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A non-parametric pattern classifying diagnostic method and its application
Abstract
The goal of this dissertation is to introduce a method of fault diagnosis that is designed to cope with fault signature variability, the main source of difficulty for the existing diagnostic systems. This method is a non-parametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model. In this method, process abnormalities are detected through processing the sensory data and flagging, and diagnostic reasoning is performed by matching the flagged measurements against the columns of the influence matrix. Fault signature improvement is achieved by a Flagging Unit, which is tuned based on a training set. This unit is shown to have the ability to improve detection, reduce false alarms, and enhance diagnostics. The improved fault signatures by the Flagging Unit are also shown to be beneficial to other classifiers such as the Bayes classifier and artificial neural nets. The applicability of the MVIM method is investigated in fault diagnosis of a helicopter gearbox. A total of five tests were performed, during which eight failures occurred. In order to enhance the effect of the failures on the vibration data, the vibration signals obtained from the gearbox were digitized and processed by a vibration signal analyzer. The parameters obtained from this signal analyzer were then utilized to train the MVIM method and test its performance for both detection and diagnosis. The averaged values of the parameters obtained from individual accelerometers were used to reduce the processing time. Training sets were formed based on parameters from various combinations of the five tests, and the MVIM method was tested based on the parameters from all of the five tests. Detection results indicate that the MVIM method provided excellent results when the full range of faults' effects on the vibration measurements were included in the training set. The MVIM method was also utilized to rank the parameters for their significance in detection. It is shown that through this ranking the optimal subset of parameters for detection can be selected, which is particularly important in reducing processing time for on-line detection. For diagnosis, the MVIM method was used in a hierarchical manner. The parameters from individual accelerometers were first processed through detection MVIMs, to trigger the presence of a fault, and then examined by diagnostic MVIMs to identify the fault. Diagnostic results show that the MVIM method had a correct diagnostic rate of 95% for the faults included in training. (Abstract shortened by UMI.)
Subject Area
Mechanical engineering|Aerospace engineering
Recommended Citation
Chin, Hsinyung, "A non-parametric pattern classifying diagnostic method and its application" (1993). Doctoral Dissertations Available from Proquest. AAI9316632.
https://scholarworks.umass.edu/dissertations/AAI9316632