Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

Research, the universal pursuit of new knowledge, is embarking on a fresh journey into artificial intelligence (AI). Nature reports that AI arose nine places to the fourth-most popular search term and that search terms machine learning and deep learning appeared in the top 20 for the first time since 2018. It is pertinent for industrial informatics to embrace this renewed surge of interest in AI with a clear direction and purpose that engages scholars, practitioners, and professionals alike.

[1]  Bereket Tanju,et al.  A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations , 2017, IEEE Internet of Things Journal.

[2]  Heiko Wersing,et al.  Incremental on-line learning: A review and comparison of state of the art algorithms , 2018, Neurocomputing.

[3]  Paulo Leitão,et al.  Past, Present, and Future of Industrial Agent Applications , 2013, IEEE Transactions on Industrial Informatics.

[4]  Tianyou Chai,et al.  Performance-Based Adaptive Fuzzy Tracking Control for Networked Industrial Processes , 2016, IEEE Transactions on Cybernetics.

[5]  Yacine Rezgui,et al.  An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings , 2017, IEEE Transactions on Automation Science and Engineering.

[6]  Fiorella Lauro,et al.  Fault detection analysis using data mining techniques for a cluster of smart office buildings , 2015, Expert Syst. Appl..

[7]  Allen Newell,et al.  A VARIETY OF INTELLIGENT LEARNING IN A GENERAL PROBLEM SOLVER , 2012 .

[8]  Kilian Wasmer,et al.  Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission , 2019, IEEE Transactions on Industrial Informatics.

[9]  Pablo Basanta-Val,et al.  An Efficient Industrial Big-Data Engine , 2018, IEEE Transactions on Industrial Informatics.

[10]  Mianxiong Dong,et al.  When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid , 2017, IEEE Communications Magazine.

[11]  Dong Yue,et al.  Multi-View Stacking Ensemble for Power Consumption Anomaly Detection in the Context of Industrial Internet of Things , 2018, IEEE Access.

[12]  Jiafu Wan,et al.  A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks , 2019, IEEE Transactions on Industrial Informatics.

[13]  Victor C. M. Leung,et al.  Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Industrial Informatics.

[14]  Valeriy Vyatkin,et al.  Toward Industrially Usable Agent Technology for Smart Grid Automation , 2015, IEEE Transactions on Industrial Electronics.

[15]  Xinghuo Yu,et al.  Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading , 2018, Applied Energy.

[16]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[17]  Valeriy Vyatkin,et al.  Multi-agent deep learning for simultaneous optimization for time and energy in distributed routing system , 2019, Future Gener. Comput. Syst..

[18]  Evgeny Osipov,et al.  Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Joel J. P. C. Rodrigues,et al.  Effective Features to Classify Big Data Using Social Internet of Things , 2018, IEEE Access.

[21]  Andrew McCallum,et al.  Energy and Policy Considerations for Deep Learning in NLP , 2019, ACL.

[22]  Mohamed Saber Naceur,et al.  A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search , 2018, ArXiv.

[23]  Xinghuo Yu,et al.  Intelligent Detection of Driver Behavior Changes for Effective Coordination Between Autonomous and Human Driven Vehicles , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[24]  Ding Li,et al.  Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid , 2015, IEEE Systems Journal.

[25]  Xinghuo Yu,et al.  Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey , 2016, IEEE Transactions on Industrial Informatics.

[26]  Fuchun Sun,et al.  Active Object Detection With Multistep Action Prediction Using Deep Q-Network , 2019, IEEE Transactions on Industrial Informatics.

[27]  Ryutaro Ichise,et al.  Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices , 2018, Energies.

[28]  Pierluigi Siano,et al.  A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems , 2014, IEEE Transactions on Industrial Informatics.

[29]  Chin-Teng Lin,et al.  Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Shun-ichi Azuma,et al.  Secure Real-Time Control Through Fog Computation , 2019, IEEE Transactions on Industrial Informatics.

[31]  Ujjwal Maulik,et al.  Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach , 2015, IEEE Transactions on Industrial Informatics.

[32]  Fan Yang,et al.  Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes , 2018, IEEE Transactions on Industrial Electronics.

[33]  L. Belkhir,et al.  Assessing ICT global emissions footprint: Trends to 2040 & recommendations , 2018 .

[34]  Keemin Sohn,et al.  Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network , 2018, IEEE Transactions on Intelligent Transportation Systems.

[35]  Naveen K. Chilamkurti,et al.  Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments , 2018, Future Gener. Comput. Syst..

[36]  Fu Xiao,et al.  Data mining in building automation system for improving building operational performance , 2014 .

[37]  G. Reeke.  Marvin Minsky, The Society of Mind , 1991, Artif. Intell..

[38]  Xiaoming He,et al.  QoE-Driven Big Data Architecture for Smart City , 2018, IEEE Communications Magazine.

[39]  Rama Chellappa,et al.  Deep Multitask Learning for Railway Track Inspection , 2015, IEEE Transactions on Intelligent Transportation Systems.

[40]  Jun Wu,et al.  Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach , 2019, IEEE Transactions on Industrial Informatics.

