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AI-Enabled Monitoring, Diagnosis & Prognosis

The emerging and development of Artificial Intelligence (AI), especially deep learning, has stimulated its application in various engineering domains. Monitoring, diagnosis and prognosis, as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0, has also benefited from the advancement of AI technology. The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring, diagnosis and prognosis driven by AI, and promote its application in intelligent maintenance of manufacturing system in China. Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.

The first paper written by Yan’s group provided a systematic overview of the current technologies and research challenges of AI-enabled monitoring, diagnosis, and prognosis. In this paper, open source datasets and codes are also provided so that readers can use them as a starting point for further development. When adopting a deep learning model for a given task, the effect of different network structures and parameters on the model performance should be considered. Zhao et al. reviewed recurrent neural network model and its variants for remaining useful life prediction, with a specific focus on performance comparison study caused by optimisers, activation functions, neuron quantities and input sequence length. Data quality and model interpretability are two important factors for wide application of AI, Zhang and Gao summarized several key techniques related to data quality where advancement in data denoising, outlier detection, imputation, balancing, and semantic annotation were reviewed from the perspective of effective information extraction from measurement data. Also the model interpretation methods that focus on model transparency were highlighted.

Bearings and gears in rotating machinery often generate various faults due to severe work conditions. Timely diagnosing those faults, and, if possible, predicting their remaining useful life can ensure safe production with high quality products in modern manufacturing industry. Ahmed and Nandi proposed a convolutional neural network (CNN)-based bearing fault diagnosis approach using connected components-based image representations with sets of colours and texture features of vibration signals. Li et al. presented a dynamic distribution adaptation based transfer network (DDATN), where instance-weighted dynamic maximum mean discrepancy (IDMMD) was proposed for dynamic distribution adaptation (DDA), so that cross domain bearing fault diagnosis can be achieved. Cheng et al. developed a novel interpretable denoising layer, based on reproducing kernel Hilbert space (RKHS), as the first layer of a standard neural network model. This enabled physical interpretation of the network model with parameter adaption for better bearing and gears fault diagnosis. In another study, Wang et al. studied a novel bearing prognostics approach by using time–frequency representation (TFR) subsequence as input to a three-dimensional CNN model for health indicator construction, and Gaussian process regression (GPR) for subsequent RUL estimation.

Prediction of tool wear is also one of typical applications in modern industry for enabling high quality product manufacturing. Xu et al. developed a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) model, where six parallel and independent branches with different kernel sizes were designed to form a multi-scale CNN for adaptive features learning at different time scales, to process raw data for effective tool wear prediction. In some applications, there is an increasing demand for not only diagnosing the known type of fault, but also detecting unknown types of faults. To achieve this goal, Yang et al. presented a sparse autoencoder-based multi-head deep neural network (DNN) model to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data, thus enabling both bearing and belt fault diagnosis with detection of some unknow faults. In other cases, sensors themselves may also produce fault signals, which cause risk for a monitored system. Gao et al. developed a deep learning based data fusion method for sensor fault diagnosis, which can improve reliability of the system.

From the papers published in this Special Issue, we hope they will benefit the intelligence maintenance research community.

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Ruqiang Yan (Senior Member, IEEE) received his Ph.D. degree in mechanical engineering from the University of Massachusetts, USA, in 2007. From 2009 to 2018, he was a Professor with the School of Instrument Science and Engineering, Southeast University, China. He joined the School of Mechanical Engineering, Xi’an Jiaotong University, China, in 2018. His research interests include data analytics, machine learning, and energy-efficient sensing, and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Dr. Yan is a Fellow of American Society of Mechanical Engineers (2019). His was the recipient of several awards and honors, including the IEEE Instrumentation and Measurement Society Technical Award in 2019, New Century Excellent Talents in University Award from the Ministry of Education in China in 2009, and multiple best paper awards. He is an Associate Editor-in-Chief for the IEEE Transactions on Instrumentation and Measurement and an Associate Editor for the IEEE Systems Journal.

Xuefeng Chen (Senior Member, IEEE) received his Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, China, in 2004. He is currently a Full Professor and the Dean of School of Mechanical Engineering, Xi’an Jiaotong University, China. He has authored more than 100 SCI publications in areas of composite structure, aeroengine, wind power equipment, etc. Dr. Chen was the recipient of the National Excellent Doctoral Thesis Award in 2007, First Technological Invention Award of Ministry of Education in 2008, Second National Technological Invention Award in 2009, First Provincial Teaching Achievement Award in 2013, First Technological Invention Award of Ministry of Education in 2015, and Science and Technology Award for Chinese Youth in 2013. He hosted a National Key 973 Research Program of China as a Principal Scientist in 2015. He is the Executive Director of the Fault Diagnosis Branch in China Mechanical Engineering Society.

Weihua Li, born in 1973, is currently a professor at School of Mechanical & Automotive Engineering, South China University of Technology, China. His main research interests include nonlinear time series analysis, dynamic signal processing, autonomous driving, and machine learning methods for condition monitoring and health diagnosis of complex dynamical systems.

Robert X. Gao is the Cady Staley Professor of Engineering and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University, Cleveland, OH, USA. Since receiving his Ph.D. degree from the Technical University of Berlin, Germany, in 1991, he has been working in the areas of signal transduction mechanisms for process-embedded sensing, multiresolution data analysis, stochastic modeling, and physics-informed machine learning for improving the observability of cyber physical systems, with applications in manufacturing.

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Correspondence to Ruqiang Yan.

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Yan, R., Chen, X., Li, W. et al. AI-Enabled Monitoring, Diagnosis & Prognosis. Chin. J. Mech. Eng. 34, 68 (2021). https://doi.org/10.1186/s10033-021-00589-w

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