State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area.

[1]  John Ahmet Erkoyuncu,et al.  A design framework for adaptive digital twins , 2020, CIRP Annals.

[2]  Shan Gai,et al.  New banknote defect detection algorithm using quaternion wavelet transform , 2016, Neurocomputing.

[3]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[4]  Günther Schuh,et al.  Increasing data integrity for improving decision making in production planning and control , 2017 .

[5]  Yong Zhang,et al.  Degradation assessment of bearings with trend-reconstruct-based features selection and gated recurrent unit network , 2020 .

[6]  Giacomo Boracchi,et al.  Defect Detection in SEM Images of Nanofibrous Materials , 2017, IEEE Transactions on Industrial Informatics.

[7]  Thomas Parisini,et al.  Distributed Fault Detection for Interconnected Large-Scale Systems: A Scalable Plug & Play Approach , 2019, IEEE Transactions on Control of Network Systems.

[8]  Du-Ming Tsai,et al.  Defect Detection in Electronic Surfaces Using Template-Based Fourier Image Reconstruction , 2019, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[9]  Michel Verhaegen,et al.  Fault Estimation Filter Design With Guaranteed Stability Using Markov Parameters , 2017, IEEE Transactions on Automatic Control.

[10]  Tao Huang,et al.  Online Monitoring Machining Errors of Thin-Walled Workpiece: A Knowledge Embedded Sparse Bayesian Regression Approach , 2019, IEEE/ASME Transactions on Mechatronics.

[11]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[12]  Hong Pei,et al.  Review of Machine Learning Based Remaining Useful Life Prediction Methods for Equipment , 2019, Journal of Mechanical Engineering.

[13]  Beitong Zhou,et al.  Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network , 2019, Applied Energy.

[14]  Fuad E. Alsaadi,et al.  Detection of intermittent faults for nonuniformly sampled multi-rate systems with dynamic quantisation and missing measurements , 2018, Int. J. Control.

[15]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[16]  Jianbo Yu,et al.  Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment , 2012, IEEE Transactions on Industrial Electronics.

[17]  Thyago P. Carvalho,et al.  A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..

[18]  Ling Wang,et al.  Crack detection in magnetic tile images using nonsubsampled shearlet transform and envelope gray level gradient , 2017 .

[19]  Qingjin Peng,et al.  Dependency and correlation analysis of specifications and parameters of products for supporting design decisions , 2020 .

[20]  Yong Zhang,et al.  A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method , 2019, Measurement.

[21]  Philip A. Scarf,et al.  A review on maintenance optimization , 2020, Eur. J. Oper. Res..

[22]  Yong Zhang,et al.  A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series , 2020, Neurocomputing.

[23]  Umezuruike Linus Opara,et al.  Machine learning applications to non-destructive defect detection in horticultural products , 2020 .

[24]  Ruqiang Yan,et al.  Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.

[25]  Alf J. Isaksson,et al.  The Autonomous Industrial Plant -Future of Process Engineering, Operations and Maintenance , 2019, IFAC-PapersOnLine.

[26]  Kaixiang Peng,et al.  A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter , 2019, Neurocomputing.

[27]  Xiujuan Zheng,et al.  Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation , 2020, Neurocomputing.

[28]  Shijun Liu,et al.  A fast shapelet selection algorithm for time series classification , 2019, Comput. Networks.

[29]  Yibin Huang,et al.  Surface defect saliency of magnetic tile , 2018, The Visual Computer.

[30]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[31]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[32]  Ying Zheng,et al.  Normalized Relative RBC-Based Minimum Risk Bayesian Decision Approach for Fault Diagnosis of Industrial Process , 2016, IEEE Transactions on Industrial Electronics.

[33]  Domingo Mery,et al.  GDXray: The Database of X-ray Images for Nondestructive Testing , 2015, Journal of Nondestructive Evaluation.

[34]  Fei Teng,et al.  A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings , 2020, IEEE/ASME Transactions on Mechatronics.

[35]  Hironobu Fujiyoshi,et al.  Attention Branch Network: Learning of Attention Mechanism for Visual Explanation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Ye Yuan,et al.  High Precision Variational Bayesian Inference of Sparse Linear Networks , 2019, Autom..

[37]  Martin Kampel,et al.  A dataset for computer-vision-based PCB analysis , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[38]  Peigen Li,et al.  Toward New-Generation Intelligent Manufacturing , 2018 .

[39]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[40]  Cheng Cheng,et al.  Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression , 2020, Neurocomputing.

[41]  Vincent Christlein,et al.  Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized Lp Norm , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[42]  Walter Sextro,et al.  Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification , 2016, PHM Society European Conference.

[43]  Brigitte Chebel-Morello,et al.  PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .

[44]  Jinjiang Wang,et al.  Machine vision intelligence for product defect inspection based on deep learning and Hough transform , 2019, Journal of Manufacturing Systems.

[45]  Hugh Chen,et al.  From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.

[46]  Jiong Tang,et al.  Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning , 2017, IEEE Access.

[47]  Yunhui Yan,et al.  A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects , 2013 .

[48]  Jay Lee,et al.  Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.

[49]  Wei Zhou,et al.  Data driven discovery of cyber physical systems , 2018, Nature Communications.

[50]  Ming Yin,et al.  A novel surface defect inspection algorithm for magnetic tile , 2016 .

[51]  Ziyang Meng,et al.  A survey of distributed optimization , 2019, Annu. Rev. Control..

[52]  N. Asokan,et al.  Making Targeted Black-box Evasion Attacks Effective and Efficient , 2019, AISec@CCS.

[53]  Cheng Cheng,et al.  A general end-to-end diagnosis framework for manufacturing systems , 2019 .

[54]  Fuad E. Alsaadi,et al.  Annulus-event-based fault detection, isolation and estimation for multirate time-varying systems: Applications to a three-tank system , 2019, Journal of Process Control.