Research On Key Technologies In Maintenance Basis Optimization For Equipment Of Steam Power Plant

Scientific decision making is the key guarantee for equipment's safety, reliability and economic running. According to the actuality of maintenance basis optimization (MBO) being popularized in Chinese electric power industry and the problems during its application, the key technology during MBO, which are reliability spot inspection (RSI), fault diagnosis, condition prediction and estimation, risk evaluation and maintenance decision making, are researched, and the MBO system for equipment of steam power plant is also developed in this thesis.Aim at the problem of blindness during implement of spot inspection in steam power plant, RSI is proposed and the model of RSI is built. RSI is made of two phases which are static programming and dynamic regulation. From the point of system's inherent structure function, static programming takes weighted fault mode, effect and criticality analysis (WFMECA) to equipments, and programs spot inspect tasks according to equipment's inherent importance, which assures enough supervision on important equipments and avoids un-essential spot inspection tasks. Dynamic regulation is to estimate equipment's condition periodically and regulate spot inspect tasks according to equipment's present running risk, which avoids over spot inspection and deficiency spot inspection. Compared with traditional spot inspection, RSI can both assure the necessary reliability of equipment and save cost, thus, it improves the efficiency and economy of spot inspection. Importance analysis is the basis of RSI and maintenance decision making, a new equipment's importance index that is synthetic weigh is proposed. Synthetic weigh reflects the practical importance of equipment in system by synthesizing the equipment's inherent structure, basis function and running station.In order to deal with the inconsistent and redundance information, a model of vibration faults diagnosis based on Rough Set and information fusion is brought forward based on the analysis of vibration characteristic of rotary machine. The model constructs a Rough decision making system by using vibration information entropy and deduces decision making rules by reduction, which not only realizes the diagnosis for unification vibration imformation, but also for the inconsistent vibration imformation by a Rough factor. So, the model improves the identification of vibration fault mode. At the same time, a fault diagnosis model for electric equipment with complex fault modes is proposed too. The model realizes the fuzzy fault modes identification for this kind equipment by calculating the approach degrees between fault mode and standard modes.An improved GM(1,1) model is brought forward to disposing the problem of fluctuant character parameters caused by outside interference. The model translates the fluctuant sequence to a exponential rule sequence by a operator of m points, and constructs an identical demensions and a new information model to reduce the grey space, thus, it realizes the effective prediction for fluctuant sequence. Furthermore, a new concept of grey space relation is advanced and defined. Grey space relation reflects the approximation degree of two sequences both in distance and shape, which is a quantity index that denotes the relation between sequences. A grey model of condition evaluation for electric equipment based on grey space relation is constituted. By calculating the grey space relation between condition parameter sequence and rating condition parameter sequence of evaluated equipment, the approximation degree of equipment condition and rating condition is obtained, thus, the fix quantity evaluation of equipment is realized, especially, the model preferably evaluates the equipment condition when a few parameter deviate the rating condition parameter badly.According to the equipment's risk analysis, equipment's running vulnerability is defined and risk evaluation model is built. The model combines the factors of equipment's condition, failure sequent and running tendency, and constructs the vulnerability's 3-demensions drawing by quantifying the vulnerability' fuzzy rules to realize the equipment's risk evaluation. Base on the importance analysis, the maintenance mode optimization for equipment is realized by logic analysis, which provides the most effective maintenance mode for equipment's fault. At the same time, a maintenance time optimization model is built. The model takes the unit's benefit as object function by synthesizing the factors of generating unit cost, electricity selling price, unit generating electricity amount, maintenance cost, break down loss, and saved maintenance cost because of prolonging maintenance interval, then makes the optimization maintenance time for prediction maintenance. Finally, MBO system has been developed according to the above research. The system consists of equipment management module, RSI management module, fault diagnosis module, equipment's condition prediction and estimation module, equipment's running risk evaluation module, maintenance decision making module and maintenance management module, which realizes a whole MBO procedure. Some of the modules are developed and applied with good effect.