Summary When traditional machinery fault diagnosis methods are used to handle diagnostic problems, the problems such as low diagnosis accuracy and bad real‐time capability will arise if there are lots of data and various complex faults. An integrated fault diagnosis reasoning strategy based on fusing rough sets, neural network, and evidence theory is presented using the principles of data fusion and meta‐synthesis. Firstly, use the the parallel neural network structure to improve diagnosis ability of the local diagnosis networks; preprocess the data with rough set theory to simplify the complex neural networks; and eliminate redundant properties in order to determine the topological structure of network. By this way, the shortcomings of network, such as large scale and slow classification, can be overcome. Secondly, a new objectified method of basic probability assignment is given. Besides, the accuracy and efficiency of the fault diagnosis can be improved obviously according to the various redundant and complementary fault information by using the combination rule of the evidence theory to synthesize and make decisions on the evidence. The example of rotating machinery diagnostic given in the paper proves the method to be feasible and available.

To solve the difficulties in practice caused by the subjectivity, relativity and evidence combination focus element explosion during the process of solving the uncertain problems of fault diagnosis with evidence theory, this paper proposes a fault diagnosis inference strategy by integrating rough sets with evidence theory along with the theories of information fusion and mete-synthesis. By using rough sets, redundancy of characteristic data is removed and the unrelated essential characteristics are extracted, the objective way of basic probability assignment is proposed, and an evidence synthetic method is put forward to solve high conflict evidence. The method put forward in this paper can improve the accuracy rate of fault diagnosis with the redundant and complementary information of various faults by synthesizing all evidences with the rule of the composition of evidence theory. Besides, this paper proves the feasibility and validity of experiments and the efficiency in improving fault diagnosis.

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By analyzing multi-sensor information fusion system and hall for workshop of meta-synthetic engineering (HWME) essentially, a universal information fusion system of HWME based on multi-sensor is put forward. Analyzing the fault diagnosis framework of complex system based on information fusion technique, together with the research on the general process of information fusion synthesis fault diagnosis, a fault diagnosis system framework of HWME based on information fusion is set up, which is all-purposed and runs through the whole process of synthetic fault diagnosis. By this method, a new approach to multi-sensor information fusion synthesis fault diagnosis has been found.