Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
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Javier Del Ser | Basilio Sierra | Diego Galar | Alberto Diez-Olivan | J. Ser | B. Sierra | D. Galar | Alberto Diez-Olivan
[1] Jyoti K. Sinha,et al. Effective vibration-based condition monitoring (eVCM) of rotating machines , 2017 .
[2] Quanmin Zhu,et al. Complex System Modelling and Control Through Intelligent Soft Computations , 2016, Studies in Fuzziness and Soft Computing.
[3] Weisong Shi,et al. Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.
[4] Bartosz Krawczyk,et al. Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.
[5] J. Moubray. Reliability-Centered Maintenance , 1991 .
[6] Markus J. Buehler,et al. De novo composite design based on machine learning algorithm , 2018 .
[7] Nagi Gebraeel,et al. Scalable prognostic models for large-scale condition monitoring applications , 2017 .
[8] Plamen P. Angelov,et al. Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier , 2015, Neurocomputing.
[9] Ravi Shankar,et al. A big data driven sustainable manufacturing framework for condition-based maintenance prediction , 2017, J. Comput. Sci..
[10] C. L. Philip Chen,et al. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..
[11] Nguyen Lu Dang Khoa,et al. Kernel-based support vector machines for automated health status assessment in monitoring sensor data , 2018 .
[12] H. B. Barlow,et al. Unsupervised Learning , 1989, Neural Computation.
[13] M. Klein,et al. Calculating Life Cycle Impact Assessment of Chemicals with Neural Networks , 2014 .
[14] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[15] Isaac Animah,et al. Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets , 2017 .
[16] Soumaya Yacout,et al. Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data , 2018, Expert Syst. Appl..
[17] Hong-Zhong Huang,et al. Physics of failure-based reliability prediction of turbine blades using multi-source information fusion , 2018, Appl. Soft Comput..
[18] A. Gunasekaran,et al. Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .
[19] André Stork,et al. Visual Computing Challenges of Advanced Manufacturing and Industrie 4.0 [Guest editors' introduction] , 2015, IEEE Computer Graphics and Applications.
[20] Mostafa Zandieh,et al. Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm , 2017, Appl. Soft Comput..
[21] Saqib Rasool Chaudhry,et al. IoT architecture challenges and issues: Lack of standardization , 2016, 2016 Future Technologies Conference (FTC).
[22] Jian Zhang,et al. Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.
[23] Francis Rousseaux,et al. BIG DATA and Data-Driven Intelligent Predictive Algorithms to support creativity in Industrial Engineering , 2017, Comput. Ind. Eng..
[24] Liang Guo,et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.
[25] Abdelhakim Khatab,et al. State of the art review of quality, reliability and maintenance issues in closed-loop supply chains with remanufacturing , 2017, Int. J. Prod. Res..
[26] Weiming Shen,et al. A sensor fusion and support vector machine based approach for recognition of complex machining conditions , 2018, J. Intell. Manuf..
[27] Francisco Charte,et al. Addressing imbalance in multilabel classification: Measures and random resampling algorithms , 2015, Neurocomputing.
[28] A. Elayaperumal,et al. Fault diagnostics of spur gear using decision tree and fuzzy classifier , 2017 .
[29] Pasi Fränti,et al. Outlier detection using k-nearest neighbour graph , 2004, ICPR 2004.
[30] R. B. Gopaluni,et al. Deep reinforcement learning approaches for process control , 2017, 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP).
[31] I. S. Jawahir,et al. Quantitative modeling and analysis of supply chain risks using Bayesian theory , 2014 .
[32] Don R. Hush,et al. A Classification Framework for Anomaly Detection , 2005, J. Mach. Learn. Res..
[33] Chen Li,et al. A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops , 2018, Comput. Ind. Eng..
[34] Amanda J. Schmitt,et al. OR/MS models for supply chain disruptions: a review , 2014 .
[35] Axel Tuma,et al. Energy-efficient scheduling in manufacturing companies: A review and research framework , 2016, Eur. J. Oper. Res..
[36] Jinliang Ding,et al. Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system , 2017, Appl. Soft Comput..
[37] Rumi Ghosh,et al. Manufacturing Analytics and Industrial Internet of Things , 2017, IEEE Intelligent Systems.
