Gearbox degradation assessment based on a sparse representation feature and Euclidean distance technique

ABSTRACT

Gearboxes have developed rapidly, and studies on gearbox degradation assessment are of great significance. In the interest of obtaining an effective gearbox degradation feature, a new methodology based on sparse representation and compressive sensing theory is proposed. The wavelet packet transform is an excellent time-frequency analysis method, and the subbands with the largest energy are selected for training the sparse dictionaries. Furthermore, the overcomplete dictionaries of each degradation state can well reflect the signal structure by calculating the compressive sensing reconstruction residuals. Based on the sparse representation feature, a Euclidean distance technique is performed to calculate the assessment rate for each degradation state. The validity and superiority of the proposed assessment method are validated by crack failure data and broken tooth failure data under three kinds of work loads. A comparison of the sparse representation feature with the features of wavelet packet energy, kurtosis, and Renyi entropy is also performed.

KEYWORDS:

• Sparse representation
• compressive sensing
• degradation assessment
• feature extraction
• gearbox
• Euclidean distance technology

Nomenclature

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Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Yunfei Ma

Yunfei Ma received M.Sc. degree from Army Engineering University, Shijiazhuang, China, in 2017. Now he is a Ph.D. candidate in Army Engineering University.‎His current research interests include Compressive Sensing and mechanical fault diagnosis.

Xisheng Jia

Xisheng Jia received his Ph.D. degree from University of Salford, United Kingdom, in 2001. Now he is a Professor and Ph. D supervisor in Army Engineering University. His main research interests include Reliability Centered Maintenance (RCM), Prognostic and Health Management (PHM).

Huajun Bai

Huajun Bai received his M.Sc. degree from Beijing Jiaotong University, Beijing,China. Now he is a Ph.D. candidate in Army Engineering University.‎ His current research interests include wireless sensor network and Industrial Engineering.