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Sensors 2012, 12(5), 5919-5939; doi:10.3390/s120505919
Article

An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network

1,2
, 1,*  and 2
Received: 27 March 2012; in revised form: 2 May 2012 / Accepted: 3 May 2012 / Published: 8 May 2012
(This article belongs to the Section Physical Sensors)
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Abstract: This study proposes a new condition diagnosis method for rotating machinery developed using least squares mapping (LSM) and a fuzzy neural network. The non-dimensional symptom parameters (NSPs) in the time domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using detection index (DI) is also proposed for detecting and distinguishing faults in rotating machinery. In order to raise the diagnosis sensitivity of the symptom parameters the synthetic symptom parameters (SSPs) are obtained by LSM. Moreover, possibility theory and the Dempster & Shafer theory (DST) are used to process the ambiguous relationship between symptoms and fault types. Finally, a sequential diagnosis method, using sequential inference and a fuzzy neural network realized by the partially-linearized neural network (PLNN), is also proposed, by which the conditions of rotating machinery can be identified sequentially. Practical examples of fault diagnosis for a roller bearing are shown to verify that the method is effective.
Keywords: condition diagnosis; least squares mapping; possibility theory; Dempster & Shafer theory; fuzzy neural network condition diagnosis; least squares mapping; possibility theory; Dempster & Shafer theory; fuzzy neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Li, K.; Chen, P.; Wang, S. An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network. Sensors 2012, 12, 5919-5939.

AMA Style

Li K, Chen P, Wang S. An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network. Sensors. 2012; 12(5):5919-5939.

Chicago/Turabian Style

Li, Ke; Chen, Peng; Wang, Shiming. 2012. "An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network." Sensors 12, no. 5: 5919-5939.


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