[38] Qing Ling,et al. Heterogeneous Online Learning for “Thing-Adaptive” Fog Computing in IoT , 2018, IEEE Internet of Things Journal.
[39] Tullio Tolio,et al. Design and management of manufacturing systems for production quality , 2014 .
[40] Farid Kadri,et al. Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems , 2016, Neurocomputing.
[41] Davide Anguita,et al. Machine learning for wear forecasting of naval assets for condition-based maintenance applications , 2015, 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS).
[42] M. S. Safizadeh,et al. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.
[43] Cesare Alippi,et al. Just-In-Time Classifiers for Recurrent Concepts , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[44] John R. Anderson,et al. MACHINE LEARNING An Artificial Intelligence Approach , 2009 .
[45] Richard D. Braatz,et al. Perspectives on process monitoring of industrial systems , 2016, Annu. Rev. Control..
[46] Mostafa Zandieh,et al. An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach , 2017 .
[47] Robert Campbell,et al. Failure Modes and Predictive Diagnostics Considerations for Diesel Engines , 2001 .
[48] M. Blanchet,et al. Industrie 4.0: the new industrial revolution. How Europe will succeed , 2014 .
[49] Andrew Starr,et al. Context-based and human-centred information fusion in diagnostics , 2016 .
[50] Soumaya Yacout,et al. Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation , 2016, J. Intell. Manuf..
[51] Basilio Sierra,et al. Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming , 2019, Comput. Electron. Agric..
[52] Rajkumar Buyya,et al. Distributed data stream processing and edge computing: A survey on resource elasticity and future directions , 2017, J. Netw. Comput. Appl..
[53] Kai Goebel,et al. A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.
[54] Ray Y. Zhong,et al. Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .
[55] H. Kaebernick,et al. Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks , 2007 .
[56] Lovekesh Vig,et al. Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.
[57] Dawei Sun,et al. An intelligent data fusion framework for structural health monitoring , 2016, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA).
[58] Dong-Ling Xu,et al. Evolutionary robust optimization in production planning - interactions between number of objectives, sample size and choice of robustness measure , 2017, Comput. Oper. Res..
[59] Benjamin T. Hazen,et al. Big data and predictive analytics for supply chain and organizational performance , 2017 .
[60] Loredana Cristaldi,et al. A comparative study on data-driven prognostic approaches using fleet knowledge , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.
[61] Gang Feng,et al. Proactive content caching by exploiting transfer learning for mobile edge computing , 2018, Int. J. Commun. Syst..
[62] Lifeng Zhou,et al. Industry 4.0: Towards future industrial opportunities and challenges , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[63] Fei Tao,et al. Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.
[64] Gautam Shroff,et al. Prescriptive information fusion , 2014, 17th International Conference on Information Fusion (FUSION).
[65] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[66] Raúl Rojas,et al. Neural Networks - A Systematic Introduction , 1996 .
[67] Hui Wang,et al. A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems , 2016, Soft Computing.
[68] Iiro Harjunkoski,et al. Optimization of multipurpose process plant operations: A multi-time-scale maintenance and production scheduling approach , 2017, Comput. Chem. Eng..
[69] E. Peter Carden,et al. Vibration Based Condition Monitoring: A Review , 2004 .
[70] Ahmad-Reza Sadeghi,et al. Security and privacy challenges in industrial Internet of Things , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[71] Camilla Lundgren,et al. Quantifying the Effects of Maintenance – a Literature Review of Maintenance Models , 2018 .
[72] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[73] Tatiana Mazali. From industry 4.0 to society 4.0, there and back , 2017, AI & SOCIETY.
[74] John M. Wassick,et al. From rescheduling to online scheduling , 2016 .
[75] Salvatore Distefano,et al. Distributed Data Fusion for the Internet of Things , 2017, PaCT.
[76] Manabu Enoki,et al. Fatigue Performance Prediction of Structural Materials by Multi-scale Modeling and Machine Learning , 2017 .
[77] Zigor Uriondo,et al. Condition-Based Maintenance for medium speed diesel engines used in vessels in operation , 2015 .
[78] James She,et al. BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities , 2018, IEEE Internet of Things Journal.
