Research Of Fault Diagnosis Method In Rotating Machinery Based On Neural Network

In these years, machinery fault diagnosis technology has grown rapidly and has been used in nearly every part of industrial field. However, for rotating machinery, it is very difficult to carry out fault diagnosis for its complicated structures and the ubiquitous fuzziness and complexity in character and causation of the fault. Therefore, the research on rotating machinery fault diagnosis is of great significance.In view of the characteristic of rotating machinery fault, the fault diagnosis methods of neural network and fuzzy theory are researched. After comparing their respective good and bad points, a rotating mechanical failure diagnosis method based on fuzzy neural network (ANFIS) is applied, which has the merits of real-time computing, low mistakenly reporting ratio, simple algorithm and multi-parameters compatibility. The method is applied to the fault diagnosis of rotating machinery and the experimental result indicates that this method, compared with the common one, can make up the shortcoming of the single-handed application of fuzzy classification or neural network. Moreover, it owns the better validity and popularity .It has a good application prospects in rotating machinery fault diagnosis.Based on the deep analysis of the diagnosis process for rotating machinery fault diagnosis system, the machinery fault diagnosis research has accomplished the design of rotating prototype by the Visual C++6.0 language. The validity of the fault diagnosis system was well tested and simulated by eigenvectors, which are common appeared in rotating machinery fault status.