[41]  Y. Ahmet Sekercioglu,et al.  Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[43]  Tingwen Huang,et al.  Generalized Predictive Control for Industrial Processes Based on Neuron Adaptive Splitting and Merging RBF Neural Network , 2019, IEEE Transactions on Industrial Electronics.

[44]  Yuan-Shin Lee,et al.  Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing , 2017 .

[45]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

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

[47]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[48]  Hai Jin,et al.  Predicting Transportation Carbon Emission with Urban Big Data , 2017, IEEE Transactions on Sustainable Computing.

[49]  Pethuru Raj,et al.  Precision centric framework for activity recognition using Dempster Shaffer theory and information fusion algorithm in smart environment , 2019, J. Intell. Fuzzy Syst..

[50]  V. Vyatkin,et al.  Multiagent Smart Grid Automation Architecture Based on IEC 61850/61499 Intelligent Logical Nodes , 2012, IEEE Transactions on Industrial Electronics.

[51]  Lingjuan Lyu,et al.  Fog-Embedded Deep Learning for the Internet of Things , 2019, IEEE Transactions on Industrial Informatics.

[52]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[53]  Valeriy Vyatkin,et al.  Task Allocation Algorithm for Energy Resources Providing Frequency Containment Reserves , 2019, IEEE Transactions on Industrial Informatics.

[54]  Jun Guo,et al.  The Role of Data Analysis in the Development of Intelligent Energy Networks , 2017, IEEE Network.

[55]  Daswin De Silva,et al.  Integer Self-Organizing Maps for Digital Hardware , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[56]  Yan Chen,et al.  Intelligent 5G: When Cellular Networks Meet Artificial Intelligence , 2017, IEEE Wireless Communications.

[57]  Valeriy Vyatkin,et al.  Distributed Software Architecture Enabling Peer-to-Peer Communicating Controllers , 2013, IEEE Transactions on Industrial Informatics.

[58]  Daswin De Silva,et al.  Incremental knowledge acquisition and self learning from text , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[59]  Zoran A. Salcic,et al.  Software Composition and Distributed Operation Scheduling in Modular Automated Machines , 2015, IEEE Transactions on Industrial Informatics.

[60]  Patrick Benavidez,et al.  Pedestrian detection system for smart communities using deep Convolutional Neural Networks , 2017, 2017 12th System of Systems Engineering Conference (SoSE).

[61]  Hamed Haddadi,et al.  Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning , 2018, Computer.

[62]  L. Floridi,et al.  A Unified Framework of Five Principles for AI in Society , 2019, Issue 1.

[63]  Jianbin Qiu,et al.  A Combined Fault-Tolerant and Predictive Control for Network-Based Industrial Processes , 2016, IEEE Transactions on Industrial Electronics.

[64]  Xinghuo Yu,et al.  A cognitive data stream mining technique for context-aware IoT systems , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[65]  Erees Queen B. Macabebe,et al.  Image segmentation using K-means color quantization and density-based spatial clustering of applications with noise (DBSCAN) for hotspot detection in photovoltaic modules , 2016, 2016 IEEE Region 10 Conference (TENCON).

[66]  Benedict Young,et al.  The wizard is Oz , 2010 .

[67]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[68]  Evgeny Osipov,et al.  Integer Echo State Networks: Hyperdimensional Reservoir Computing , 2017, ArXiv.

[69]  Dmitri A. Rachkovskij,et al.  Neural Distributed Autoassociative Memories: A Survey , 2017, ArXiv.

[70]  Su Nguyen,et al.  Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management , 2019, IEEE Transactions on Intelligent Transportation Systems.

[71]  Yue Zhang,et al.  Sensing and Classifying Roadway Obstacles in Smart Cities: The Street Bump System , 2016, IEEE Access.

[72]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[73]  S. G. Axline,et al.  Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. , 1975, Computers and biomedical research, an international journal.

[74]  Chongqing Kang,et al.  Sparse and Redundant Representation-Based Smart Meter Data Compression and Pattern Extraction , 2017, IEEE Transactions on Power Systems.

[75]  Yi Luo,et al.  All-optical machine learning using diffractive deep neural networks , 2018, Science.

[76]  Cheng-Xiang Wang,et al.  Artificial Intelligence to Manage Network Traffic of 5G Wireless Networks , 2018, IEEE Network.

[77]  Qi Wang,et al.  An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition , 2017, IEEE Transactions on Intelligent Transportation Systems.

[78]  Ali Hassan Sodhro,et al.  Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications , 2019, IEEE Transactions on Industrial Informatics.

[79]  Guanghui Wen,et al.  Neuro-Adaptive Consensus Tracking of Multiagent Systems With a High-Dimensional Leader , 2017, IEEE Transactions on Cybernetics.

[80]  Okyay Kaynak,et al.  Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond , 2018, IEEE Access.

[81]  Jian Ma,et al.  Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.

[82]  Xinghuo Yu,et al.  Spatiotemporal Anomaly Detection Using Deep Learning for Real-Time Video Surveillance , 2020, IEEE Transactions on Industrial Informatics.

[83]  Katina Michael,et al.  Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems , 2019, Proc. IEEE.

