Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Abstract

When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

Research Information Security Technology Based on Machine Learning

“…The focus of situation awareness is situation assessment [11]. The situation assessment system can timely find hidden dangers and threats in various situation elements, generate situation values that can accurately evaluate the network security status, and then predict the change trend of the network security status in a long period of time, so that network managers can timely master the network security situation [12]. For possible future security events, we should build protective measures and cut off attack sources as soon as possible, so as to ensure the normal operation of the network and reduce unnecessary economic losses [13].…”

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

“…To summarize the current research of intelligent FDP, there are a number of outstanding surveys on the topic of intelligent FDP [ 1 , 7 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. They conduct extensive review on existing literature quantitatively and qualitatively from their unique viewpoints, and identify the trends and ideas of FDP methods for different scenarios.…”

“…A comprehensive review of Big Data-driven intelligent FDP for mechanical systems was given by Lei et al [ 28 ], wherein the latest cutting-edge research results are focused, e.g., deep transfer learning-based FD, Big Data-driven RUL prediction, data-model fusion prognosis, etc. In addition, Fernandes et al [ 20 ] provided a systematic literature review of ML methods for mechanical FDP in manufacturing. They examined and characterized the research in more details based on five basic research questions.…”

“…The aforementioned review work provides a very good foundation for the work in this paper. Some surveys concentrate on FDP for specific type of device, e.g., machinery [ 20 , 21 , 22 , 23 , 24 , 27 , 28 ], wind power converter [ 25 ], lithium-ion battery system [ 26 ], while some focus on specific FDP method, e.g., deep domain adaptation [ 21 ], attention mechanism [ 22 ], recurrent neural network (RNN) [ 23 ], etc. Most of these reviews cover the data-driven ML techniques, but few of them give a comprehensive overview of the generic DL techniques used for industrial FDP.…”

A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data

“…With the Industrial Internet of Things (IIoT), a massive amount of collected data is used to forecast production tools' aging or failures by Machine Learning (ML) techniques. Some examples are the detection of equipment failure, supply chain optimization, performance monitoring, and predictive maintenance [1]. IIoT is an infrastructure of software and hardware that connects the physical world of the company processes with the Internet [2].…”