The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review <s> 2) COMPOSITE MULTISCALE PERMUTATION ENTROPY (CMPE) <s> A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings. <s> BIB001 </s> The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review <s> 2) COMPOSITE MULTISCALE PERMUTATION ENTROPY (CMPE) <s> Abstract Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes. <s> BIB002
Like CMSE and CMFE, the composite multi-scale permutation entropy (CMPE) is also developed to overcome the shortcomings of MPE. Because PE has better performance in the fault feature extraction and high calculation efficiency, CMPE also show outstanding merits in the fault diagnosis of rotating machinery. Tang et al. BIB001 proposed a bearing fault diagnosis method based on CMPE and Dual Tree Complex Wavelet Packet Transform, the final classification accuracy using CWRU data can achieve 98.79%. Li et al. BIB002 developed a bearing fault diagnosis method based on EEMD and CMPE and a satisfactory classification result with 98.79% can be achieved using CWRU experimental data.