Showing papers on "Condition monitoring published in 2008"

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

Abstract: Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.

Review of condition monitoring of rotating electrical machines

Abstract: Condition monitoring of rotating electrical machinery has received intense research interest for more than 30 years. However, electrical machinery has been considered reliable and the application of fast-acting digital electrical protection has rather reduced the attention operators pay to the equipment. The area based upon current literature and the author's experience is reviewed. There are three restrictions: only on-line techniques for rotating machines are dealt with; specific problems of variable speed drives are not dealt with, except in passing; conventional rather than emerging brushless, reluctance and permanent magnet machines of unusual topology are concentrated upon. The art of condition monitoring is minimalist, to take minimum measurements from a machine necessary to extract a diagnosis, so that a condition can be rapidly inferred, giving a clear indication of incipient failure modes. The current state of the art is reviewed in the following ways: survey developments in condition monitoring of machines, mechanically and electrically, over the last 30 years; put that work in context alongside the known failure mechanisms; review those developments which have proved successful and identify areas of research which require attention in the future to advance the subject.

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Abstract: This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

Abstract: A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of testing and monitoring methods, categorizing them into online and offline methods, each of which is further grouped into specific areas according to their physical nature. The main focus of this paper is on testing and monitoring techniques that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. In order to give a compact overview, the results are summarized in two tables. In addition to monitoring methods on turn-to-turn insulation, some of the most common methods to assess the stator's phase-to-ground and phase-to-phase insulation conditions are included in the tables as well.

Condition Monitoring of Rotating Electrical Machines

Abstract: Condition monitoring of engineering plant has increased in importance as engineering processes are automated and manpower is reduced. However, electrical machinery receives attention only at infrequent intervals when plant is shut down and the application of protective relays to machines has also reduced operator surveillance. A first edition of Condition Monitoring of Electrical Machines, written by Tavner and Penman, was published in 1987. The economics of industry have now changed, as a result of the privatisation and deregulation of the energy industry, placing emphasis on the importance of reliable operation of plant, throughout the whole life cycle, regardless of first cost. The availability of advanced electronics and software in powerful instrumentation, computers, and digital signal processors (DSP) has simplified our ability to instrument and analyse machinery. As a result condition monitoring is now being applied to a wider range of systems, from fault-tolerant drives of a few hundred watts in the aerospace industry, to machinery of a few hundred megawatts in major capital plant. In this new book the original authors have been joined by Ran, an expert in power electronics and control, and Sedding, an expert in the monitoring of electrical insulation systems. Together the authors have revised and expanded the earlier book, merging their own experience with that of machine analysts to bring it up to date. The book is aimed at professional engineers in the energy, process engineering and manufacturing industries, plus research workers and students.

Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation

Abstract: Overall system performance on a production line is one of the major concerns in modern industry where induction motors are present and their condition monitoring is mandatory. Periodic offline monitoring of the motor condition is usually performed in the industry, consuming production time and increasing cost. Broken rotor bars are among the most common failures in induction motors. Reported research projects give a broken-rotor-bar-detection methodology based on personal-computer implementation that is performed offline and requires an expert technician interpretation which is not a cost-effective solution. The novelty of this paper is the development of an automatic online diagnosis algorithm for broken-rotor-bar detection, optimized for single low-cost field-programmable gate-array (FPGA) implementation, which guarantees the development of economical self-operated equipment. The proposed algorithm requires less computation load than the previously reported algorithms, and it is mainly based on the discrete-wavelet-transform application to the start-up current transient; a further single mean-square computation determines a weighting function that, according to its value, clearly points the motor condition as either healthy or damaged. In order to validate the proposed algorithm, several tests were performed, and an FPGA implementation was developed to show the algorithm feasibility for automatic online diagnosis.

A Neural Network Degradation Model for Computing and Updating Residual Life Distributions

Abstract: The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components. Initial predicted failure times are estimated through trained neural networks using real-time sensory signals. These estimates are used to derive a prior failure time distribution for the component that is being monitored. Subsequent failure time estimates are then utilized to update the prior distributions using a Bayesian approach. The proposed methodology is tested using real world vibration-based degradation signals from rolling contact thrust bearings. The proposed methodology performed favorably when compared to other reliability-based and statistical-based benchmarks.