[84]  Jin Liu,et al.  Industrial Big Data Analytics: Challenges, Methodologies, and Applications , 2016, ArXiv.

[85]  Fei Tao,et al.  Long/Short-Term Utility Aware Optimal Selection of Manufacturing Service Composition Toward Industrial Internet Platforms , 2019, IEEE Transactions on Industrial Informatics.

[86]  Xinghuo Yu,et al.  Bio-Inspired Multisensory Fusion for Autonomous Robots , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[87]  Marco Maggipinto,et al.  A Computer Vision-Inspired Deep Learning Architecture for Virtual Metrology Modeling With 2-Dimensional Data , 2018, IEEE Transactions on Semiconductor Manufacturing.

[88]  Allen Newell,et al.  The logic theory machine-A complex information processing system , 1956, IRE Trans. Inf. Theory.

[89]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[90]  Yuguang Fang,et al.  EPIC: A Differential Privacy Framework to Defend Smart Homes Against Internet Traffic Analysis , 2018, IEEE Internet of Things Journal.

[91]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[92]  Damminda Alahakoon,et al.  Unsupervised Machine Learning Based Scalable Fusion for Active Perception , 2019, IEEE Transactions on Automation Science and Engineering.

[93]  Haidong Shao,et al.  Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network , 2018, IEEE Transactions on Industrial Electronics.

[94]  Valeriy Vyatkin,et al.  Automatic Generation of Control Flow From Requirements for Distributed Smart Grid Automation Control , 2020, IEEE Transactions on Industrial Informatics.

[95]  Christian Mathis,et al.  Data Lakes , 2017, Datenbank-Spektrum.

[96]  Zibin Zheng,et al.  Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids , 2018, IEEE Transactions on Industrial Informatics.

[97]  Teresa Pamula,et al.  Road Traffic Conditions Classification Based on Multilevel Filtering of Image Content Using Convolutional Neural Networks , 2018, IEEE Intelligent Transportation Systems Magazine.

[98]  Fangming Ye,et al.  Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[99]  Fernando J. Corbacho,et al.  Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I) , 2019, ArXiv.

[100]  Zhehan Yi,et al.  Reinforcement-Learning-Based Optimal Control of Hybrid Energy Storage Systems in Hybrid AC–DC Microgrids , 2019, IEEE Transactions on Industrial Informatics.

[101]  Jinya Su,et al.  Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles , 2019, IEEE Transactions on Industrial Informatics.

[102]  M. Majidi,et al.  A Novel Method for Single and Simultaneous Fault Location in Distribution Networks , 2015, IEEE Transactions on Power Systems.

[103]  Chuang Sun,et al.  Discriminative Deep Belief Networks with Ant Colony Optimization for Health Status Assessment of Machine , 2017, IEEE Transactions on Instrumentation and Measurement.

[104]  Zhihan Lv,et al.  Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics , 2017, IEEE Transactions on Industrial Informatics.

[105]  Jan M. Rabaey,et al.  Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[106]  Guo-Jian Cheng,et al.  Industry 4.0 Development and Application of Intelligent Manufacturing , 2016, 2016 International Conference on Information System and Artificial Intelligence (ISAI).

[107]  Nikolaos Papakonstantinou,et al.  Hyperdimensional Computing in Industrial Systems: The Use-Case of Distributed Fault Isolation in a Power Plant , 2018, IEEE Access.

[108]  Mohsen Guizani,et al.  Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services , 2018, IEEE Internet of Things Journal.

[109]  Hassan Ghasemzadeh,et al.  Autonomous Training of Activity Recognition Algorithms in Mobile Sensors: A Transfer Learning Approach in Context-Invariant Views , 2018, IEEE Transactions on Mobile Computing.

[110]  Pascal Vincent,et al.  Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.

[111]  Sang Jun Lee,et al.  Learning Framework for Robust Obstacle Detection, Recognition, and Tracking , 2017, IEEE Transactions on Intelligent Transportation Systems.

[112]  Pierluigi Siano,et al.  A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems , 2015, IEEE Transactions on Industrial Electronics.

[113]  M. Giurfa,et al.  Simultaneous mastering of two abstract concepts by the miniature brain of bees , 2012, Proceedings of the National Academy of Sciences.

[114]  Bruce G. Buchanan,et al.  On generality and problem solving: a case study using the DENDRAL program , 1970 .

[115]  Luciano Floridi,et al.  Establishing the rules for building trustworthy AI , 2019, Nature Machine Intelligence.

[116]  Xinghuo Yu,et al.  An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems , 2014, Comput. Secur..

[117]  Marian Verhelst,et al.  Embedded Deep Neural Network Processing: Algorithmic and Processor Techniques Bring Deep Learning to IoT and Edge Devices , 2017, IEEE Solid-State Circuits Magazine.

[118]  Valeriy Vyatkin,et al.  Intelligent Component-Based Automation of Baggage Handling Systems With IEC 61499 , 2010, IEEE Transactions on Automation Science and Engineering.

[119]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[120]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[121]  Bo Tang,et al.  Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities , 2017, IEEE Transactions on Industrial Informatics.

[122]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.