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Research Papers

An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation

[+] Author and Article Information
Dong Wang

School of Aeronautics and Astronautics,
Sichuan University,
No. 24 South Section 1, Yihuan Road,
Chengdu, Sichuan 610065, China
e-mail: dongwang4-c@my.cityu.edu.hk

Qiang Miao

School of Aeronautics and Astronautics,
Sichuan University,
No. 24 South Section 1, Yihuan Road,
Chengdu, Sichuan 610065, China
e-mail: mqiang@scu.edu.cn

Qinghua Zhou, Guangwu Zhou

School of Aeronautics and Astronautics,
Sichuan University,
No. 24 South Section 1, Yihuan Road,
Chengdu, Sichuan 610065, China

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received April 28, 2014; final manuscript received October 10, 2014; published online November 14, 2014. Assoc. Editor: Prof. Philippe Velex.

J. Vib. Acoust 137(2), 021004 (Apr 01, 2015) (12 pages) Paper No: VIB-14-1154; doi: 10.1115/1.4028833 History: Received April 28, 2014; Revised October 10, 2014; Online November 14, 2014

Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a high-order complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brand-new state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.

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Copyright © 2015 by ASME
Topics: Gears , Errors , Signals
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Figures

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Fig. 1

Frequency spectra: (a) of the complex Morlet wavelets with different bandwidths and (b) of the developed high-order complex Morlet wavelets with different orders

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Fig. 2

Temporal signals: (a) a raw normal gearbox vibration signal and (b) a normal gear residual error signal

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Fig. 3

Temporal signals: (a) a raw gearbox vibration signal subjected to gear faults and (b) a gear residual error signal subjected to gear faults

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Fig. 4

The proposed intelligent prognostic system for gear performance degradation assessment and RUL estimation

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Fig. 5

The test rig used for collecting accelerated gear degradation signals [5]

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Fig. 6

Gear health condition monitoring by using: (a) the kurtosis of the raw gear signals and (b) the RMS of the raw gear signals

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Fig. 7

Gear health condition monitoring by using: (a) the kurtosis of the gear residual error signals obtained by the proposed method and (b) the RMS of the gear residual error signals obtained by the proposed method

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Fig. 8

Early gear fault detection and performance degradation assessment by using (a) HI1 and (b) HI2

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Fig. 9

The gear residual error signals obtained by the developed high-order comblet: (a) at file number 34, (b) at file number 35, (c) file number 36, and (d) at file number 37

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Fig. 10

The gear residual error signals obtained by the developed high-order comblet: (a) at file number 38, (b) at file number 39, (c) file number 40, and (d) at file number 41

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Fig. 11

Predicted results obtained by using the developed method at file number 80 for gear prognosis

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Fig. 12

Predicted results obtained by using the developed method at file number 110 for gear prognosis

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