University of Guilan Computational Sciences and Engineering 2783-2503 2 2 2022 09 01 Data Fusion Techniques for Fault Diagnosis of Industrial Machines: A Survey 239 250 6248 10.22124/cse.2023.23757.1040 EN Amir Eshaghi Chaleshtori School of Industrial engineering, K.N.Toosi University of Technology,Tehran,Iran Abdollah Aghaie Professor of industrial engineering, K.N. Toosi University of Technology, Tehran, Iran. Journal Article 2022 12 28 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. https://cse.guilan.ac.ir/article_6248_5273e6eccf824dea35969880b007ea1b.pdf