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Research Papers

Fault Diagnostics of Helicopter Gearboxes Based on Multi-Sensor Mixtured Hidden Markov Models

[+] Author and Article Information
Zhongsheng Chen1

Yongmin Yang

Key Laboratory of Science and Technology on ILS, College of Mechatronics Engineering and Automation,  National University of Defense Technology, Changsha, Hunan, P. R. C., 410073

1

Corresponding author.

J. Vib. Acoust 134(3), 031010 (Apr 24, 2012) (8 pages) doi:10.1115/1.4005830 History: Received November 28, 2010; Revised November 15, 2011; Published April 23, 2012; Online April 24, 2012

Accurate identification of faults in gearboxes is of vital importance for the safe operation of helicopters. Although hidden Markov models (HMMs) with Gaussian observations have been successfully used for fault diagnostics of mechanical systems, a Gaussian HMM must assume that the observation sequence is generated from a Gaussian process. Conversely, vibration signals from helicopter gearboxes are often non-Gaussian and non-stationary. Also, it always needs to use multi-sensors for more accurate fault diagnostics in practice. Thus, a classical Gaussian HMM may not meet the need of helicopter gearboxes, and it needs to study novel HMMs to model multi-sensor, non-Gaussian signals. This paper presents a multi-sensor mixtured HMM (MSMHMM), which is built on multi-sensor signals. For a MSMHMM, each sensor signal will be considered as the mixture of non-Gaussian sources, so it can depict non-Gaussian observation sequences very well. Then, learning mechanisms of MSMHMM parameters are formulated in detail based on the expectation-maximization (EM) algorithm and a framework of MSMHMM-based fault diagnostics is proposed. In the end, the proposed method is validated on a helicopter gearbox, and the results are very exciting.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 2

Basic framework of MSMHMM-based fault diagnostics

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Figure 3

Experimental setup of a helicopter gearbox

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Figure 4

Two kinds of faults on the bearing 1

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Figure 5

Optimal state sequences under different conditions after training

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Figure 6

Identified results of MSMHMM1 using testing samples under three conditions

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Figure 1

A graphical MSMHMM

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Figure 10

Identified results of GHMM2 using testing samples under three conditions

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Figure 11

Identified results of GHMM3 using testing samples under three conditions

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Figure 7

Identified results of MSMHMM2 using testing samples under three conditions

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Figure 8

Identified results of MSMHMM3 using testing samples under three conditions

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Figure 9

Identified results of GHMM1 using testing samples under three conditions

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