|Budget Amount *help
¥2,100,000 (Direct Cost : ¥2,100,000)
Fiscal Year 1999 : ¥500,000 (Direct Cost : ¥500,000)
Fiscal Year 1998 : ¥1,600,000 (Direct Cost : ¥1,600,000)
Recently, industry world wide has been experiencing profound changes as the result of the development of flexible and intelligent manufacturing system. This tendency towards unmanned plants will to continue to develop in the 21st century. In line with these developments, the role of plant maintenance will also continue to evolve to one of a "guarantor" or high productivity and quality.
In the field of condition monitoring for plant machinery, vibration or sound signal for measured for detection of failures and discrimination of kinds of failure. When the signals for the diagnosis are measured at an early stage of a machine failure or at a distant location from the failure parts, the extraction of failure signal and the early detection of failure are difficult, because the failure signal is strongly contaminated by noise. It is important to cancel the noise from the sound signal as cleanly as possible in order to increase the sensitivity of failure detection. For noise canceling, many me
thods have been proposed. For example, band pass filter, adaptive filter, Wiener filter, and Kalman filter etc.. But in the field of machinery diagnosis, these methods can not always be applied to failure signal extraction.
Furthermore. When using a computer for condition monitoring for plant machinery, excellent feature parameters are necessary, by which patterns can be precisely distinguished. Currently there is not an acceptable method for extracting the excellent feature parameter.
For overcoming these difficulties, this study proposes new method as follows.
(1) extraction methods of failure signal
1) Extraction method of the failure signal from thc signal measured in the abnormal state of a machine using genetic algorithms (GA) and statistical information.
2) Extraction method of failure frequency areas from spectrum measured in the abnormal state of a machine by sequential statistical tests.
(2) Automatic Generation Method of Feature Parameters
1) Self-reorganization of feature parameters in time domain by genetic algorithms
2) Self-reorganization of feature parameters in frequency domain by genetic algorithms.
3) Automatic generation method of feature parameters by Wavelet analysis and genetic algorithms for diagnosis of machine in unsteady operating conditions
(3) Intelligent diagnosis method
The "Partially-linearized Neural Network (P.N.N.)" and the knowledge acquisition method by rough sets have been proposed, in order to diagnosing failures of a gear equipment and processing ambiguous diagnosis by neural network.
The efficiencies of all the methods proposed in this study have been verified by applying them to practical failure diagnosis, such as, rolling bearing, gear equipment etc.. Less