Abstract
The traditional multiscale entropy algorithm shows inconsistency because some points are ignored when the signal is coarsened. To solve this problem, this paper proposes an improved multiscale permutation entropy (IMSPE). Firstly, the fault signal is decomposed into several product functions (PF) by local mean decomposition (LMD). Secondly, IMSPE is proposed to extract fault features of product functions. IMSPE integrates the information of multiple coarse sequences and solves problems of entropy inconsistency. Finally, the proposed method based on LMD and IMSPE is applied into gear fault diagnosis system. The experiment shows the proposed method can distinguish different gear fault types with a higher accuracy than traditional methods.
Highlights
Gear box is an important mechanical part which is used to transfer power and movement
The improved multiscale permutation entropy (IMSPE) method can be effectively utilized to measure the complexity of gear and extract the key feature vector which contained in product functions (PF) components of local mean decomposition (LMD)
IMSPE can obtain more effective signal features to distinguish different fault types and extract gearbox fault information more accurately compared with multiscale sample entropy (MSE), multiscale fuzzy entropy (MFE) and multiscale permutation entropy (MPE)
Summary
Gear box is an important mechanical part which is used to transfer power and movement. Gao et al applied multiscale permutation entropy (MPE) and tensor nuclear norm canonical polybasic decomposition in the fault detection of gears [17] It shows this method can accurately identify the different faults of gear. The proposed IMSPE is improved from two aspects based on the MPE: the process of coarsening and the definition of entropy On this basis, the IMSPE method can be effectively utilized to measure the complexity of gear and extract the key feature vector which contained in PF components of LMD. (2) The proposed IMSPE solves the errors and fluctuations of the traditional multiscale entropy through the process of coarsening For this reason, IMSPE can obtain more effective signal features to distinguish different fault types and extract gearbox fault information more accurately compared with MSE, MFE and MPE.
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