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Article type: Cover
2002 Volume 24 Issue 4 Pages
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
i-iii
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Article type: Index
2002 Volume 24 Issue 4 Pages
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Article type: Index
2002 Volume 24 Issue 4 Pages
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Hiromichi FUKUMOTO
Article type: Article
2002 Volume 24 Issue 4 Pages
263-
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Yoshiyuki IHARA
Article type: Article
2002 Volume 24 Issue 4 Pages
264-266
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Katsunori NOGAMI
Article type: Article
2002 Volume 24 Issue 4 Pages
267-275
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Shigeru UZAWA, Akihiko KOMATSU, Tetsushi OGAWARA
Article type: Article
2002 Volume 24 Issue 4 Pages
276-283
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Ryoichi YASUIKE
Article type: Article
2002 Volume 24 Issue 4 Pages
284-289
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Takeshi NATSUME
Article type: Article
2002 Volume 24 Issue 4 Pages
290-293
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Hiroshi OZAKI
Article type: Article
2002 Volume 24 Issue 4 Pages
294-
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Toshiyuki AOYAMA
Article type: Article
2002 Volume 24 Issue 4 Pages
295-296
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Akihiko MASUDA
Article type: Article
2002 Volume 24 Issue 4 Pages
297-
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Takeshi HASHIMOTO
Article type: Article
2002 Volume 24 Issue 4 Pages
298-299
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Hachiro KIDO
Article type: Article
2002 Volume 24 Issue 4 Pages
300-
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
301-303
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
304-306
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
307-
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
308-310
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Peng CHEN, Fang FENG, Toshio TOYOTA
Article type: Article
2002 Volume 24 Issue 4 Pages
311-322
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In the field of machinery condition diagnosis, it is most important and difficult to extract excellent symptom parameters for detecting failures. Spectrum of vibration or sound signal is usually used to monitor conditions and distinguish failures. However, in many cases, since the relationship between failures and spectrum cannot be made clear by theoretical analysis, and spectrum is always contaminated by noise, the symptom parameters are difficult to be extracted from the spectrum, by which the feature of the spectrum can be reflected for machinery condition monitoring. In order to overcome this difficulty, this paper proposes a sequential diagnosis method by statistical tests and possibility theory for condition monitoring of plant machinery. The feature frequency of vibration signal measured in each state can be extracted by the statistical tests, and symptom parameters in frequency domain can be calculated to monitor conditions of any machines of which failures can be detected by vibration or sound signal. In order to distinguish failures, the membership functions of the symptom parameters are established by probability density function, and failures can be sequentially distinguished by possibility theory. A practical example for gear equipment diagnosis is shown to verify the efficiency of the method.
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Article type: Appendix
2002 Volume 24 Issue 4 Pages
323-328
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
329-330
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
331-332
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[in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
333-334
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[in Japanese], [in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
335-336
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
337-338
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
339-340
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
341-342
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[in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
343-344
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[in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
345-346
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[in Japanese], [in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
347-348
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[in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
349-350
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2002 Volume 24 Issue 4 Pages
351-352
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Article type: Article
2002 Volume 24 Issue 4 Pages
353-354
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[in Japanese], [in Japanese], [in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
355-356
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Article type: Article
2002 Volume 24 Issue 4 Pages
357-358
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[in Japanese]
Article type: Article
2002 Volume 24 Issue 4 Pages
359-360
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Article type: Cover
2002 Volume 24 Issue 4 Pages
Cover2-
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