A comprehensive working condition identification scheme for rolling bearings based on modified CEEMDAN as well as modified hierarchical amplitude-aware permutation entropy. (1st July 2022)

• Record Type: Journal Article
• Authors: Shu, Ling; Deng, Hongbin; Liu, Xiaoming; Pan, Zhenhua
• Is Part Of: Measurement science & technology. Volume 33:Number 7(2022)
• Publication Date: 2022-07-01
• Subjects: MCEEMDAN -- modified hierarchical amplitude-aware permutation entropy -- multi cluster feature selection -- rolling bearing -- comprehensive working condition detection; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac5b2c
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: As a pivotal part of a machine driven system, the health states of rolling bearings usually determine the normal operation of a whole item of equipment. Consequently, it is important to make accurate and timely judgments as to the operating conditions of rolling bearings. In this paper, a synthesized diagnosis technology, including fault pre-judgment and identification for rolling bearings is proposed. In the first section, a threshold value is defined on the basis of the sensitivity of amplitude-aware permutation entropy (AAPE) to bearing faults. Whether the bearing has defects is judged is based on this value. If a defect exists, a feature extraction scheme combining the modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and the modified hierarchical AAPE (MHAAPE) is adopted, to fully mine the hidden state features. Firstly, the scheme uses MCEEMDAN, which benefits from a good time-frequency decomposition capability, to divide the signal of trouble into a group of intrinsic mode functions (IMFs). Second, the MHAAPE of each IMF component is computed to form the candidate state features. Then, multi cluster feature selection is employed to compress the high-dimensional fault features to form the low-dimensional sensitive feature vectors required for subsequent classification. Finally, the sensitive feature vectors are input into a random forest classifier for training and classification, so as to ascertain the different fault types and severity. In addition, different contrastive methods are tested based on experimental data. The experiment results indicate that, compared to contrastive methods, the proposed scheme enjoys better performance, which can effectively judge whether the bearing is healthy and accurately identify different fault states in bearings.