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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June 2018
Research-Article
Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems
Mohammed A. Hassan,
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
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
Search for other works by this author on:
Abdel M. Bayoumi
Abdel M. Bayoumi
Professor
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
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
Search for other works by this author on:
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
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
Rania A. Abul Seoud
Abdel M. Bayoumi
Professor
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
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. Jun 2018, 140(6): 061009 (9 pages)
Published Online: December 22, 2017
Article history
Received:
March 18, 2017
Revised:
October 5, 2017
Citation
Hassan, M. A., Habib, M. R., Abul Seoud, R. A., and Bayoumi, A. M. (December 22, 2017). "Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems." ASME. J. Dyn. Sys., Meas., Control. June 2018; 140(6): 061009. https://doi.org/10.1115/1.4038243
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