Research Papers

Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems

[+] Author and Article Information
Mohammed A. Hassan

Electrical Engineering Department,
Fayoum University,
Faiyum 63514, Egypt;
Centre of Excellence for Predictive Maintenance,
The British University in Egypt,
Cairo 11837, Egypt
e-mail: hassanm@fayoum.edu.eg

Michael R. Habib

Electrical Engineering Department,
Fayoum University,
Faiyum 63514, Egypt
e-mail: mrw11@fayoum.edu.eg

Rania A. Abul Seoud

Electrical Engineering Department,
Fayoum University,
Faiyum 63514, Egypt
e-mail: raa00@fayoum.edu.eg

Abdel M. Bayoumi

Fellow ASME
Mechanical Engineering,
Center for Predictive Maintenance,
College of Engineering and Computing,
University of South Carolina,
300 Main Street, Room A223,
Columbia, SC 29208
e-mail: bayoumi@sc.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 18, 2017; final manuscript received October 5, 2017; published online December 22, 2017. Assoc. Editor: Yongchun Fang.

J. Dyn. Sys., Meas., Control 140(6), 061009 (Dec 22, 2017) (9 pages) Paper No: DS-17-1156; doi: 10.1115/1.4038243 History: Received March 18, 2017; Revised October 05, 2017

Condition monitoring and fault diagnostics in rotorcraft have significant effect on improving safety level and reducing operational and maintenance costs. In this paper, a new method is proposed for fault detection and diagnoses of AH-64D (Apache helicopter) tail rotor drive-shaft problems. The proposed method depends on decomposing signal into different frequency ranges using mother wavelet. The most informative part of the vibration signal is then determined by calculating Shannon entropy of each part. Bispectrum is calculated for this part to investigate quadratic nonlinearities in this segment. Then, search algorithm is used to extract minimum number of indicative features from the bispectrum, which are then fed to classification algorithms. In order to quantitatively evaluate the proposed method, six classification algorithms are compared against each other such as fine K-nearest neighbor (KNN), cubic KNN, quadratic discriminant analysis, linear support vector machine (SVM), Gaussian SVM, and neural network. Comparison criteria include accuracy, precision, sensitivity, F score, true alarm, recall, and error classification accuracy (ECA). The proposed method is verified using real-world vibration data collected from a dedicated AH-64D helicopter tail rotor drive train (TRDT) research test bed. The proposed algorithm proves its ability in finding minimum number of indicative features and classifying the shaft faults with superior performance.

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Fig. 1

(a) TRDT test stand at USC and (b) location of the TRDT on an actual AH-64D

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Fig. 2

Steps of the proposed fault diagnosis system

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Fig. 3

Three levels of wavelet signal decomposition

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Fig. 4

Neural network's layers and connections

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Fig. 5

AHB sample decomposition: (a) level 1 details, (b) level 2 details, (c) level 3 details, and (d) level 3 approximation

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Fig. 6

Average Shannon entropy: (a) for AHB signal and (b) for FHB signal

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Fig. 7

Cross bispectrum example: (a) between AHB and FHB and (b) between FHB and AHB

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Fig. 8

Selected features: (a) feature 1–feature 2, (b) feature 3–feature 4, and (c) feature 1–feature 5

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Fig. 9

Comparison between different classifier results



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