A New Technology of Fuzzy Logic in Machinery Monitoring

A New Technology of Fuzzy Logic in Machinery Monitoring

Copyright: © 2018 |Pages: 15
DOI: 10.4018/978-1-5225-3244-6.ch001
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Abstract

This chapter is based on fuzzy logic approaches to machinery failure analysis. Six companies and organisations were used as models for validation case studies. An introduction to fuzzy logic is done in this chapter of the book. As part of this book, it explains the marriage of condition-based maintenance (CBM) in artificial intelligence (AI) specifically using fuzzy logic as a subset of machine intelligence. The idea is to make the machine think intelligently like a human being. The International Standardization Organisation (ISO) has now set up a committee of condition monitoring and diagnosis of machines. Many manufacturing companies are pushing their production equipment for every ounce of capacity while, at the same time, trying to cut their overhead costs. This has put a strong emphasis on the importance of quality maintenance services used to care for systems. Service and maintenance are becoming essential for companies to sustain manufacturing productivity and customer satisfaction at the highest possible level.
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Introduction

One of the most important tools in minimizing downtime, whether or not a conventional preventive maintenance program is possible, is called “preventive engineering.” Although this would appear to be the application of common sense to equipment design maintenance engineering, it is a field which is often neglected. Too often maintenance engineers are so busy handling emergency repairs or in other day-to-day activities that they find no opportunity to analyze the causes for breakdowns which keep them so fully occupied. While most engineers keep their eyes open to details such as better packings, longer-wearing bearings, and improved lubrication systems, true preventive engineering goes further than this and consists of actually setting aside a specific amount of technical manpower to analyze incidents of breakdown and determine where the real effort is needed; then through redesign, substitution, changes, and specifications, or other similar means, reducing the frequency of failure and the cost of repair (Linderly, Higgins & Mobley, 2002).

The high costs in maintaining today’s complex and sophisticated equipment make it necessary to enhance modern maintenance management systems. Conventional condition-based maintenance (CBM) reduces the uncertainty of maintenance according to the needs indicated by the equipment condition. Intelligent predictive decision support system (IPDSS) for CBM supplements the conventional CBM approach by adding the capability of intelligent condition-based fault diagnosis and the power of predicting the trend of equipment deterioration. An IPDSS model, based on the recurrent neural network (RNN) approach, was developed and tested and run for the critical equipment of a power plant (Yam, Tse, Li & Tu, 2001).

These valuable results could be used as input to an integrated maintenance management system to pre-plan and pre-schedule maintenance work, to reduce inventory costs for spare parts, to cut down unplanned forced outage and to minimise the risk of catastrophic failure (Yam, Tse, Li & Tu, 2001). As soon as a bad condition is shown intelligently then react to it as soon as you see it.

Figure 1.

Maintenance advice is made in accordance with equipment condition

Yam, Tse, Li & Tu, 2001.

In this research a move from the conventional CBM is needed to come up with intelligent CBM using fuzzy logic approach.

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Research Background

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports (Shahanaghi, Babaei, Bakhsha & Fard, 2008).

Figure 2.

Three steps in a CBM program

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