A Web-Based Remote Intelligent Expert System for Ferrography Diagnosis

Abstract

Abstract. A ferrography expert system for wear particle analysi s can be an effective means of condition monitoring and fault diagnosis. The knowledge of the ferrography diagnosis expert system cannot be represented by certain rules because it is f uzzy and random, therefore an intelligent expert system is developed. A neural network with self -deleting nodes is employed in the intelligent expert system, and this overcomes to some degree the disadvantages of a traditional expert system and makes it possible to automatically acquire knowl edge by learning from samples and to realize entirely automatic processing, from wear particle rec ognition to wear diagnosis. Introduction Wear is one of the main factors causing breakdown and faults in mac hines. Therefore the ferrography technique for wear particle analysis can be an effective way for condition monitoring and fault diagnosis [1,2]. It is difficult for the ferrography tec hnique to be widely used because it mainly depends on operator’s experience. Therefore it is very useful to develop a ferrography diagnosis expert system. Some expert systems have been develope d in this field and led to some effective results. FAST, FAST PLUS [3,4], CASPA [5] and CAVE [6,7] are examples which employ traditional expert systems. It is difficult to define the knowledge of the ferrography diagnosis expert system by certain rules, because it is fuzz y and random. Some intelligent expert systems were studied [8,9], but they were still not fully automatic integrated systems. A web-based remote intelligent expert system for ferrogr aphy diagnosis (RIESFD), based on a neural network, fuzzy theory and Internet for wear particle ident ification. Wear diagnosis is presented in this paper. RIESFD overcomes to some degree the disadvanta ges of a traditional expert system, and it can automatically acquire knowledge by learning fr om samples and realize entirely automatic processing from wear particle identification to we ar diagnosis. It is very important for this expert system based on a neural network to be provided with enough samples for its learning, therefore it should be realized to collect all kinds of typical wear particle samples and share the knowledge between expert systems in different locations. The Internet is a good tool for the exchange and contact between different systems. Therefore the appl ication of a remote diagnosis course on Internet is the direction of the ferrography technology development. Basic Principle of RIESFD RIESFD is developed for full auto-identification of wear partic les and wear diagnosis. The system includes three steps: (1) Identify all wear particles on a ferrograph slice one by one, and acquire the information on the classification of wear particles at one time point. (2) Read all history information on wear particles from a database , and form the distribution of different kinds of wear particles with time. (3) Diagnose the wear fault on the basis of the above, and give the result. With the above idea, the system progress is shown as Fig. 1. The key techniques are wear particle identification and wear fault diagnosis, therefore a ne ural network is employed for this purpose. The wear particles are randomly produced during equipment running, and different wear particles can be produced at different states and different friction pai rs, therefore it is very important