Research And Applications On Intelligent Multi-Fault Diagnosis Methods Based On Sympotom Analysis

With the rapid development of modern industry and sci-technology, the industry equipments increasingly become large-scaled, continued, networked, high-speeded and automated, which accordingly leads to more and more functions, and more and more complicated structures. Meantime, multi-fault occur simultaneously in mechanical equipment has become frequently, which requires the new development of intelligence fault diagnosis.Due to single fault may be caused by many reasons, and one reason may cause many faults. Thus, there is a need to form a multiple-to-multiple map. As different faults and symptoms form different aggregations or regions, the multi-fault diagnosis has become a very complicated task. In traditional intelligence fault diagnosis process, the characters of multi-fault are not deeply analyzed and processed. The methods and systems designed to diagnose multi-fault are incomplete, the systematic research is not yet developed widely. Therefore, there are differences between diagnosis results and actual faults in ordinary fault diagnosis system. More researches for designing a multi-fault intelligence diagnosis method and symptoms based multi-fault description are needed. Especially, researches on the description and diagnosis of symptoms are needed as well.Many fault diagnosis methods and applications are analysed in this paper. Aiming at the fault that its symptom is numerical or can be converted to numeral, the definition of multi-fault diagnosis is proposed. At the same time, the deficiencies and limitations of existing multi-fault diagnosis methods are analysed. The relationship between multi-fault diagnosis and mono-fault is discussed, and the research direction and route are proposed. The main research results are:Both description of fault symptom and multi-fault model are discussed in this paper. Focused on mapping realtionship"multi to multi"between fault and symptom in actual fault system, conducted researches on fault symptom vector and the description for multi-fault diagnosis, the mapping relationship"symptom to fault"is proposed. Meanwhile, according to symptom indications in different kinds of multi-fault, defined multi-fault composed of several symptoms of single faults as separable multi-fault. On the other hand, for multi-fault that composed of the coupled symptoms, hard to be decoupled by normal mathematic methods are defined"symptom couple"multi-fault, artificial neural network (ANN) based researches is conducted as well.According to description of multi-fault symptom, symptom is mapped in fault-space, the space-indication of symptom vector is established. Euclidian distance, similarity and marching score are taken as diagnosis coordinates. Application of normalization and fuzzy control for dereferencing fault symptom vector is discussed. For"separable"multi-fault, diagnosis methods based on logic mathematical, correlation coefficient, probability calculation are proposed. Symptom analysises of all of the three methods are using methmatical calculation, the relationship and difference between actual fault and known faults are shown in numerical vaule intuitively, which can be used as important factor and refenrence.A multi-fault diagnosis method based on self-organizing map (SOM) and optimization of the adjacent-searching is developed. A SOM neural network that used to multi-fault diagnosis is created. The inaccurate clustering of traditional SOM algorithm is analyzed. According to the analysis, Euclidean distance is taken as the main discrimination, and the adjacent-searching algorithm is optimized. Using the optimized algorithm, the cluster results of input samples are obtained, symptomss of faults are mapped, and a multi-disease diagnosis model is developed. The proposed SOM-based model has three layers. The symptom array of faults can be accurately sorted and clustered using the optimized model.A multi-fault diagnosis method based on combining radial base function (RBF) and self-associational memory is developed. Aiming at a problem that the medium Hopfield neural network only has self-associational memory property, according on proposed multi-fault diagnosis method bases on self-associational memory and fuzzy control, RBF neural network is used to improving the method. The associational memory capability of Hopfield neural network, the adaptive capacity and self-learning capacity of RBF nerual network are used to memorizing and classification. Through the implementation of the process, all of the symptom and fault are classified and mapped, and multi-fault diagnosis is achieved.A multi-fault diagnosis method based hetero-associative memory is developed. Taking bidirectional associative memory neural network as reference, a Hopfield network that has hetero-associative memory property is proposed. Four weight matrix building method are discussed.Based on the actual acquired data, the relationship and deference between fault diagnosis and disease diagnosis are annlysed. The commonality of the multi-fault diagnosis methods proposed in this paper is proved. The turbine generator fault system and tomato disease are taken as inspecting and verifying objects for these methods. The multi-fault character is obvious in the systems that have typicality. The simulation results show that the proposed methods perform well and the proposed multi-fault (disease) diagnosis is effective, and the result corresponds to the diagnosis requirement. On the other hand, the methods can simulate human-thinking, distinguish mono-fault and multi-fault, and provide diagnosis strategy support.Finally, the range of applications, merits and demerits of the methods are discussed, all of the research in this paper is summarized. At the same time, the key points of multi-fault diagnosis researching in the future are proposed.