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Vol. 158, Issue 11, November 2013, pp. 427-435

 

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Study on Multi-Equipment Failure Prediction Based on System Network
 
1 Fayu SUN, 1 Lei GAO, 1 Jinlong ZOU, 2 Tao WU, 3 Jing LI

1 Science and Technology on Electromechanical Dynamic Control Laboratory, Xian, Shaanxi 710065, China

2 Computer Science School, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China

3 School of Electronic Information Engineering, Xi'an Technological University, Xi'an, Shaanxi 710032, China

1 E-mail: sfy212@sina.com

 

Received: 19 August 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems

 

Abstract: For failure prediction and information interaction problems of multi-equipment health management, fault prediction technology of multi-equipment and multi-parameter was proposed based on the system network. The state judgment and fault diagnosis algorithm is adopted by SOM self-organizing feature map neural network, the network parameters and node structure are changed adaptively, and the real-time updating of running status of equipment fault detection is realized. The improved fault prediction algorithm based on Elman feedback artificial neural network promoted the characteristics of the approximate any nonlinear function with arbitrary precision. Referencing to historical data by the feedback for the health management of multi-equipment, the algorithm provided early detection, isolation, management and forecast for fault omen, incipient fault status and ancillary component failure state in multi-equipment health management. The self adaptive abilities and the robustness of fault prediction system are improved effectively.

 

Keywords: System network, Failure diagnosis, Failure prediction, Artificial neural network, Self-organizing feature map.

 

 

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