Showing papers on "Condition monitoring published in 2003"

Review of modern diagnostic techniques for assessing insulation condition in aged transformers

Abstract: Cellulosic paper and oil insulation in a transformer degrade at higher operating temperatures. Degradation is accelerated in the presence of oxygen and moisture. Power transformers being expensive items need to be carefully monitored throughout their operation. Well established time-based maintenance and conservative replacement planning is not feasible in a current market driven electricity industry. Condition based maintenance and online monitoring are now gaining importance. Currently there are varieties of chemical and electrical diagnostic techniques available for insulation condition monitoring of power transformers. This paper presents a description of commonly used chemical diagnostics techniques along with their interpretation schemes. A number of new chemical techniques are also described in this paper. A number of electrical diagnostic techniques have gained exceptional importance to the utility professionals. Among these techniques polarisation/depolarisation current measurement, return voltage measurement and frequency domain dielectric spectroscopy at low frequencies are the most widely used. This paper describes analyses and interpretation of these techniques for transformer insulation condition assessment.

Gear noise and vibration

Abstract: Causes of noise Harris mapping for spur gears theoretical helical effects prediction of static transmission error prediction of dynamic effects measurements transmission error measurement recording and storage analysis techniques improvements lightly loaded gears planetary and split drives high contact ratio gears low contact ratio gears condition monitoring vibration testing couplings failures strength versus noise.

Basic vibration signal processing for bearing fault detection

Abstract: Faculty in the College of Engineering at the University of Alabama developed a multidisciplinary course in applied spectral analysis that was first offered in 1996. The course is aimed at juniors majoring in electrical, mechanical, industrial, or aerospace engineering. No background in signal processing or Fourier analysis is assumed; the requisite fundamentals are covered early in the course and followed by a series of laboratories in which the fundamental concepts are applied. In this paper, a laboratory module on fault detection in rolling element bearings is presented. This module is one of two laboratory modules focusing on machine condition monitoring applications that were developed for this course. Background on the basic operational characteristics of rolling element bearings is presented, and formulas given for the calculation of the characteristic fault frequencies. The shortcomings of conventional vibration spectral analysis for the detection of bearing faults is examined in the context of a synthetic vibration signal that students generate in MATLAB. This signal shares several key features of vibration signatures measured on bearing housings. Envelope analysis and the connection between bearing fault signatures and amplitude modulation/demodulation is explained. Finally, a graphically driven software utility (a set of MATLAB m-files) is introduced. This software allows students to explore envelope analysis using measured data or the synthetic signal that they generated. The software utility and the material presented in this paper constitute an instructional module on bearing fault detection that can be used as a stand-alone tutorial or incorporated into a course.

Condition monitoring of wind generators

Abstract: This paper focuses on the experimental investigation for incipient fault detection and fault detection methods existing in the literature suitably adapted for use in wind generator systems using doubly fed induction generators (DFIGs) Three main experiments (one for stator phase unbalance, one for rotor phase unbalance and one for turn-to-turn faults) have been performed to study the electrical behaviour of the DFIG The article aims to provide wind generators with further documentation for an advanced condition monitoring system, in order to avoid undesirable operating conditions and to detect and diagnose incipient electrical faults

Bearing defect diagnosis and acoustic emission

Abstract: Acoustic emission (AE) was originally developed for non-destructive testing of static structures, but over the years its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison with vibration analysis. However, limitations in the successful application of the AE technique for monitoring bearings have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The investigation reported in this paper was centred on the application of standard AE characteristic parameters on a radially loaded bearing. An experimental test rig was modified such that defects could be seeded onto the inner and outer races of a test bearing. As the test rig was adapted for this purpose, it offered high background acoustic emission noise providing a realistic test for fault diagnosis. In addition to a review of current diagnostic methods for applying AE to bearing diagnosis, the results of ...

