Amir Eshaghi Chaleshtori - ph.D. student of industrial engineering, K.N.Toosi University of Technology, Iran, Tehran
Abdollah aghaie - Professor of industrial engineering, K.N.Toosi University of Technology, Iran

In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented

Data fusion, predictive maintenance, fault diagnosis, fault prognosis, industrial machines, data mining