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Proceedings Paper

Experimental studies on intelligent fault detection and diagnosis using sensor networks on mechanical pneumatic systems
Author(s): Kunbo Zhang; Imin Kao; Sachin Kambli; Christian Boehm
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Paper Abstract

Fault is a undesirable factor in any mechanical/pneumatic system. It affects the efficiency of system operation and reduces economic benefit in industry. The early detection and diagnosis of faults in a mechanical system becomes important for preventing failure of equipment and loss of productivity and profits. In this paper, we present our ongoing research results on intelligent fault detections and diagnosis (FDD) on mechanical/ pneumatic systems. Using data from sensors and sensor network in an integrated industrial system, our proposed FDD methodology provides the analysis of necessary sensory information (for example, flow rates and pressure, as well as other digital sensor data) for the detection and diagnosis of system fault. In this experimental study, the leakage of pneumatic cylinder was the "fault." It was shown that the FDD analysis was able to make diagnosis of leakage both in location and size of the fault. In addition, the systematic fault and localized faults can be detected separately. The proposed wavelet method gives rise to the fingerprint analysis to recognize the patterns of the flow rate and pressure data - a very useful tool in intelligent fault detection and diagnosis.

Paper Details

Date Published: 10 April 2008
PDF: 9 pages
Proc. SPIE 6932, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008, 693247 (10 April 2008); doi: 10.1117/12.797198
Show Author Affiliations
Kunbo Zhang, SUNY at Stony Brook (United States)
Imin Kao, SUNY at Stony Brook (United States)
Sachin Kambli, Festo Corp. USA (United States)
Christian Boehm, Festo Corp. USA (United States)


Published in SPIE Proceedings Vol. 6932:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008
Masayoshi Tomizuka, Editor(s)

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