Research On The Fault Diagnosis Technology For The Hydraulic Press Based On Multi-source Information Fusion And Rough Set Theory

Hydraulic press plays a very important role in modern manufacture, the failures of hydraulic press often lead to the stop of the production line, even the occurrence of the accident. So it is necessary to determine the work status of hydraulic press correctly. However, the hydraulic press is a complex system, with diversity, randomness, complexity, and the relevancy of information gathering. More than 10 kinds of signal need to be monitored, together with the means of acquisition information are easily affected by variety of factors,which result in the signal's uncertainty and causes the great difficulties of hydraulic press's fault diagnosis.Thus,the thesis researches on the advanced fault diagnosis method to obtain accurate working status of the hydraulic press.In this paper, the hydraulic press fault diagnosis is divided into two parts, one part is the hydraulic press power system, and the other part is the hydraulic press control system. We focused on discussion about hydraulic pumps of the power system and hydraulic control system's fault diagnosis.Due to poor working environment of the hydraulic pump, monitoring signal of the pump's output port is usually chaotic, and vulnerable to be interfered by the noises. The frequency characteristics of the information extracted from single sensor are often ambiguous. The conventional signal processing methods can not extract fault features effectively. As a result, it is necessary to take advantage of the different information from multi-sensor to obtain reliable estimates of the state of equipment. This paper utilizes the three directions's vibration signals of the hydraulic pump shell comprehensively,supplemented by temperature signals of the hydraulic pump output port. The method fuses the vibration signals in space to diagnose in first level and fuses the vibration and temperature diagnostic result in second level to diagnose the hydraulic pump accurately.Hydraulic press control systems of the hydraulic press involve many equipments, the collection of feature is very complicated. It is hard to obtain effective fault diagnosis rules and fault diagnosis is very difficult. Therefore, based on rough set theory , the fault rule extraction algorithm for the hydraulic control system was proposed, which extracted clear and regular fault rules through attribute reduction and decision-making network construction. It is easy to speculate the reasons for failure from the characters of the hydraulic control system with the extracted fault rules.Finally, a set of remote on-line monitoring and fault diagnosis system of the hydraulic press based on B / S structure was developed,which adopted the advanced fault diagnosis theory that proposed in this paper. It can get on-line data of equipment working state , transmit long-distance characteristics data and realize remote fault diagnosis. The practical application in the plant showed that the system solved the problem of the difficulty and inefficient in hydraulic press diagnosis and obtained better diagnostic results. The system can also be easily applied to other large equipment condition monitoring and fault diagnosis.The main innovation points are as follows:(1) The PARD-BP(PARD, Pruning Algorithm based Random Degree)neural network fault diagnosis method was introduced in this thesis,which pruned the redundant nodes of the BP neural network on the basis of random degree and division algorithm to obtain the reduced network structure. The reduced neural network has better generalization performance and the fault diagnosis conclusion is more credible;(2) The H-BP multi-stage neural network fault diagnosis method based on particle swarm optimization was proposed in this paper, which takes the advantages of the PSO's caculation ability to optimize the weight matrix of the Hopfield network, pre-processes the characteristics of the failure by the optimized Hopfield network, and fault diagnoses by the BP neural network. This method can effectively solve the problem that BP network is easy to fall into local minimum and can improve the accuracy of diagnosis effectively;(3) The 2-level multi source information fusion fault diagnosis model was proposed in this paper,which took full advantage of multi-sensor to maximize utilization of system resources.The model took PARD-BP neural network to carry out the 3-direction vibration singal diagnosis and fuses the diagnostic results in the first level;and then use the H-BP neural network for temperature singnal diagnosis;at last ,fused diagnosis at the second level taking the two diagnosis result as the independent evidences and constructing the mass function with the two signal's diagnosis results.Applied data fusion technology to the hydraulic pump fault diagnosis can obtain accurate state estimates to a certain extent and increase the confidence and improve the robustness of diagnosis;(4) In order to improve the efficiency of faults diagnosis rule extraction in hydraulic press control system,the reduction algorithm of rough set theory is optimized to shorten the rules' extraction time;at the same time,in order to effectively filter out noise and to deal with inconsistent rules,the concept coverage was introduced on the basis of the accuracy concepts to further evaluate the extracted rules and finally extracted the effective diagnostic rules.The examples proved the effectiveness of the method;(5) Intelligent fault diagnosis theory has been introduced to the system " The large-scale electrical and mechanical equipment on-line monitoring and fault diagnosis system based on B / S structure ".Compared with the traditional fault diagnosis system, the on-line sytsem with the intelligent fault diagnosis theory can avoid misdiagnosis and wrong diagnosis caused by excessive signal noise , and can monitor the status of the equipment real-time.