Title
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
Abstract
With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment). Moreover, because the existing PdM research is still in primary experimental stage, most works are conducted utilizing several open-datasets, and the combination with specific applications such as rotating machinery is especially rare. Hence, in this paper, we focus on data-driven methods for PdM, present a comprehensive survey on its applications, and attempt to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published. Specifically, we first briefly introduce the PdM approach, illustrate our PdM scheme for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic washing equipment</italic> , and demonstrate the challenges encountered when we conduct a PdM research. Second, we classify the specific industrial applications based on six algorithms of machine learning and deep learning (DL), and compare five performance metrics for each classification. Furthermore, the accuracy (a metric to evaluate the algorithm performance) of these PdM applications is analyzed in detail. There are some important conclusions: 1) the data used in the summarized literature are mostly from public datasets, such as case western reserve university (CWRU)/intelligent maintenance systems (IMS); and 2) in recent years, researchers seem to focus more on DL algorithms for PdM research. Finally, we summarize the common features regarding our surveyed PdM applications and discuss several potential directions.
Year
DOI
Venue
2019
10.1109/jsyst.2019.2905565
IEEE Systems Journal
Keywords
Field
DocType
Predictive maintenance,Prognostics and health management,Predictive models,Data models,Fault diagnosis,Machine learning algorithms
Data science,Data modeling,Smart manufacturing,Data-driven,Prognostics,Computer science,Real-time computing,Industrial equipment,Artificial intelligence,Deep learning,Predictive maintenance,Big data
Journal
Volume
Issue
ISSN
13
3
1932-8184
Citations 
PageRank 
References 
5
0.46
0
Authors
3
Name
Order
Citations
PageRank
Zhang Wei139253.03
Dong Yang221120.38
Hongchao Wang3356.19