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Intelligent fault detection and diag...

Li, Xiaolin.

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Intelligent fault detection and diagnosis of mechanical-pneumatic systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent fault detection and diagnosis of mechanical-pneumatic systems./
作者:	Li, Xiaolin.
面頁冊數:	135 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 5050.
Contained By:	Dissertation Abstracts International66-09B.
標題:	Engineering, Mechanical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3189414
ISBN:	054232136X

Intelligent fault detection and diagnosis of mechanical-pneumatic systems.
Li, Xiaolin.

Intelligent fault detection and diagnosis of mechanical-pneumatic systems. - 135 p.

Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 5050.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2005.

The supervision of systems is important in modern engineering process and manufacturing automation. In order to improve maintainability and availability and to protect environment and personnel, faults have to be detected early enough for counter-measures. During the last few years, many research efforts were presented in the field of process supervision and fault diagnosis.

ISBN: 054232136XSubjects--Topical Terms:

783786
Engineering, Mechanical.

Intelligent fault detection and diagnosis of mechanical-pneumatic systems.
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245 1 0 $a Intelligent fault detection and diagnosis of mechanical-pneumatic systems.
300 $a 135 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 5050.
500 $a Adviser: Imin Kao.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2005.
520 $a The supervision of systems is important in modern engineering process and manufacturing automation. In order to improve maintainability and availability and to protect environment and personnel, faults have to be detected early enough for counter-measures. During the last few years, many research efforts were presented in the field of process supervision and fault diagnosis.
520 $a This dissertation presents the research in fault detection and diagnosis (FDD) for mechanical and pneumatic systems, which are often found in manufacturing floors for automation. The research in intelligent FDD utilizes data obtained from sensors and sensor network of manufacturing systems to prevent failure of devices that causes shutdown and loss of precious production time and profits in automation. The diagnosis systems presented in this dissertation include the model-based and signal-based approaches. In the model-based approach, the physical model of the system is employed in the analysis of fault detection and diagnosis (FDD) of pneumatic systems using the electro-pneumatic analogy. In the signal-based approach, methodology of pattern recognition is used in the construction of the FDD system. To this end, the wavelet methodology has been employed to reduce the redundancy which exists in various sensory signals. The signatures (features) of the sampled signals with known leakage are drawn from the coefficients of the wavelet decomposition to establish the relationship between the signature and the targeted faults. Vectorized maps are employed for the diagnosis of unknown faults based on the established FDD information. The step-wise Voronoi diagrams, which take advantage of the Voronoi but avoid the overhead of building a high-dimensional Voronoi diagram, are employed in fast search for fault diagnosis. Experimental results and analysis are presented to illustrate the FDD technique.
520 $a Finally, sensor reduction and resolution improvement are discussed. A wavelet manager, a cost function manager, and a classifier manager are proposed to increase the flexibility and error resistance of the system.
590 $a School code: 0771.
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