A review on the application of deep learning in system health management

By Samir Khan, Takehisa Yairi. 2018

1,005 citations recorded by the source corpus.

Abstract Given the advancements in modern technological capabilities, having an integrated health management and diagnostic strategy becomes an important part of a system’s operational life-cycle. This is because it can be used to detect anomalies, analyse failures and predict the future state based on up-to-date information. By utilising condition data and on-site feedback, data models can be trained using machine learning and statistical concepts. Once trained, the logic for data processing can be embedded on on-board controllers whilst enabling real-time health assessment and analysis. However, this integration inevitably faces several difficulties and challenges for the community; indicating the need for novel approaches to address this vexing issue. Deep learning has gained increasing attention due to its potential advantages with data classification and feature extraction problems. It is an evolving research area with diverse application domains and hence its use for system health management applications must been researched if it can be used to increase overall system resilience or potential cost benefits for maintenance, repair, and overhaul activities. This article presents a systematic review of artificial intelligence based system health management with an emphasis on recent trends of deep learning within the field. Various architectures and related theories are discussed to clarify its potential. Based on the reviewed work, deep learning demonstrates plausible benefits for fault diagnosis and prognostics. However, there are a number of limitations that hinder its widespread adoption and require further development. Attention is paid to overcoming these challenges, with future opportunities being enumerated.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

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Citations in the corpus

A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, neutral): “methods were reported [24], [25].”
Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, neutral): “Yet bridge SHM has much more complexities than the other two fields where both BD and AI techniques have acquired many applications (Khan and Yairi 2018; Yao et al. 2018).”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “To achieve an even better performance at versatile operating conditions and noisy environments, deep learning (DL) based methods are becoming increasingly popular to meet this demand [22]–[25].”
Deep Learning for Anomaly Detection: A Survey (2019, background): “Several studies have been conducted to understand the theoretical and practical applications of deep learning in medical and bio-informatics (Min et al. [2017], Cao et al. [2018a], Zhao et al. [2016], Khan and Yairi [2018]).”
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, background): “In addition to these different domains of application, researchers have explored different ML models, for example, support vector machine/regression [67], Gaussian Process Regression (GPR) or kriging [69], and Deep Learning for structural reliability problems, RUL prediction, fire hazard simulation,”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “To achieve an even better performance at versatile operating conditions and noisy environments, deep learning (DL) based methods are becoming increasingly popular to meet this demand [22]–[24].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “Applications of DL to PHM are wellreviewed in Khan and Yairi (2018), Zhao et al.”
Fusing Physics-based and Deep Learning Models for Prognostics (2020, background): “In particular, deep learning has recently gained attention due to its ability to learn failure patterns directly from raw sensor data [4].”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “Recent investigations have shown that ML approaches are more meticulous and superior than the traditional rule-based methods especially in dealing with problems where the data is fuzzy [55], vague, or noise-contaminated [56].”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “Khan and Yairi 32 used a different approach to review DL research on system health management.”