Applications of machine learning to machine fault diagnosis: A review and roadmap. (April 2020)

• Record Type: Journal Article
• Authors: Lei, Yaguo; Yang, Bin; Jiang, Xinwei; Jia, Feng; Li, Naipeng; Nandi, Asoke K.
• Is Part Of: Mechanical systems and signal processing. Volume 138(2020)
• Publication Date: 2020-04
• Subjects: Machines -- Intelligent fault diagnosis -- Machine learning -- Deep learning -- Transfer learning -- Review and roadmap; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.106587
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Applications of machine learning to machine fault diagnosis are reviewed. Traditional machine learning brought intelligence to fault diagnosis in the past. Deep learning focuses on further enhanced benefits in the present. Transfer learning promotes achievements to engineering scenarios in the future. A roadmap of intelligent fault diagnosis is pictured to provide research trends. Abstract: Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines, thus it has attracted much attention in the last two or three decades. Although IFD has achieved a considerable number of successes, a review still leaves a blank space to systematically cover the development of IFD from the cradle to the bloom, and rarely provides potential guidelines for the future development. To bridge the gap, this article presents a review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective. In the past, traditional machine learning theories began to weak the contribution of human labor and brought the era of artificial intelligence to machine fault diagnosis. Over the recent years, the advent of deep learning theories has reformed IFD in further releasing the artificial assistance since the 2010s, which encourages to construct an end-to-end diagnosis procedure. It means to directly bridge the relationship between the increasingly-grown monitoring data and the health states of machines. In the future, transfer learning theories attempt to use the diagnosis knowledge from one or multiple diagnosis tasks to other related ones, which prospectively overcomes the obstacles in applications of IFD to engineering scenarios. Finally, the roadmap of IFD is pictured to show potential research trends when combined with the challenges in this field.