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Sensors 2015, 15(9), 21857-21875; doi:10.3390/s150921857

Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 7 July 2015 / Revised: 21 August 2015 / Accepted: 24 August 2015 / Published: 31 August 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [933 KB, uploaded 31 August 2015]   |  

Abstract

Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV). View Full-Text
Keywords: gearboxes; multi-senor data fusion; relevance vector machine; ant colony optimization; fault detection gearboxes; multi-senor data fusion; relevance vector machine; ant colony optimization; fault detection
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. (CC BY 4.0).

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MDPI and ACS Style

Liu, Z.; Guo, W.; Tang, Z.; Chen, Y. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection. Sensors 2015, 15, 21857-21875.

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