Extraction Method of Failure Signal by Genetic Algorithm and the Application to Inspection and Diagnosis Robot

Peng CHEN; Toshio TOYOTA

Extraction Method of Failure Signal by Genetic Algorithm and the Application to Inspection and Diagnosis Robot

Peng CHEN, Toshio TOYOTA

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In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability P_s (t^*_k) of failure signal" and statistical information I_qp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.12 pp.1620-1626

Publication Date: 1995/12/25

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Type of Manuscript: Special Section PAPER (Special Section on Acoustic Diagnosis)

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Peng CHEN, Toshio TOYOTA, "Extraction Method of Failure Signal by Genetic Algorithm and the Application to Inspection and Diagnosis Robot" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 12, pp. 1620-1626, December 1995, doi: .
Abstract: In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability P_s (t^*_k) of failure signal" and statistical information I_qp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e78-a_12_1620/_p

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@ARTICLE{e78-a_12_1620,
author={Peng CHEN, Toshio TOYOTA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Extraction Method of Failure Signal by Genetic Algorithm and the Application to Inspection and Diagnosis Robot},
year={1995},
volume={E78-A},
number={12},
pages={1620-1626},
abstract={In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability P_s (t^*_k) of failure signal" and statistical information I_qp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - Extraction Method of Failure Signal by Genetic Algorithm and the Application to Inspection and Diagnosis Robot
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1620
EP - 1626
AU - Peng CHEN
AU - Toshio TOYOTA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1995
AB - In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability P_s (t^*_k) of failure signal" and statistical information I_qp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.
ER -