Details

Autor(en) / Beteiligte

• Yang, Yuantao
• Zheng, Huailiang
• Yin, Jiancheng
• Xu, Minqiang
• Chen, Yushu

Titel

Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine

Ist Teil von

• Measurement : journal of the International Measurement Confederation, 2020-02, Vol.151, p.107233, Article 107233

Ort / Verlag

London: Elsevier Ltd

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •RCmvMSDE is proposed to extract features for fault diagnosis.•The method efficiently extracts discriminative features from multivariate signals.•The stability and reliability of the RCmvMSDE are verified by Synthetic signals.•Two diagnosis case studies show the effectiveness and superiority of RCmvMSDE. Accurate and efficient identification of various fault categories, especially for the big data and multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive multivariate data, extracting discriminative and stable features with high efficiency is the significant step. This paper proposes a novel feature extraction method, called Refined Composite multivariate Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic entropy can capture more identification information from multiple sensors with superior computational efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method to measure the complexity of multivariate time series and identify the signals with different components are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball bearing dataset under time-varying speed condition are applied. Compared with the existing methods, the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively. Meanwhile, the proposed method saves at least half of the computational time. The result shows that the proposed method is effective to improve the efficiency and classification accuracy dealing with the massive multivariate signals.