Research On Methods For Fault Diagnosis Of Rotating Machines Based On Independent Component Analysis

Based on the "Research on New Method for Rotating Machine Faults Diagnosis Based on Independent Component Analysis" (National Nature Science Fund Project, No: 50205025), and the "Research on Technique for Fault Diagnosis with Mechanical Noise Based on Blind Source Separation" (Nature Science Fund Project of Zhejiang Province, No: 5001004), this dissertation with full title "Research on Methods for Fault Diagnosis of Rotating Machines Based on Independent Component Analysis" was written. In this dissertation, a new frame for fault diagnosis based on source separation was proposed, some possible applications of independent component analysis to fault diagnosis of rotating machines were explored. The details were studied as follows:Chapter one recited briefly the intercrossed development of diagnostics with other knowledge firstly. By analysing the existing problems in diagnostics. Then, ICA theory and its worldwide applications were summarized, and its feasibility used for mechanical faults diagnosis was analyzed. Thus, a new frame for mechanical faults diagnosis based on source separation was proposed. At last, the centre, implementation and novelty of this dissertation were brought forth.Chapter two gave out ICA model and its custom implementation from the point of view of contrast function and optimisation algorithm firstly. Then, some important ICA (BSS) algorithms, especially the adaptive algorithms based on artificial neural network that would be used in this dissertation were described in details. Also, the improved BSS algorithm based on band-pass was introduced, which would be tested by some experiments in Chapter three.Chapter three explored the subject of source separation of mechanical vibration and acoustic observations by sensors based on BSS, including such units as forward-analysis, BSS and backward-analysis. In this subject, some strategies for improving ICA (BSS) algorithm were also proposed. Thus, a feasible and integrated solution to mechanical source separation was given out. At last, this scheme was verified by experiments.Chapter four explored feature extraction, as the key to fault diagnosis, on the background of ICA from the viewpoint of pattern recognition. Several novel and effective strategies for feature extraction based on ICA were proposed, and some traditional methods for feature extraction such as wavelet were improved.Chapter five explored the application of several typical ANN including BP, RBF and SOM, and SVM classifier to pattern recognition and classification of mechanical faults. And, a lot of experiments for faults classification were made to test these classifiers.Chapter six developed applicable software for fault diagnosis: BSS based software for mechanical sources separation and ICA based software for fault diagnosis. Ideally, BSS based technique for interference removal, ICA based strategy for feature extraction and some typical methods for pattern classification can be integrated as a practical software-hardware system for fault diagnosis of machines, along with traditional Analogy-Digital Converter (ADC). However, some related problems such as data communication, interface and real-time running, etc. must be solved. At last, an experiment with data from the real world was made by means of the ICA-based software.At last, in the seventh chapter, all of the work in this dissertation was summed up, and the futureresearches on applications of ICA (BSS) were prospected.