Artificial intelligence for fault diagnosis of rotating machinery: A review

By Ruonan Liu, Boyuan Yang, Enrico Zio, Xuefeng Chen. Mechanical systems and signal processing, 2018

1,920 citations recorded by the source corpus.

Abstract Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/3affddc9f3f913019d9c94ceecbe83f5383aae5b

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In Control and Systems Engineering

In Mechanics of Materials

Citations in the corpus

Deep Residual Shrinkage Networks for Fault Diagnosis (2020, background): “As a consequence, finding a discriminative feature set has become a long-standing challenge for machine learning-powered fault diagnosis [5].”
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “[57] reviewed the applications of artificial intelligence algorithms in rotating machinery fault diagnosis, mainly focusing on conventional machine learning methods.”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “, to parse the data, learn from them, and apply what they have learned to make intelligent decisions regarding the presence of bearing faults [18]–[21].”
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, background): “The ability of deep neural networks to learn low-level and high-level features from abundant datasets has been well known and has been widely exploited in fault diagnosis [5], [6].”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “The high efficiency of vibration-based SDD systems via SHM of rotating machines [35] is based on the fact that the vibration response of such machines is minimally affected by the operational and environmental conditions [36–38].”
An Overview of Artificial Intelligence Applications for Power Electronics (2020, background): “Moreconsiderations shouldbedevotedtothegapbetweentheoreticalalgorithmsand practical implementations, where the practical considerations include the following.”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “…learning algorithms, i.e., artiﬁcial neural networks (ANN), principal component analysis (PCA), support vector machines (SVM), etc., to better parse the data, learn from them, and then apply what they’ve learned to make intelligent decisions regarding the presence of bearing faults [30]–[33].”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “[21] paid more attention to traditional ML algorithms.”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[98], and the success of the ML techniques is largely attributed to the good understanding of certain machine and process based on which the physics-informed features from data, such as those in time, frequency, and time-frequency domain, can be effectively extracted [11].”
A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, methodology): “In generally, these machine learning methods are combined with the feature extraction processes [11].”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “For instance, Liu et al. [16] summarized five artificial intelligent algorithms for fault diagnosis of rotating machinery.”
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “[16] reviewed Al-based approaches including KNN, SVM, ANN, Naive Bayes, and DL for fault diagnosis of rotating machinery.”