[79] Yuquan Chen,et al. Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network , 2017 .
[80] Yan Zhang,et al. Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.
[81] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[82] Sotiris B. Kotsiantis,et al. Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.
[83] Linxia Liao,et al. Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.
[84] Christian Bierwirth,et al. Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.
[85] Tao Zhang,et al. Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.
[86] Shahrul Kamaruddin,et al. Maintenance policy optimization—literature review and directions , 2015 .
[87] Kwangyeol Ryu,et al. Evolutionary resource assignment for workload-based production scheduling , 2016, J. Intell. Manuf..
[88] Yan Liu,et al. Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.
[89] Zhenghua Zhou,et al. A novel approach for fault diagnosis of induction motor with invariant character vectors , 2014, Inf. Sci..
[90] Jarka Glassey,et al. Benefits and Challenges of Hybrid Modeling in the Process Industries: An Introduction , 2018 .
[91] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[92] Ada Che,et al. Bi-objective scheduling on uniform parallel machines considering electricity cost , 2018 .
[93] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[94] Wentian Zhao,et al. Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach , 2016, Eng. Appl. Artif. Intell..
[95] Fei Tao,et al. Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.
[96] Erin E. Peterson,et al. An assessment framework for measuring agroecosystem health , 2017 .
[97] Basilio Sierra,et al. Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score , 2017, Neurocomputing.
[98] Mohmmad Hanafy,et al. Co-design of Products and Systems Using a Bayesian Network☆ , 2014 .
[99] Jianchao Zeng,et al. Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence , 2016, Comput. Ind. Eng..
[100] Abdelhamid Boudjelida,et al. On the robustness of joint production and maintenance scheduling in presence of uncertainties , 2019, J. Intell. Manuf..
[101] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[102] Christian Brecher,et al. Industrial Internet of Things and Cyber Manufacturing Systems , 2017 .
[103] Jian Guo,et al. Maintenance scheduling optimization based on reliability and prognostics information , 2016, 2016 Annual Reliability and Maintainability Symposium (RAMS).
[104] Yixiong Feng,et al. Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting , 2017 .
[105] P. O'Donovan,et al. Big data in manufacturing: a systematic mapping study , 2015, Journal of Big Data.
[106] Wenqiang Zhang,et al. Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling , 2017, Comput. Ind. Eng..
[107] Basim Al-Najjar,et al. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making , 2003 .
[108] Diego Cabrera,et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .
[109] Bartosz Krawczyk,et al. Online ensemble learning with abstaining classifiers for drifting and noisy data streams , 2017, Appl. Soft Comput..
[110] Joaquín B. Ordieres Meré,et al. Optimizing the production scheduling of a single machine to minimize total energy consumption costs , 2014 .
[111] Lei Shu,et al. Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.
[112] Song Han,et al. Industrial Internet of Things: Challenges, Opportunities, and Directions , 2018, IEEE Transactions on Industrial Informatics.
[113] Basilio Sierra,et al. Kernel Density-Based Pattern Classification in Blind Fasteners Installation , 2017, HAIS.
[114] M. Duran Toksarı,et al. Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects , 2018, Int. J. Prod. Res..
[115] Iñaki Inza,et al. Weak supervision and other non-standard classification problems: A taxonomy , 2016, Pattern Recognit. Lett..
[116] Taghi M. Khoshgoftaar,et al. A survey of transfer learning , 2016, Journal of Big Data.
[117] Sudip Misra,et al. Assessment of the Suitability of Fog Computing in the Context of Internet of Things , 2018, IEEE Transactions on Cloud Computing.
[118] Yousef Saad,et al. Trace optimization and eigenproblems in dimension reduction methods , 2011, Numer. Linear Algebra Appl..
[119] Diego Cabrera,et al. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.
[120] Yingfeng Zhang,et al. A framework for Big Data driven product lifecycle management , 2017 .
[121] Behnam Malakooti. Operations and Production Systems with Multiple Objectives , 2014 .
[122] V. Sugumaran,et al. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines , 2015 .
[123] Seyyed M. T. Fatemi Ghomi,et al. A survey of multi-factory scheduling , 2016, J. Intell. Manuf..
