Work

Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery

Year: 2019

Type: article

Abstract: Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed.

Source: IEEE Access

DOI: 10.1109/access.2019.2963092

Authors: Shengnan Tang, Shouqi Yuan, Yong Zhu

Institutions: Jiangsu University, State Key Laboratory Fluid Power and Mechatronic Systems, Zhejiang University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,750