An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques

Abstract

Fault Detection in rotating machinery can be done by regular condition monitoring and vibration analysis of machinery. The task of condition monitoring and fault diagnosis of rotating machinery faults is both significant and important but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert knowledge. Various diagnostics methods have been proposed for different types of rotating machinery. However, little research has been conducted on synthesizing and analyzing these techniques, resulting in apprehension when technicians need to choose a technique suitable for application. Various automatic detection methods were introduced, even though they are confined to one or few problems and are not accurate and reliable. Hence automatic fault detection technique are required which are reliable, fast and accurate that can be applied to find solutions to numerous problems. Intelligent condition Monitoring System for fault detection based on expert system is an automated system that detects errors in machinery based on trained neural network model. The objective of the current investigation is to introduce a novel approach to Intelligent Condition Monitoring System which can work with the implementation of Back propagation Neural Network and Radial Basis network and genetic algorithm (which can used for optimization in selecting the network). Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. An ANN was optimized for efficient fault diagnosis in machinery equipment. Also this system is an Online Web-based conditioning monitoring system to be applied successfully in preventing machinery failures and exclusively tested on Air blower. The results are compared with manual calculations and found to be accurate and reliable.