Evaluation of saliency tracking as an alternative for health monitoring in PMSM-drives under nonstationary conditions

This paper evaluates the capability of saliency tracking to assess the health condition of permanent magnet synchronous motor (PMSM) drives operating under nonstationary conditions. The evaluated scheme is based on saliency tracking methods, which are associated to the accurate sensorless control of AC drives without zero speed limitations. In this work two representative saliency tracking architectures are evaluated: High Frequency (HF) injection, and PWM transient excitation. Although a monitoring approach based on HF injection was previously reported, a comparative study to evaluate the most representative saliency tracking schemes to assess health condition in drives was still missing. The aim of this work is to fill out this gap by evaluating and comparing two saliency-based monitoring schemes (one based on HF-injection and the other based on PWM transient excitation) to evaluate their performance in the presence of inter-turn winding faults. Simulation and experimental results are presented which confirm that both schemes offer excellent detection capabilities and that are suitable for drives operating under nonstationary conditions including standstill operation. Significant differences are also found for instance, PWM transient excitation offers improved accuracy since the approach is not affected by the inverter nonlinearities and is suitable for full-speed range applications. The main drawback here is complexity and the hardware requirements. Schemes based on HF-injection proved to be very simple and provide comparable results; however a good performance is only guaranteed for the zero-to-medium speed range applications which limit their applicability

Stator Interturn Fault Detection in Permanent-Magnet Machines Using PWM Ripple Current Measurement

This paper proposes a novel method of interturn fault detection based on measurement of pulsewidth modulation (PWM) ripple current. The method uses the ripple current generated by the switching inverter as a means to detect interturn fault. High-frequency (HF) impedance behavior of healthy and faulted windings is analyzed and modeled, and ripple current signature due to interturn faults is quantified. A simple analog circuit is designed to extract the PWM ripple current via a bandpass (BP) filter and a root-mean-square (RMS) detector for fault detection. In addition, this method can also identify the faulted phase, which can be used for fault mitigation strategies. The method is tested experimentally on a five-phase permanent-magnet (PM) machine drive

Development of an induction motor condition monitoring test rig And fault detection strategies

Includes bibliographical references.This thesis sets out to develop an induction motor condition monitoring test rig to experimentally simulate the common faults associated with induction motors and to develop strategies for detecting these faults that employ signal processing techniques. Literature on basic concepts of induction motors and inverter drives, the phenomena of common faults associated with induction motors, the condition monitoring systems were intensively reviewed

Detection of inter-turns short circuits in permanent magnet synchronous motors operating under transient conditions by means of the zero sequence voltage

This work proposes the zero sequence voltage component (ZSVC) of the stator three-phase voltages as a method for detecting winding inter-turns short circuits in permanent magnet synchronous motors PMSM operating under transient conditions. Additionally it proves the linear relationship between the ZSVC and speed, which is effectively used as a fault severity index. The acquired ZSVC temporal signal is processed by means of the Hilbert-Huang transform (HHT). Experimental results presented in this work show the advantages of the method to provide helpful data for online diagnosis of stator winding inter-turn faults.Peer ReviewedPostprint (author’s final draft

Signal Injection as a Fault Detection Technique

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies

PWM Ripple Currents Based Turn Fault Detection for Multiphase Permanent Magnet Machines

Most permanent magnet machines are driven by inverters with pulse width modulation (PWM) voltages. The currents contain high frequency (HF) components which are inversely proportional to machine inductance. The HF PWM ripple currents can be used to detect a turn fault that gives rise to changes in inductance. The features of these HF components in turn fault conditions are analyzed. A bandpass (BP) filter is designed to extract the selected sideband components, and their root-mean-square (RMS) values are measured. The RMS values in all phases are compared. It is shown that the RMS ripple current ratios between two adjacent phases provide a very good means of detecting turn fault with high signal-to-noise ratio. The detection method can identify the faulted phase, tolerate inherent imbalance of the machine, and is hardly affected by transient states. The method is assessed by simulations and experiments on a five-phase permanent magnet machine

Motor Fault Diagnosis Using Higher Order Statistical Analysis of Motor Power Supply Parameters

Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis