasive sensors offer a relatively simple and cost effective fault diagnosis, more emphasis is given to stator current analysis rather than vibration or acoustic analysis in induction machines. Here, further interest has been paid on modern signal processing techniques with a special attention to their performances in time domain, frequency domain and time-frequency domain. Among these methods, some of them have been applied to the stator current collected from induction machine based test-rigs with electrical or mechanical faults. It will be demonstrated that only recently numerous techniques have been adapted to induction machines diagnosis. They have been developed primarily based upon basic digital signal processing techniques, in order to achieve a more reliable identification and quantification of fault indexes.