[124] Masatoshi Sakawa,et al. Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms , 2000, Eur. J. Oper. Res..
[125] Hassan Ghasemzadeh,et al. Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges , 2017, Inf. Fusion.
[126] Didier Stricker,et al. Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet , 2015, IEEE Computer Graphics and Applications.
[127] Vic Barnett,et al. Outliers in Statistical Data , 1980 .
[128] Xindong Wu,et al. Mining Recurring Concept Drifts with Limited Labeled Streaming Data , 2010, TIST.
[129] Mostafa Zandieh,et al. A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms , 2016, Comput. Oper. Res..
[130] Ciprian Dobre,et al. Event-based sensor data exchange and fusion in the Internet of Things environments , 2018, J. Parallel Distributed Comput..
[131] Lionel Tarassenko,et al. Static and dynamic novelty detection methods for jet engine health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[132] Bogdan Gabrys,et al. Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..
[133] H. Kaebernick,et al. Determining the Reuse Potential of Components Based on Life Cycle Data , 2005 .
[134] Wenzhu Liao,et al. Multi-objective group scheduling optimization integrated with preventive maintenance , 2017 .
[135] Calin Florin Baban,et al. Using a fuzzy logic approach for the predictive maintenance of textile machines , 2016, J. Intell. Fuzzy Syst..
[136] C. James Li,et al. Acoustic emission analysis for bearing condition monitoring , 1995 .
[137] Rong-Hwa Huang,et al. An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting , 2017, Appl. Soft Comput..
[138] Marcello Braglia,et al. Data classification and MTBF prediction with a multivariate analysis approach , 2012, Reliab. Eng. Syst. Saf..
[139] K. Goebel,et al. Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.
[140] Mykola Pechenizkiy,et al. An Overview of Concept Drift Applications , 2016 .
[141] S. Salcedo-Sanz,et al. A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources , 2015 .
[142] Nezih Mrad,et al. The role of data fusion in predictive maintenance using digital twin , 2018 .
[143] Marilyn Wolf,et al. Industrial Internet of Things , 2018 .
[144] Sungzoon Cho,et al. Mining the relationship between production and customer service data for failure analysis of industrial products , 2017, Comput. Ind. Eng..
[145] Hoda A. ElMaraghy,et al. Integrated products–systems design environment using Bayesian networks , 2017, Int. J. Comput. Integr. Manuf..
[146] Richard Curran,et al. Knowledge-Based Engineering Review: Conceptual Foundations and Research Issues , 2010, ISPE CE.
[147] Francesc Pozo,et al. Wind turbine fault detection and classification by means of image texture analysis , 2018, Mechanical Systems and Signal Processing.
[148] James R. Ottewill,et al. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion , 2017 .
[149] Sylvain Verron,et al. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..
[150] Marvin Rausand,et al. Reliability Centred Maintenance , 2008 .
[151] Dustin Harvey,et al. Characterization and Prognosis of Multirotor Failures , 2015 .
[152] Hongzheng Fang,et al. Research on Software Architecture of Prognostics and Health Management System for Civil Aircraft , 2017, 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC).
[153] Tullio Tolio,et al. Virtual Factory: An Integrated Framework for Manufacturing Systems Design and Analysis☆ , 2013 .
[154] Dirk Schaefer,et al. On Servitization of the Manufacturing Industry in the UK , 2016 .
[155] D. Y. Sha,et al. A Multi-objective PSO for job-shop scheduling problems , 2009, 2009 International Conference on Computers & Industrial Engineering.
[156] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[157] Chrysanthos E. Gounaris,et al. Multi‐stage adjustable robust optimization for process scheduling under uncertainty , 2016 .
[158] Stephen D. J. McArthur,et al. Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring , 2017, IEEE Transactions on Reliability.
[159] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[160] Lin Ma,et al. Prognostic modelling options for remaining useful life estimation by industry , 2011 .
[161] Sarvesh Rawat,et al. Multi-sensor data fusion by a hybrid methodology - A comparative study , 2016, Comput. Ind..
[162] Anastassios N. Perakis,et al. Statistical Methods for Planning Diesel Engine Overhauls in the U. S. Coast Guard , 2004 .
[163] Ming Liu,et al. Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning , 2015 .
