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Proceedings of the International Seminar on Maintenance, Condition Monitoring and Diagnostics, MCMD 2005
Abstract
The papers published in these proceedings are presented in the Second International Seminar on Maintenance, Condition Monitoring and Diagnostics, to be arranged in Oulu, Finland, in 28th – 29th September, 2005. Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present seminar is supported by a variety of Finnish industrial enterprises.
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Machine condition monitoring enables reliable and economical way of action for maintenance operations in modern industrial plants. Increasing number of measurement points and more demanding problems require automatic fault detection. Advanced signal processing methods exposed failures earlier and then it's possible to plan more operating time and less shutdowns. Intelligent methods have been increasingly used in model based fault diagnosis and intelligent analysers. Intelligent methods provide various techniques for combining a large number of features. A test rig was used to simulate different fault types and changes in operating conditions. Linguistic equation (LE) models were developed for the normal operation and nine fault cases including rotor unbalance, bent shaft, misalignment and bearing faults. Classification is based on the degrees of membership developed for each case from the fuzziness of the LE models. The classification results of the experimental cases are very good and logical. As even very small faults are detected by a slight increase of membership, the results are very promising for early detection of faults. Together with the compact implementation and the operability of the normal model, this makes the extension to real world problems feasible.
In this competitive open world environment, it has become a challenge for Pulp and paper Industry to sustain good performance. Adherence to the maintenance approach with best practices is one of the key to upkeep the Machinery health well in order to deliver a good quality of product at competitive cost to reach and retain the customers. It is necessary to have strong maintenance strategy to convertmaintenance function in to a profit centre with structured approach, techniques and continual improvements. ITC Ltd Paper Boards and Specialty Papers division - Unit Bhadrachalam isimplementingPlanned Maintenance programas a part of manufacturing excellence journey for the past 10 years. Continual learning cum implementation journey has resulted in productivity improvement, reduced unplanned stoppages and optimum maintenance cost. © 2017 Indian Pulp and Paper Technical Association. All rights reserved.
A description is given of the methods and equipment used by manufacturers and users of rolling element bearings to measure bearing structure-borne noise. The primary technique used to assess bearing quality involves analysis of structure-borne noise in three standard frequency bands. It may be augmented by two additional diagnostic techniques - namely, signal crest detection and shock pulse emission analysis - which are particularly suited to detection of damage or contamination of bearing lubricant.
The New Life Theory, even in its simplified 'catalogue equation' form, provides a tool for predicting bearing life more realistically than the ISO standard methods. The new method can be used to study the effects on bearing life of contamination as well as lubrication life, and shows the benefits of using sealed bearings or improved external seals. For clean applications, the much enhanced reliability is demonstrated and there are opportunities for downsizing bearings, or uprating machine performance. (from paper)
In this paper, we study a maintenance model with general repair and two types of replacement: failure and preventive replacement. When the system fails a decision is made whether to replace or repair it. The repair degree that affects the virtual age of the system is assumed to be a random function of the repair-cost and the virtual age at failure time. The system can be preventively replaced at any time before failure. The objective is to find the repair/replacement policy minimizing the long-run expected average cost per unit time. It is shown that a generalized repair-cost-limit policy is optimal and the preventive replacement time depends on the virtual age of the system and on the length of the operating time since the last repair. Computational procedures for finding the optimal repair-cost limit and the optimal average cost are developed. This model includes many well-known models as special cases and the approach provides a unified treatment of a wide class of maintenance models.
In the last thirty years there have been many developments in the use of vibration measurement and analysis for monitoring the condition of rotating machinery while in operation. These have been in all three areas of interest, namely fault detection, diagnosis and prognosis. Of these areas, diagnosis and prognosis still require an expert to determine what analyses to perform and to interpret the results. Currently much effort is being put into automating fault diagnosis and prognosis. Major economic benefits come from being able to predict with reasonable certainty how much longer a machine can safely operate (often a matter of several months from when incipient faults are first detected). This article discusses the different requirements for detecting and diagnosing faults, outlining a robust procedure for the former, and then goes on to discuss a large number of signal processing techniques that have been proposed for diagnosing both the type and severity of the faults once detected. Change in the severity can of course be used for prognostic purposes. Most procedures are illustrated using actual signals from case histories. Part 2 of this article will appear in the May 2004 issue of S&V.
In spite of the long testing times, life tests were carried out under loads approaching field loads, ideal operating, elastohydrodynamic lubricating, and cleanliness conditions. The lower the loads the more the life values are exceeded which are obtained by means of the standard life calculation. At loads which, compared with field loads, are relatively high, the bearing does not fail from fatigue. Should damage occur, it is started, in agreement with theory, in the subsurface. Calculated life values are reached if solid contaminants enter the bearing and damage the surface. Bearings damaged by indentations approximate the theoretical connection between load and life. The influence of contaminants is great with small bearings and decreases with increasing bearing size. Consequently, large bearings have a larger capacity than calculated. Test results show possibilities for improving utilization of the capacity reserves of the bearings.
For the maintenance personnel the key questions in every day life are: Is everything working properly and if not should we do something? It is especially important to know when some action should be taken i.e. will the machine in question hold until the next scheduled maintenance or does it not? Considering this from the condition monitoring point of view it is important to get a reliable indication of an upcoming failure so early that the necessary maintenance tasks can be planned well in advance and really perform them when the production machinery is stopped for scheduled maintenance. It is not an easy task to predict from measured parameters how quickly the fault will progress. The paper discusses some possible models for the progress of condition monitoring parameters i.e. how the condition monitoring parameters indicate the development of wear as a function of time. The prediction of the development/increase of these parameters is based on regression analysis techniques. The choice of these models is discussed keeping in mind that for practical purposes they should be simple and fast to use. The models are tested with some very common components which suffer from a type of wear which tends to progress with increasing speed towards the end of the life of the component. The first example is from tool wear monitoring where the life of the tool is very short and the measured values usually follow a certain trend and the second example is from a bearing test where the trend of the measured parameter is not that obvious. In both cases the suggested regression analysis technique works very well and can give prognosis of the further development of the monitored parameter.