Bearing fault detection via autoregressive stator current modeling

Abstract: This research proposes a method for detecting developing bearing faults via stator current Current-based condition monitoring offers significant economic savings and implementation advantages over vibration-based techniques This method begins by filtering the stator current to remove most of the significant frequency content unrelated to bearing faults Afterwards, the filtered stator current is used to train an autoregressive signal model This model is first trained while the bearings are healthy, and a baseline spectrum is computed As bearing health degrades, the modeled spectrum deviates from its baseline value; the mean spectral deviation is then used as the fault index This fault index is able to track changes in machine vibration due to developing bearing faults Due to the initial filtering process, this method is robust to many influences including variations in supply voltage, cyclical load torque variations, and other (nonbearing) fault sources Experimental results from 10 different bearings are used to verify the proficiency of this method

Wind Turbine Operation & Maintenance based on Condition Monitoring WT-Ω

Abstract: Acknowledgement This report is part of the project entitled WT_Ω (WT_OMEGA = Wind Turbine Operation and Maintenance based on Condition Monitoring) which has been carried out in cooperation with Lagerwey the WindMaster, Siemens Nederland, and SKF.

Optimal replacement under partial observations

Abstract: In this paper, we present a framework for the condition-based maintenance optimization. A technical system which can be in one of N operational states or in a failure state is considered. The system state is not observable, except the failure state. The information that is stochastically related to the system state is obtained through condition monitoring at equidistant inspection times. The system can be replaced at any time; a preventive replacement is less costly than failure replacement. The objective is to find a replacement policy minimizing the long run expected average cost per unit time. The replacement problem is formulated as an optimal stopping problem with partial information and transformed to a problem with complete information by applying the projection theorem to a smooth semimartingale process in the objective function. The dynamic equation is derived and analyzed in the piecewise deterministic Markov process stopping framework. The contraction property is shown and an algorithm for the calculation of the value function is presented, illustrated by an example.

Analysis of power transformer dissolved gas data using the self-organizing map

Abstract: Incipient faults in power transformers can degrade the oil and cellulose insulation, leading to the formation of dissolved gases. Even though established approaches that relate these dissolved gas information to the condition of power transformers are already developed, it is discussed in this paper that they still contain some limitations. In view of that, this paper introduces an alternative approach for the analysis of dissolved gas data, which can produce more convincing interpretation and fault diagnosis. The proposed approach, which is based on the data mining methodology and the self-organizing map, has been compared and validated using conventional interpretation schemes and real fault-cases, thereby proven to be capable of enhancing the condition monitoring of power transformers.

Induction machine on-line condition monitoring and fault diagnosis - a survey

Abstract: Induction machines play a pivotal role in industry and there is a strong demand for their reliable and safe operation. They are generally reliable but eventually do wear out. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues, and this motivates the examination of on-line condition monitoring. On-line condition monitoring involves taking measurements on a machine while it is operating in order to detect faults with the aim of reducing both unexpected failures and maintenance costs. This paper surveys the current trends in on-line fault detection and diagnosis of induction machines and identifies future research areas.

Pipe inspection using a laser-based transducer and automated analysis techniques

Abstract: This paper presents a new sensing methodology for the automated inspection of pipes. Standard inspection systems, as they are for example used in waste pipes and drains, are based on closed-circuit television cameras which are mounted on remotely controlled platforms and connected to remote video recording facilities. Two of the main disadvantages of such camera-based inspection systems are: 1) the poor quality of the acquired images due to difficult lighting conditions and 2) the susceptibility to error during the offline video assessment conducted by human operators. The objective of this research is to overcome these disadvantages and to create an intelligent sensing approach for improved and automated pipe-condition assessment. This approach makes use of a low-cost lighting profiler and a camera which acquires images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the acquired images is introduced. The image data analysis is based on differential processing leading to highly-noise tolerant algorithms, particularly well suited for the detection of small faults in harsh environments. With the subsequent application of artificial neural networks, the system is capable of recognizing defective areas with a high success rate. Experiments in a range of waste pipes with different diameters and material properties have been conducted and test results are presented.