[164] Daniel A. Keim,et al. Visual Analytics: Definition, Process, and Challenges , 2008, Information Visualization.
[165] Jay Lee,et al. Introduction to Data-Driven Methodologies for Prognostics and Health Management , 2017 .
[166] David Grube Hansen,et al. Industry 4.0 and digitalization call for vocational skills, applied industrial engineering, and less for pure academics , 2016 .
[167] Laurent Mora,et al. Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part I—Modeling and measurements , 2018 .
[168] Jay Lee,et al. Guest Editorial Special Section on Smart Agents and Cyber-Physical Systems for Future Industrial Systems , 2017, IEEE Trans. Ind. Informatics.
[169] Ratna Babu Chinnam,et al. An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools , 2019, Comput. Ind. Eng..
[170] Seyed Hessameddin Zegordi,et al. Coordinative production and maintenance scheduling problem with flexible maintenance time intervals , 2017, J. Intell. Manuf..
[171] Ahmed El Hilali Alaoui,et al. The "Dual-Ants Colony": A novel hybrid approach for the flexible job shop scheduling problem with preventive maintenance , 2017, Comput. Ind. Eng..
[172] Omprakash Kaiwartya,et al. A Concise Review on Internet of Things (IoT) -Problems, Challenges and Opportunities , 2018, 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP).
[173] Huijun Gao,et al. Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures , 2016, IEEE Transactions on Control Systems Technology.
[174] Gang Niu,et al. IETM centered intelligent maintenance system integrating fuzzy semantic inference and data fusion , 2017, Microelectron. Reliab..
[175] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[176] Alessandro Ancarani,et al. Successful digital transformations need a focus on the individual , 2018 .
[177] Francisco Herrera,et al. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..
[178] Kirsten E. Martin. Ethical Issues in the Big Data Industry , 2015, MIS Q. Executive.
[179] Jun Wu,et al. Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system , 2018, Appl. Soft Comput..
[180] Donghua Zhou,et al. Diagnosis and Prognosis for Complicated Industrial Systems - Part I , 2016, IEEE Trans. Ind. Electron..
[181] Ju H. Park,et al. Differential feature based hierarchical PCA fault detection method for dynamic fault , 2016, Neurocomputing.
[182] Ray Y. Zhong,et al. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives , 2016, Comput. Ind. Eng..
[183] Xiaoxia Yang,et al. Joint optimization of preventive maintenance and production scheduling for parallel machines system , 2017, J. Intell. Fuzzy Syst..
[184] Noureddine Zerhouni,et al. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction , 2016, J. Intell. Manuf..
[185] Wei Li,et al. Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method , 2015 .
[186] Fatiha Nejjari,et al. Event-based approach for supply chain fault analysis , 2005 .
[187] Marco Antonelli,et al. Fault detection and explanation through big data analysis on sensor streams , 2017, Expert Syst. Appl..
[188] Peter W. Tse,et al. Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .
[189] Brian A. Weiss,et al. A review of diagnostic and prognostic capabilities and best practices for manufacturing , 2019, J. Intell. Manuf..
[190] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[191] Danny H. K. Tsang,et al. Challenges and Solutions in Fog Computing Orchestration , 2018, IEEE Network.
[192] Gang Niu,et al. Prognostic control-enhanced maintenance optimization for multi-component systems , 2017, Reliab. Eng. Syst. Saf..
[193] Yang Lu,et al. Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..
[194] Sundarapandian Vaidyanathan,et al. Computational Intelligence Applications in Modeling and Control , 2015, Computational Intelligence Applications in Modeling and Control.
[195] Xiaojun Zhou,et al. Preventive maintenance modeling for multi-component systems with considering stochastic failures and disassembly sequence , 2015, Reliab. Eng. Syst. Saf..
[196] Zhuo Chen,et al. Edge Analytics in the Internet of Things , 2015, IEEE Pervasive Computing.
[197] Aleksandar Lazarevic,et al. Outlier Detection with Kernel Density Functions , 2007, MLDM.
[198] Yang Wang,et al. A clustering approach for structural health monitoring on bridges , 2016 .
[199] Opher Etzion,et al. Event Processing in Action , 2010 .