Gerald Burt Kliman

Bio: Gerald Burt Kliman is an academic researcher from General Electric. The author has contributed to research in topics: Stator & Rotor (electric). The author has an hindex of 36, co-authored 133 publications receiving 5154 citations. Previous affiliations of Gerald Burt Kliman include Lyons.

Interior Permanent-Magnet Synchronous Motors for Adjustable-Speed Drives

Abstract: Interior permanent-magnet (IPM) synchronous motors possess special features for adjustable-speed operation which distinguish them from other classes of ac machines. They are robust high powerdensity machines capable of operating at high motor and inverter efficiencies over wide speed ranges, including considerable ranges of constant-power operation. The magnet cost is minimized by the low magnet weight requirements of the IPM design. The impact of the buried-magnet configuration on the motor's electromagnetic characteristics is discussed. The rotor magnetic circuit saliency preferentially increases the quadrature-axis inductance and introduces a reluctance torque term into the IPM motor's torque equation. The electrical excitation requirements for the IPM synchronous motor are also discussed. The control of the sinusoidal phase currents in magnitude and phase angle with respect to the rotor orientation provides a means for achieving smooth responsive torque control. A basic feedforward algorithm for executing this type of current vector torque control is discussed, including the implications of current regulator saturation at high speeds. The key results are illustrated using a combination of simulation and prototype IPM drive measurements.

Methods of Motor Current Signature Analysis

Abstract: Recently a technique for monitoring and diagnosing mechanical problems, associated with rotating machines driven by electric motors, has been proposed and is now being offered by several commercial suppliers. This technique, known as “Motor Current Signature Analysis” or MCSA, seeks to apply much of the long experience in vibration signature analysis to the analysis of motor current in effect using the motor as a sensor akin to an acceleromeier This paper explores some of the history of the technique, presents several examples, and demonstrates a first order approach to the theory with associated cautions.

A new approach to on-line turn fault detection in AC motors

Abstract: Turn fault detection is based on the principal that symmetrical (unfaulted) motors powered by symmetrical multiphase voltage sources will have no negative sequence currents flowing in the leads. A turn-to-turn fault will break that symmetry and give rise to a negative sequence current which may then be used as a measure of fault severity or to initiate protective action such as a circuit breaker trip. A new way of looking at the effects of turn faults has been developed that improves sensitivity and speed while reducing the probability of misdetection, taking into account voltage balance, load or voltage variation and instrument errors. The method has been implemented on a PC and tested, in real time, on a specially prepared small motor. Reliable detection of one shorted turn out of 648 turns per phase (in a Y connected motor) was demonstrated with the fault indicator becoming fully developed in two cycles of line frequency after initiation of the fault.

An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current

Abstract: It is well known that motor current is a nonstationary signal, the properties of which vary with respect to the time-varying normal operating conditions of the motor. As a result, Fourier analysis makes it difficult to recognize fault conditions from the normal operating conditions of the motor. Time-frequency analysis, on the other hand, unambiguously represents the motor current which makes signal properties related to fault detection more evident in the transform domain. In this paper, the authors present an adaptive, statistical, time-frequency method for the detection of broken bars and bearing faults. Due to the time-varying normal operating conditions of the motor and the effect of motor geometry on the current, they employ a training-based approach in which the algorithm is trained to recognize the normal operating modes of the motor before the actual testing starts. During the training stage, features which are relevant to fault detection are estimated using the torque and mechanical speed estimation. These features are then statistically analyzed and segmented into normal operating modes of the motor. For each mode, a representative and a threshold are computed and stored in a database to be used as a baseline during the testing stage. In the testing stage, the distance of the test features to the mode representatives are computed and compared with the thresholds. If it is larger than all the thresholds, the measurement is tagged as a potential fault signal. In the postprocessing stage, the testing is repeated for multiple measurements to improve the accuracy of the detection. The experimental results from their study suggest that the proposed method provides a powerful and a general approach to the motor-current-based fault detection.

Insulation failure prediction in AC machines using line-neutral voltages

Abstract: This paper presents a novel technique to detect insulation failure in polyphase AC machines. The machine must be star connected and have the neutral accessible. The mathematical theory of the technique is provided and supported by an experimental validation-turn faults in the stator of an induction machine are detected from the algebraic sum of the three instantaneous line-neutral voltages. However, this voltage sum contains undesirable frequencies that decrease the sensitivity of the scheme. Bandpass filtering the voltage sum (around the fundamental) optimizes sensitivity by removing the harmonics that arise from core saturation, slot harmonics, etc. The design of the scheme makes it practically immune to false alarms attributable to varying load conditions, operating temperatures and source voltage perturbations. It is shown that this technique is simpler in both theory and practice than other techniques based upon accurate calculations of sequence voltages and currents. The simplicity of this technique permits a low-cost implementation to flag a turn fault within a few cycles of the fundamental machine excitation.

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.

Pulsewidth modulation-a survey

Abstract: The author evaluates the state of the art in pulsewidth modulation for AC drives fed from three-phase voltage source inverters. Feedforward and feedback pulsewidth modulation schemes with relevance for industrial application are described and their respective merits and shortcomings are explained. Secondary effects such as the influence of load-current dependent switching time delay and transients in synchronized pulsewidth modulation schemes are discussed, and adequate compensation methods are presented. Recorded oscillograms illustrate the performance of the respective pulsewidth modulation principles. The author provides a guideline and quick reference for the practicing engineer to decide which methods should be considered for an application of a given power level, switching frequency, and dynamic response. >

Pulsewidth modulation for electronic power conversion

Abstract: The efficient and fast control of electric power forms part of the key technologies of modern automated production It is performed using electronic power converters The converters transfer energy from a source to a controlled process in a quantized fashion, using semiconductor switches which are turned on and off at fast repetition rates The algorithms which generate the switching functions-pulsewidth-modulation techniques-are manifold They range from simple averaging schemes to involved methods of real-time optimization This paper gives an overview >

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.