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Research Papers

Fault Diagnosis of Gearbox Using Particle Swarm Optimization and Second-Order Transient Analysis

[+] Author and Article Information
Sajid Hussain

Department of Mechanical Engineering,
The Petroleum Institute,
P.O. Box 2533,
Abu Dhabi, United Arab Emirates
e-mail: sahussain@pi.ac.ae

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received May 12, 2016; final manuscript received November 21, 2016; published online February 22, 2017. Assoc. Editor: Philippe Velex.

J. Vib. Acoust 139(2), 021015 (Feb 22, 2017) (7 pages) Paper No: VIB-16-1250; doi: 10.1115/1.4035379 History: Received May 12, 2016; Revised November 21, 2016

Detection of faults in a gearbox is a first and foremost step before diagnostic and prognostic operations are performed. This paper proposes a novel gearbox fault detection and feature extraction technique. The proposed method adaptively filters the vibration signals emanating from a gearbox. A bandpass filter is designed and optimized through particle swarm optimization (PSO) to maximize kurtosis as an objective function. Gearbox health-related features are extracted from the filtered signals using second-order transient analysis. The method is validated on experimental data collected from a running gearbox in healthy and faulty conditions. The proposed method has successfully identified the faulty conditions inside the gearbox.

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Figures

Grahic Jump Location
Fig. 3

PSO velocity and position updates

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Fig. 4

Pseudocode—PSO (global best)

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Fig. 2

The bandpass filter

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Fig. 1

Flowchart of the proposed method—PSO adaptive filtering

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Fig. 6

Mechanical diagnostic test bed (MDTB)-ARL, Pennsylvania State University (PSU)

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Fig. 7

The Chebyshev bandpass filter optimized by the PSO

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Fig. 10

Natural frequency versus damping ratio—second-order transient analysis

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Fig. 5

Second-order spring mass system and impulse response

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Fig. 8

PPA and faulty gear RPM calculation

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Fig. 9

Second-order transient analysis features extraction

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