Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

作者：

Highlights：

• Novel approach combining permutation entropy and MANFIS to diagnose bearing faults.

• The approach is automated and its performance is not sensitive to imbalanced data.

• The approach allows automatic selection of defect frequency bands.

• The approach combines higher accuracy with more efficient implementation compared to other methods.

摘要

•Novel approach combining permutation entropy and MANFIS to diagnose bearing faults.•The approach is automated and its performance is not sensitive to imbalanced data.•The approach allows automatic selection of defect frequency bands.•The approach combines higher accuracy with more efficient implementation compared to other methods.

论文关键词：Rolling element bearing,Fault diagnosis,Permutation entropy,Wavelet transform,Multi output adaptive neuro-fuzzy inference system

论文评审过程：Received 16 February 2021, Revised 9 November 2021, Accepted 2 June 2022, Available online 11 June 2022, Version of Record 22 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117754