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RISS 인기검색어
- 검색키워드
- 저자 : Minping Jia (검색결과 3 건)
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Weighted sparsity-based denoising for extracting incipient fault in rolling bearing
Wan Zhang,Minping Jia,Xiaoan Yan,Lin Zhu 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.10
Given that the incipient fault is too weak for extraction, a novel approach that is based on sparse optimization is proposed for incipient fault diagnosis. The proposed optimization method consists of three steps: First, autocorrelation analysis is utilized to filter broadband random noise. Then, the weighted sparsity-based denoising method is proposed to extract periodic impulses. The prior knowledge that periodic impulses are sparse is used to constitute a penalty term; thus a novel weighted sparse optimization model is established. The majorization-minimization method is used to solve the optimization model. The high-pass filter in quadratic fidelity term is constructed by a Butterworth filter based on banded matrices, thus effectively improving computational efficiency. Lastly, the interval of periodic impulses, which corresponds to the fault frequency of rolling bearing, is obtained. Moreover, simulation and experimental results show that the proposed approach can successfully extract fault features from the signals of low signal to noise ratio.
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Research on sparsity measures for rotating machinery health monitoring
Yudong Cao,Minping Jia,Jichao Zhuang,Xiaoli Zhao 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
Machine health management is one of the main research contents of PHM technology, which aims to monitor the health states of machines online and evaluate degradation stages through real-time sensor data. In recent years, classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness index, negative entropy, and Gini index have been widely used to characterize the impulsivity of repetitive transients. Since smoothness index and negative entropy were proposed, the sparse properties have not been fully analyzed. The first contribution of this paper is to analyze six properties of smoothness index and negative entropy. In addition, this paper conducts a thorough investigation on multivariate power average function and finds that existing classical sparsity measures can be respectively reformulated as the ratio of multivariate power mean functions (MPMFs). Finally, a general paradigm of index design is proposed for the expansion of sparsity measures family, and several newly designed dimensionless health indexes are given as examples. Two different run-to-failure bearing datasets were used to analyze and validate the capabilities and advantages of the newly designed health indexes. Experimental results prove that the newly designed health indexes show good performance in terms of monotonic degradation description, first fault occurrence time determination and degradation state assessment.
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Zhongyuan Su,Yaoming Zhang,Minping Jia,Feiyun Xu,Jianzhong Hu 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.2
An improved singular value decomposition method of gear fault identification based on Hilbert-Huang transform was proposed to overcome the problem of reconstructing a feature matrix of singular value decomposition. The method includes three steps. First, the instantaneous frequency and amplitude matrices were acquired by Hilbert-Huang transform from faulted gear signals. Second, after the matrices were decomposed by singular value decomposition, the defined distances of singular value vectors and the optimal threshold of the distance for classification were calculated. Third, the fault characteristics of a gearbox were identified and classified by the threshold of the distances. The result demonstrates that the proposed method effectively identifies the gear fault and can realize an automatic gear fault diagnosis.
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