Vehicle condition monitoring system

Abstract: The present invention is a system and method for monitoring conditions of a vehicle and generating a maintenance plan according to the monitored conditions. The system includes one or more sensors and a data acquisition unit located within the vehicle. The one or more sensors generate signals of a condition from various locations within the vehicle. The data acquisition unit stores the generated sensor signals at a first sampling rate. The system also includes a structural condition management system that receives the stored sensor signals from the data acquisition unit. The structural condition management system is external to the vehicle. The structural condition management system processes the transmitted sensor signals based on one or more associated predefined condition assessment algorithms and generates a maintenance plan based on the processed sensor signals.

Integrated direct/indirect adaptive robust control of SISO nonlinear systems in semi-strict feedback form

Abstract: The paper focuses on the synthesis of adaptive robust controllers that achieve not only excellent output tracking performance but also accurate parameter estimations for secondary purposes such as machine health monitoring and prognostics. Such an objective is accomplished through an intelligent integration of the output tracking performance oriented direct adaptive robust control (DARC) design with the recently proposed accurate parameter estimation based indirect adaptive robust control (IARC) design. SISO nonlinear systems transformable to semi-strict feedback forms are considered. Theoretically, regardless of the specific estimation algorithm to be used, certain guaranteed transient performance and final tracking accuracy are achieved even in the presence of uncertain nonlinearities-a desirable feature in applications. In addition, the theoretical performance of adaptive designs - asymptotic output tracking in the presence of parametric uncertainties only - is preserved. The construction of physical parameter estimation law is based on the actual system dynamics and totally independent from the design of underline robust control law, which allows various estimation algorithms having better parameter convergence properties and practical modifications such as the on-line explicit monitoring of signal excitation levels to be used to significantly improve the accuracy of the resulting parameter estimates in implementation.

NetLogger: a toolkit for distributed system performance tuning and debugging

Abstract: Developers and users of high-performance distributed systems often observe performance problems such as unexpectedly low throughput or high latency. Determining the source of the performance problems requires detailed end-to-end instrumentation of all components, including the applications, operating systems, hosts, and networks. In this paper we describe a methodology that enables the real-time diagnosis of performance problems in complex high-performance distributed systems. The methodology includes tools for generating timestamped event logs that can be used to provide detailed end-to-end application and system level monitoring; and tools for visualizing the log data and real-time state of the distributed system. This methodology, called NetLogger, has proven invaluable for diagnosing problems in networks and in distributed systems code. This approach is novel in that it combines network, host, and application-level monitoring, providing a complete view of the entire system. NetLogger is designed to be extremely lightweight, and includes a mechanism for reliably collecting monitoring events from multiple distributed locations.

The magnetostrictive sensor technology for long range guided wave testing and monitoring of structures

Abstract: A technical background on and applications of a guided wave technology called the magnetostrictive sensor are described. The magnetostrictive sensor technology is for long range global testing and condition monitoring of structures such as piping, plates and steel cables. For long range testing of piping in processing plants, such as refineries and chemical plants, the magnetostrictive sensor system, including the probe, instrument and data analysis software, is matured for use in commercial testing services. Capabilities of the present magnetostrictive sensor system for pipe testing are presented together with an example of testing data and their analysis.

Applicability of Power-Line Communications to Data Transfer of On-line Condition Monitoring of Electrical Drives

Abstract: Lappeenranta University of Technology Acta Universitatis Lappeenrantaensis 157

On-Line Vibration Analysis with Fast Continuous Wavelet Algorithm for Condition Monitoring of Bearing

Abstract: The newly developed technique of wavelet transform enables us to observe the evolution in time of the frequency content of a signal. This property makes it very suitable for the detection of vibrat...

Feature extraction and classification of gear faults using principal component analysis

Abstract: Feature extraction is a key issue to machine condition monitoring and fault diagnosis. The features must contain the necessary discriminative information for the fault classifier to have any chance of accurate classification. This paper presents a study that uses principal component analysis to reduce dimensionality of the feature space and to get an optimal subspace for machine fault classification. Industrial gearbox vibration signals measured from different operating conditions are analyzed using the above method. The experimental results indicate that the method extracts diagnostic information effectively for gear fault classification and has a good potential for application in practice.

Recursive filters for a partially observable system subject to random failure

Abstract: We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.