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Acoustic Based Condition Monitoring

Shen, Chia-Hsuan
http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408

Abstract Details

2012, Doctor of Philosophy, University of Akron, Mechanical Engineering.
Acoustic/vibration signal has traditionally benefited the condition monitoring of machinery. It can be further implemented in other field of applications as long as patterns associated with the conditions can be established. The dissertation consists of two areas of study, namely the gearbox condition monitoring and heart sound based diagnosis. The gearbox in a helicopter is a critical component with little response time prior to the failure. Therefore constant monitoring is necessary to prevent catastrophes. The developments of indicative parameters for condition monitoring of the gearbox remains to be a research of interest. Approximately more than 90% of heart murmurs are diagnosed to be normal and can be effectively determined by cardiac auscultation alone. However, current cardiology practices have been heavily relying on the expensive imaging equipment. With ever increasing national medical cost, a more optimized use of the high tech equipment is necessary. From the different sources of the acoustics, the objectives of the present dissertation concerned the analysis, the development of feature/parameter extractions, and/or the development of a pattern classifier for condition monitoring. In the gearbox condition monitoring, the vibration signatures of different gear and bearing damage scenarios were used to develop potential indicative parameters to detect gearbox faults. In heart murmur diagnosis, the study introduced a modular approach to computer-aided auscultation (CAA), where an alternative murmur characterization based on their acoustic qualities could be used. The analysis, the numerical characterizations, and the classifications for different types of the acoustic quality of murmurs as well as the classifications of the innocent murmurs and the pathological murmurs were carried out. In each of the topics of interest, analysis was performed in the time domain, the frequency domain and the time-frequency domain to acquire insights into the nature of the acoustic patterns under different conditions. Techniques such as the Fourier Transform (FT), the Continuous Wavelet Transform (CWT), and the Wigner-Ville distribution (WVD) were used for analysis for different signal domains. Possible features were than extracted to classify different representing conditions. The types of parameters/features extracted include the FT based features, the CWT based features, the Discrete Wavelet Transform (DWT) based features, and the Singular Spectrum Analysis (SSA) based features. The suitable features were selected based on techniques such as the Receiver Operating Characteristic (ROC) curve analysis and the Sequential Floating Forward Selection (SFFS) algorithm. The pattern classifiers used in the present dissertation include the K-Nearest Neighbor (KNN) classifier and the Classification and Regression Trees (CART). In gearbox condition monitoring, time-frequency analysis based on the CWT was considered a better visual examination solution among other considered techniques. Parameters based on the frequency components associated with the operating conditions were developed for damage identifications. In heart murmur diagnosis, different heart murmur qualities were quantitatively characterized by four extracted parameters based on their frequency characters and signal structure features. The parameters were able to correlate with the hemodynamics and physiology of the heart. Using the ROC curve analysis and the KNN classifier, an overall average accuracy of 87% was achieved. By using Sequential Floating Forward Selection (SFFS) and CART classifier, the average classification performance of different murmur qualities of up to 92% could be achieved. 90% accuracy was achieved for innocent murmur classification.
Fred K. Choy, Dr. (Advisor)
Shengyong Wang, Dr. (Advisor)
Francis Loth, Dr. (Committee Member)
Xiaosheng Gao, Dr. (Committee Member)
Dale Mugler, Dr. (Committee Member)
Kevin Kreider, Dr. (Committee Member)
Mechanical Engineering
gearbox health monitoring; heart sound analysis; wavelet transform; signular spectrum analysis; psychoacoustics; pattern recognitionl; heart failure monitoring

Recommended Citations

Citations

  • Shen, C.-H. (2012). Acoustic Based Condition Monitoring [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408

    APA Style (7th edition)

  • Shen, Chia-Hsuan. Acoustic Based Condition Monitoring. 2012. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408.

    MLA Style (8th edition)

  • Shen, Chia-Hsuan. "Acoustic Based Condition Monitoring." Doctoral dissertation, University of Akron, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408

    Chicago Manual of Style (17th edition)

akron1341797408
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© 2012, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.