Study Of Rotating Machinery Fault Diagnosis Based On Rough Sets Theory

Rough Sets Theory (RS) is a new fruit in the field of studying artificial intelligent decision-making technology in recent years in the world. RS researches into knowledge of expression, learning and conclusion of inexact, incomplete or uncertain. This method can be used for the classification, reduction, data-mining and regulation establishment from obtained data. The main feature of the theory is the strong qualitative-analysis ability, i.e. without advance quantity description for some character or attributes, the internal regulation can be found out directly from the description aggregation by the application of approximation universe produced by indiscernible relation and attributes.The rotating machinery is the major part of machinery, and its work mainly depends on rotor and rotating part of this mechanical device. Once some fault occurs,there will be a great loss. Due to the strategy of "fault maintenance" instead of strategy of "overmuch maintenance" in management of equipment at present, many countries pay more attention to the study of fault diagnosis in rotating machinery. Recently, some great achievement in the field has been obtained in the world. There is a trend for the method of fault diagnosis from simple to intelligent and integration. Some intelligent decision-making systems come out, such as genetic algorithm, fuzzy mathematics and neural network. But it is difficult to establish the mathematics models of these decision-making systems, and the physical meanings of the mathematics models of these decision-making systems are else not clear. In addition, due to the long time on data treatment and self-studying, the on-line control becomes impossible. For the time being, study of RS focus mainly on how to deal with some simple fault diagnosis information tables and its feasibility of the method. But it has been less reported about the data collection of mechanical failure, signal treatment from sensor, forming of knowledge database and decision-table, and decision-table reduction using RS. Therefore, the applying study based on RS of rotating machinery fault diagnosis has become to a very attractive task. The project has been gotten financially aid by the Natural Science Fund of Jiangsu province in 2001(BK2001095). This provided a good foundation for further studying. The major innovations in the present study are as follows:1. Techno-way is ascertained for rotating machinery fault diagnosis on line based on RS. This paper mainly studies four kinds of typical rotating machinery faults including rotor misalignment, rotor unbalance, bear oil whip and shaft friction and hitting. Four kinds of typical fault principles and characteristic behavior are analyzed. Vibration analysis methodis a very important fault diagnoses way for rotating machinery. Characteristic information and sensitive parameter are collected by velocity sensor, displacement sensor and acceleration sensor. The different fault characteristic information is gotten by analysis of time domain, frequency domain and time sequence. Decision table is built by figure semantization and data treatment. Decision table is reduced using RS, the simplest decision table is made a choice, and standard characteristic database is done. State information checking and measuring from running rotating machinery is contrasted with standard information database, and then decision-making of fault diagnosis is made out.2. Mechanics model and achieving method are proposed about the four kinds of faults. In simulative experiment, bearing stands are hoisted through putting shim and bringing axis-line angle misalignment in rotor misalignment. Fixing eccentricity load in the rotor that is tested in dynamic balancing forms rotor unbalance fault. Oil whip fault is gotten, as clearance is big enough between axis and axis neck. Shaft friction and hitting fault are produced by friction bolt that is away from power and near bearing stand. CRAS5.1 data collection system is applied in rotor fault simulative experiment to collect information, analyze...