Asset-Management of Transformers Based on Condition Monitoring and Standard Diagnosis [Feature Article]

Abstract: In this paper, a methodology is developed to use data acquisition derived from condition monitoring and standard diagnosis for rehabilitation purposes of transformers. The interpretation and understanding of the test data are obtained from international test standards to determine the current condition of transformers. In an attempt to ascertain monitoring priorities, the effective test methods are selected for transformer diagnosis. In particular, the standardization of diagnostic and analytical techniques are being improved that will enable field personnel to more easily use the test results and will reduce the need for interpretation by experts. In addition, the advanced method has the potential to reduce the time greatly and increase the accuracy of diagnostics. The important aim of the standardization is to develop the multiple diagnostic models that combine results from the different tests and give an overall assessment of reliability and maintenance for transformers.

Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters

Abstract: The economic case for condition monitoring (CM) applied to wind turbines is currently not well quantified and the factors involved are not fully understood. In order to make more informed decisions regarding whether deployment of CM for wind turbines is economically justified, a refined set of probabilistic models capturing the processes involved are presented. Sensitivity of the model outputs with respect to variables of interest are investigated within the bounds of published data and expert opinion. The results show that the levels of benefit are dependent on a variety of factors including wind profile, typical downtime duration and wind turbine sub-component replacement cost.

System and method for advanced condition monitoring of an asset system

Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Suitability of MEMS Accelerometers for Condition Monitoring: An experimental study

Abstract: With increasing demands for wireless sensing nodes for assets control and condition monitoring; needs for alternatives to expensive conventional accelerometers in vibration measurements have been arisen. Micro-Electro Mechanical Systems (MEMS) accelerometer is one of the available options. The performances of three of the MEMS accelerometers from different manufacturers are investigated in this paper and compared to a well calibrated commercial accelerometer used as a reference for MEMS sensors performance evaluation. Tests were performed on a real CNC machine in a typical industrial environmental workshop and the achieved results are presented.

Assessment of Condition Monitoring Techniques for Offshore Wind Farms

Abstract: This paper discusses the results of an extensive investigation to assess the added value of various techniques of health monitoring to optimise the maintenance procedures of offshore wind farms. This investigation has been carried out within the framework of the EU funded CONMOW project (Condition Monitoring for Offshore Wind Farms) which was carried out from 2002 through 2007, [5]. A small wind farm of five turbines has been instrumented with several condition monitoring systems and also with the “traditional” measurement systems. Analyses of these measurements and of data collected by the turbine's SCADA systems have been performed to assess (1) if failures can be detected; (2) if so, if they can be detected at an early stage and their progress over time can be monitored; and (3) if criteria are available to assess the component's health. Several data analysis methods and measurement configurations have been developed, applied, and tested. This paper first describes the use condition monitoring to change from scheduled and corrective maintenance to condition based maintenance. Second, the paper describes the CONMOW project, and the major results. viz. the assessment of the usefulness and capabilities of condition monitoring systems and algorithms for identifying early failures. Finally, the economic consequences of applying condition monitoring systems have been quantified and assessed.

Expert system development for vibration analysis in machine condition monitoring

Abstract: Expert systems can be adapted for machine condition monitoring data interpretation due to the ability to identify systematic reasoning processes As vibration analysis in condition monitoring is still generally performed by highly trained professionals, the use of expert systems would allow a greater analysis throughput as well as enabling technicians to perform routine analysis The development of an expert system for vibration analysis of fixed plant is discussed, as well as laboratory and industry testing Unique to existing developments, the expert system incorporates triaxial and demodulated frequency and time domain vibration data analysis algorithms for high accuracy fault detection The tests confirm the potential value of the expert system for both laboratory and on-site maintenance departments of large manufacturing and mineral processing plants

An Approach to Determine the Health Index of Power Transformers

Abstract: This paper describes a realistic health index formulation method for power transformers using readily available data. The method considers practical limitations on obtaining data, and the possible constraints on the parameters. It also utilizes IEC, IEEE, and CIGRE criteria for condition parameters. This Health Index calculation considers not only typical test results such as dissolved gas analysis (DGA), oil quality, furan, and power factor, but also other parameters such as tap changer and bushing condition, physical observations, load history, maintenance work orders, and age. The calculation includes condition ratings, weighting factors, and assigned scores for specific condition parameters. By using a multi-criteria analysis approach, the method combines the various factors into a condition-based health index.

Real-time monitoring of video quality in IP networks

Abstract: This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as video codec, loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on parameters that are difficult to estimate in real-time. As a result, we introduce a ldquorelative qualityrdquo metric (rPSNR) that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments.

Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification

Abstract: This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden degradation process. The hidden degradation process is firstly identified by use of particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.

