Wanqing Song

Bio: Wanqing Song is an academic researcher from Shanghai University of Engineering Sciences. The author has contributed to research in topics: Fractional Brownian motion & Hurst exponent. The author has an hindex of 9, co-authored 32 publications receiving 401 citations. Previous affiliations of Wanqing Song include Shanghai University & Virginia Tech.

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Abstract: Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Abstract: A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm is put forward in this paper. The improved LMD is proposed based on the self-similarity of roller bearing vibration signal extending the right and left side of the original signal to suppress its edge effect. After decomposing the extended signal into a set of product functions (PFs), the PE is utilized to display the complexity of the PF component and extract the fault feature meanwhile. Then, the optimized K-means algorithm is used to cluster analysis as a new pattern recognition approach, which uses the probability density distribution (PDD) to identify the initial centroid selection and has the priority of recognition accuracy compared with the classic one. Finally, the experiment results show the proposed method is effectively to fault extraction and recognition for roller bearing.

The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review

Abstract: Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Renyi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.

Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection

Abstract: Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem. Meanwhile, effective feature extraction from the raw vibration signal is an important procedure for timely detection of mechanical health status and the assessment of fault recognition results. Therefore, to efficiently extract fault feature information and improve fault diagnosis accuracy, a novel fault diagnosis technique based on improved multiscale dispersion entropy (IMDE) and max-relevance min-redundancy (mRMR) is proposed in this paper. Firstly, the IMDE method is developed to capture multi-scale fault features from the collected original vibration signal, which can overcome the deficiencies of traditional multiscale entropy and improve the stability of the recently presented multiscale dispersion entropy (MDE). Then, the mRMR algorithm is utilized to select automatically the sensitive features from the candidate multi-scale features without any prior knowledge. Finally, the sensitive feature vector set after normalization treatment is inputted into the extreme learning machine (ELM) classifier to train the intelligent diagnosis model and provide fault diagnosis results. The validity of our proposed method is assessed through two experimental examples. The experimental results show that our proposed method works efficiently for identification of different fault conditions of mechanical components including rolling bearing and gearbox. Moreover, our proposed method gives better diagnosis results as compared to some existing approaches (e.g. MSE and MPE) when being utilized for fault condition classification. This research provides a new perspective for fault information extraction and fault classification of rotating machinery.

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Abstract: Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.