R.R. Schoen

Bio: R.R. Schoen is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Induction motor & Fault detection and isolation. The author has an hindex of 4, co-authored 4 publications receiving 1433 citations.

Motor bearing damage detection using stator current monitoring

Abstract: This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault. >

An unsupervised, on-line system for induction motor fault detection using stator current monitoring

Abstract: A new method for online induction motor fault detection is presented in this paper. This system utilizes artificial neural networks to learn the spectral characteristics of a good motor operating online. This learned spectrum may contain many harmonics due to the load which correspond to normal operating conditions. In order to reduce the number of harmonics which are continuously monitored to a manageable number, a selective frequency filter is employed. This frequency filter only passes those harmonics which are known to be of importance in fault detection, or which are continuously above a set level, to a neural net clustering algorithm. After a sufficient training period, the neural network signals a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, online failure prediction is possible with this system without requiring information on the motor or load characteristics. The detection algorithm was implemented and its performance verified on various fault types.

Effects of time-varying loads on rotor fault detection in induction machines

Abstract: The authors address the problem of motor current spectral analysis for the detection of nonidealities in the airgap flux density in the presence of an oscillation or position-varying load torque. An analysis of the effects of position-varying loads on the current harmonic spectrum is presented. The load torque-induced harmonics are shown to be coincidental with rotor fault-induced harmonics when the load varies synchronously with the rotor position. Since the effect of the load and fault on a single stator current harmonic component is spatially dependent, the fault-induced portion cannot be separated from the load portion. Therefore, an online detection scheme which measures the spectrum of a single-phase current must rely on monitoring those spectral components which are not affected by the load torque oscillations. With this in mind, the detection of broken bars is still possible, since the current typically contains higher order harmonics than those induced by the load. >

Evaluation and implementation of a system to eliminate arbitrary load effects in current-based monitoring of induction machines

Abstract: This paper presents a method for removing the load effects from the monitored quantity of the machine. Fault conditions in induction machines cause the magnetic field in the air gap of the machine to be nonuniform. This results in harmonics in the stator current of the motor which can be measured in order to determine the health of the motor. However, variations in the load torque which are not related to the health of the machine typically have exactly the same effect on the load current. Previously presented schemes for current-based condition monitoring ignore the load effect or assume it is known. Therefore, a scheme for determining machine health in the presence of a varying load torque requires some method for separating the two effects. This is accomplished by comparing the actual stator current to a model reference value which includes the load effects. The difference between these two signals provides a filtered quantity, independent of variations of the load, that allows continuous on-line condition monitoring to be conducted without concern for the load condition. Simulation and test results illustrate the effects on the spectrum of the monitored quantity for both constant and eccentric air gaps when in the presence of an oscillating load.

Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review

Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.

A review of induction motors signature analysis as a medium for faults detection

Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.

Advances in Diagnostic Techniques for Induction Machines

Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.