Improved broad learning system for machinery intelligent fault diagnosis with increasing fault samples, fault modes, and running conditions

Yang Fu; Hongrui Cao; Xuefeng Chen; Jianming Ding

doi:10.1016/j.isatra.2022.10.014

Improved broad learning system for machinery intelligent fault diagnosis with increasing fault samples, fault modes, and running conditions

ISA Trans. 2023 May:136:400-416. doi: 10.1016/j.isatra.2022.10.014. Epub 2022 Oct 22.

Authors

Yang Fu¹, Hongrui Cao², Xuefeng Chen¹, Jianming Ding³

Affiliations

¹ State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
² State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: chr@mail.xjtu.edu.cn.
³ State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China.

PMID: 36336475
DOI: 10.1016/j.isatra.2022.10.014

Abstract

Intelligent fault diagnosis (IFD) plays an indispensable role in protecting machinery from catastrophic accidents. Existing IFD methods are mainly developed in the framework of one-time learning. Therefore, they work under the hypothesis of complete dataset. Nevertheless, it is unrealistic to gain the complete dataset of machinery faults at once. More practically, new data will be progressively acquired over time. Therefore, it is urgently required to develop the incremental learning (IL) capabilities for IFD models to learn new knowledge continually from new data. For this purpose, this study proposes an improved broad learning system (IBLS) for lifelong learning IFD. Firstly, the initial IBLS is constructed based on the original broad learning system (BLS). Then, the IL capabilities of the IBLS are developed for three scenarios: increasing fault samples, increasing fault modes, and increasing running conditions. Based on these IL capabilities, the IBLS can be progressively updated to learn more and more diagnosis functions. Finally, the effectiveness of the proposed IBLS is verified using three experiments of high-speed train bearing, disc component, and Case Western Reserve University bearing. The results show that the IBLS is capable of learning continually new knowledge from new data. Besides, the diagnosis accuracy of the IBLS is 12.45%, 7.84%, and 5.10% higher than that of the original BLS in the three case studies. The satisfying results prove that the proposed IBLS is a useful method to solve the lifelong learning IFD problem.

Keywords: Broad learning system (BLS); Incremental learning (IL); Intelligent fault diagnosis (IFD); Lifelong learning.