Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

An Approach to Insulation Condition Monitoring and Life Assessment in Emerging Electrical Environments

Abstract: Distributed generation, dc transmission and distribution, and power electronics for ac/dc/ac conversion bring the advantages of increased control and flexibility in electric power management. These advantages also introduce challenges that must be managed. An electrical environment where voltage is not anymore strictly sinusoidal implies that a new approach for the design and maintenance of electrical insulation systems has to be devised. Non-sinusoidal voltage supply often causes increased failure probability and, thus, reduced reliability and life of an electrical asset. This research addresses the implications of the transient and steady waveform distortion introduced by power electronic systems and dc supply, for which the electrical insulation was designed and tested through consolidated criteria based on sinusoidal voltage supply. Based on condition monitoring, a health index definition is proposed, which allows the condition of the insulation of an electrical apparatus to be assessed as a function of operation time, and, based on the aging and life models, the evaluation of maintenance actions and of the feasibility and extent of life extension plans to be carried out.

Air-gap force distribution and vibration pattern of Induction motors under dynamic eccentricity

Abstract: A method for determining the signatures of dynamic eccentricity in the airgap force distribution and vibration pattern of induction machine is presented. The radial electromagnetic force distribution along the airgap, which is the main source of vibration, is calculated and developed into a double Fourier series in space and time. Finite element simulations of faulty and healthy machines are performed. They show that the electromagnetic force distribution is a sensible parameter to the changes in the machine condition. The computations show the existence of low frequency and low order force distributions, which can be used as identifiable signatures of the motor condition by measuring the corresponding low order vibration components. These findings are supported by vibration measurements and modal testing. The low frequency components offer an alternative way to the monitoring of slot passing frequencies, bringing new components that allow to discriminate between dynamic eccentricity and rotor mechanical unbalance. The method also revealed a non linear relationship between loading, stress waves and vibration during dynamic eccentricity.

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

Abstract: This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple $x{-\rm{ }}y$ graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.

On monitoring transparent optical networks

Abstract: Fault identification and localization problems in optical networks have become crucial. Due to network transparency and high data rates, optical networks are vulnerable to sophisticated attacks on the physical infrastructure, and hence require adequate fault monitoring in order to accurately identify and locate network failures. In transparent optical networks, faults may propagate to various parts of the network from the origin, and multiple alarms can be generated for a single failure. In order to reduce the number of redundant alarms, simplify fault localization, as well as lower financial investment in network monitoring equipment, fault monitor placement should be optimized for a given network. In this paper, we formulate a problem on the optimal placement of network monitoring devices and propose a solution approach. We provide a brief summary of available physical-layer monitoring devices, and present a scheme for optimal monitor placement.

The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics

Abstract: Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools.Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations.This paper presents recent developments in technology and strategies in engine condition monitoring including:1) application of statistical analysis and artificial neural network filters to improve data quality;2) neural networks for trend change detection, and classification to diagnose performance change; and3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.Copyright © 1998 by ASME