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Maintenance Online : Maintenance & Asset Management Journal : ABSTRACTS : VOLUME 13, NUMBER 5, DECEMBER 1998
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Vol 13, No 5, The Maintenance Theory Jungle - Deryk Anderson, Director, Maintenance Management Solutions Pty Ltd
An increasing amount of attention has been paid to the subject of maintenance over the past twenty years. Academics, industrial practitioners and consultants have proposed numerous theories connected with maintenance management during this period. This has resulted in a Maintenance Theory Jungle, a region of confusion and conflict for many that are entangled in it. This paper identifies and classifies the major areas of entanglement and proposes solutions for eliminating them.
Price: £5.00
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Vol 13, No 5, The Maintenance Benefits of Smart Technolology - Chris Kirkham, Brunel University
The field of machinery maintenance is facing a revolution. The continued evolution of computing power and technology is facilitating the embodiment of intelligent tools on to the desktop. These tools are no longer the toys of researchers and academics with super-computers, but find important applications in business and in the improvement of product quality and design.
The term Smart Technology applies to a group of adaptive, or intelligent, computing techniques that embody human learning, pattern recognition, and decision making abilities. The more popular techniques to which this term applies are Neural Computing, Fuzzy Logic, Knowledge Based Systems, Genetic Algorithms, and Case Based Reasoning. These generally perform more rapidly, reliably and consistently than their conventional, and even human, equivalents.
Maintenance engineers are facing an uphill battle of increased workload, more demanding plant operation requirements, wider application of machinery monitoring, budgetary restrictions and downsizing of the workforce, the net result of which is an increase in the numbers of measurement points that have to be processed. Smart computing could provide a solution an intelligent analysis assistant.
Price: £5.00
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Vol 13, No 5, Maintenance of Robots in Automated Production. Part 1 - a survey of Plant and Data Collection - Richard Wynne, Sheffield Hallam University; Andrew Starr, University of Manchester
Robot failures are costly and difficult to diagnose. Advanced diagnostics and condition monitoring can reduce breakdowns and downtime, but it is important to have a complete picture of the problem. Breakdown data is hard to obtain, so this study has first focused on collection and analysis of robot failure information. Historical records for about two hundred robots in automotive applications were analysed, identifying the major failure modes. The study uncovered significant new industrial evidence of the need for advanced diagnostic techniques in industrial robotics.
This paper describes the plant, and presents a critical review of existing maintenance and condition monitoring methods. The data collection methods were analysed, and the limitations of such systems are discussed. The paper concludes with a brief resume of the results, which will be reported in detail in further papers. Subsequent reports will analyse the problems of selecting condition monitoring methods for robots and will review measurement methods, before presenting field and laboratory results of condition monitoring studies.
Price: £5.00
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Maintenance Online : Maintenance & Asset Management Journal : ABSTRACTS : VOLUME 13, NUMBER 5, DECEMBER 1998
| The maintenance and asset management web site |
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