A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power Factor

Abstract: Fault detection and diagnosis of asynchronous machine has become a central problem in industry over the past decade. A solution to tackle this problem is to use stator current for a condition monitoring, referred to as motor current signature analysis. This paper argues that bearing faults would have a negligible effect on motor currents and instead argues that the more likely reason why the faults can be detected in currents is because they entail a fluctuating resistive torque which acts immediately, in contrast to the radial displacement which takes time to integrate to a perceptible displacement even in response to a step change in velocity. In this context, we propose a new method for detecting bearing defects based on the exploitation of the instantaneous power factor that varies according to torque oscillations. Experimental results show the good performances of the proposed method which will be compared with the instantaneous power method to highlight the feasibility and advantages of this method.

Health Monitoring for Damage Initiation and Progression During Mechanical Shock in Electronic Assemblies

Abstract: Electronic products may be subjected to shock and vibration during shipping, normal usage, and accidental drop High strain rate transient bending produced by such loads may result in failure of fine pitch electronic interconnects Current experimental techniques rely on electrical resistance for determination of failure Significant advantage can be gained by prior knowledge of impending failure for applications where the consequences of system failure may be catastrophic This research effort focuses on an alternate approach to damage quantification in electronic assemblies subjected to shock and vibration, without testing for electrical continuity The proposed approach can be extended to monitor product level damage In this paper, statistical pattern recognition and leading indicators of shock damage have been used to study the damage initiation and progression in shock and drop of electronic assemblies Statistical pattern recognition is currently being employed in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, artificial intelligence, computer vision, and remote sensing The application quantification of shock damage in electronic assemblies is new Previously, free vibration of rectangular plates has been studied by various researchers for development of analytical closed form models In this paper, closed form models have been developed for the eigen frequencies and mode shapes of electronic assemblies with various boundary conditions and component placement configurations Model predictions have been validated with experimental data from modal analysis Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage Sensitivity of leading indicators of shock damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response has been quantified A damage index for experimental damage monitoring has been developed using the failure indicators The above damage monitoring approach is not based on electrical continuity and hence can be applied to any electronic assembly structure irrespective of the interconnections The damage index developed provides parametric damage progression data, thus removing the limitation of current failure testing, where the damage progression cannot be monitored Hence the proposed method does not require the assumption that the failure occurs abruptly after some number of drops and can be extended to product level drops

Diagnosis of Process Faults in Chemical Systems Using a Local Partial Least Squares Approach

Abstract: This article discusses the application of partial least squares (PLS) for monitoring complex chemical systems. In relation to existing work, this article proposes the integration of the statistical local approach into the PLS framework to monitor changes in the underlying model rather than analyzing the recorded input/output data directly. As discussed in the literature, monitoring changes in model parameters addresses the problems of nonstationary behavior and presents an analogy to model-based approaches. The benefits of the proposed technique are that (i) a detailed mechanistic plant model is not required, (ii) nonstationary process behavior does not produce false alarms, (iii) parameter changes can be non-Gaussian, (iv) Gaussian monitoring statistics can be established to simplify the monitoring task, and (v) fault magnitude and signatures can be estimated. This is demonstrated by a simulation example and the analysis of recorded data from two chemical processes. © 2008 American Institute of Chemical Engineers AIChE J, 2008

The review of non-destructive testing techniques suitable for inspection of the wind turbine blades

Abstract: Wind power is a promising source of environmentally safe and renewable energy with a high potential. However, in order to fully exploit energy of wind power the construction elements of the wind turbine should be inspected periodically. Wind turbine blades are complicated objects for inspection because they have an arbitrary curved surface, are multi-layered, have variable thickness and are made from anisotropic materials.The presented study covers an overview of the techniques which are used or could be used for on site condition monitoring and effective NDT of wind turbine blades. Inspection methods based on vibration analysis, thermography, X-ray imaging, acoustic emission and ultrasound are reviewed. The objective of this study is review of different NDT techniques, which are used, or could be used for non-destructive testing of wind turbine blades, taking into account the complicated structure of the wind turbine blades as well as possibility to make non-destructive testing in harsh on-site conditions.

Model-based condition monitoring at the wheel–rail interface

Abstract: The dynamics of a rail vehicle is driven by the interaction between the wheel and rail. Any change to, for example, the shape of the wheel–rail profile or the contact adhesion conditions will change the response of the vehicle. The condition monitoring challenge is to interpret these changes into useful condition information. This paper presents the ongoing research into model-based condition monitoring at the wheel–rail interface applied to two applications: (i) wheel–rail profile estimation; and (ii) low adhesion detection. The wheel–rail profile estimation was carried out on a linearised simulation model that included a nonlinear conicity function. This function could be successfully estimated by also estimating the lateral track irregularities and giving the Kalman Filter self-updating information about the shape of the conicity function. The low adhesion detection was carried out on a complex nonlinear half vehicle model that included saturating contact force equations. The contact forces could be es...