Research On Method And System Of Information Fusion And Fault Diagnosis Of Smoke Exhauster Fan Of Alumina Sintering Kiln

Smoke exhauster fan is important equipment in economic construction. It is of great significance to research intelligent fault diagnosis method. At present, the main fault diagnosis method of smoke exhauster fan is frequency spectrum diagnosis. As the development of intelligence diagnosis technology, diagnosis expert system have appeared consecutively, specifically for single fields, for instance, rotator fault diagnose system, motor fault diagnose system etc.. Since the effect factor of machinery structure difference and adverse circumstances, especially in metallurgy, mine etc., therefore, diagnosis effect is relatively poor. In order to improve accuracy of monitoring and diagnose of smoke extaction fan, the author apply the advanced theory and algorithm synthetically such as information fusion theory, signal pretreatment method of lifting wavelet, blind source separation, BP-ART2 neural networks fault diagnose, multi-experts collaborative diagnose theory, and so on, to fuse multi-sensor information by multi-diagnosis method on multi-level structure, on basis of research of theory, and develop two kind monitorings and fault diagnosis systems of smoke extaction fan: PC concentrated and DSP distributed.In signal pretreatment of information fusion theory, a kind improved wavelet threshold function and splitting changing function of lifting wavelet is designed, and an adaptive de-noising method of lifting wavelet on basis of local feature of signals is brought forward , which gain fine signal treatment effect.On the data level of information fusion theory, under uncertain situation of number of fault source in dynamic estimation of source number, a source number estimation algorithm based on exhibition fourth-order cumulants is designed, and an adaptive fault diagnosis method of blind source separation which chooses the relactive algorithm such as determine, over-determined and Under-determined, according to the relation of the source number and the sensor number is researched. This method is able to distinguish and diagnose of the dynamic fault of smoke extaction fan effectively.On the feature level of information fusion theory, an improved BP-ART2 neural network fault diagnosis method which utilizes synthetically both benefit of BP neural network and ART2 adaptive resonance theory is researched. On the iput layer of ART2 structure an nonlinear hidden layer is adden to reduce the dimension of input feature and inprove the diagnosis efficiency of ART2 neural network . In the fault cluster of ART2 an local adaptivly adjustive algrithm based on warning threshod of ART2 is designed, and respective threshod is interposed to every cluster , and the hidden layer mapping weight is adjusted according to the error between clustering result and the expected value. On the index of clustering deciding double indexes is adopt to judge the threshod, the first one is the amplitude error between the signal and corresponding clustering centre, the second one is the warning threshod . When the both indexes are satisfied clustering is successful.Secondly, On the feature level of information fusion theory, aimed at various diagnose method of smoke exhauster fan such as rotor fault diagnosis, electricity fault diagnosis, electromechanical coupling fault diagnosis, and so on, on the basis of machinery diagnosis and electricity diagnosis, a multi-experts coordination diagnosis system based on blackboard is researched which fuses synthetically time-domain diagnosis and frequency-domain diagnosis, machinery diagnosis and electricity diagnosis, and realizes double fault diagnosis of machinery and electricity diagnosis. According to the diagnosis logic structure of the blackboard multi-experts coordination diagnosis system is divided to 8 layers, each layer contains corresponding diagnosis condition, diagnosis method and diagnosis conclusion. A corresponding blackboard supervision mechanism is built and multi-experts fusion diagnosis algorithm is designed.On the decision level of information fusion theory, a multi-sensor weighted incentive fusion method is designed, which imitates diagnosis experts to consider many sensors diagnosis information synthetically, and realizes several sensor diagnosis results comparing and certificating mutually, and according to the weighted stimulates fusion modulus matrix between each two sensors, analyzes stimulating and strengthening degree, and calculates weighted fusion result, fuses and normalizes synthetically all two sensors weighted fusion results, and comes to a conclusion of multi-sensor fault fusion diagnosis, and aimed at the local diagnosis results of multi-method of diagnosis adopts D-S evidence theory to make global fusion and come to decision-making fusion conclusion.Adopting the signal treatment and fault diagnosis methods which are researched in this dissertation, according to the actual state of smoke exhauster fan, two kind monitoring and fault diagnosis systems of smoke exhauster fan are developed: PC concentrated and DSP distributed, which already have been successfully applied to the smoke exhauster fan of several enterprise, and realized condition real-time monitoring and fault diagnosis.