Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.

[1]  Yigeng Xu,et al.  Experimental and Computational Vibration Analysis for Diagnosing the Defects in High Performance Composite Structures Using Machine Learning Approach , 2022, Applied Sciences.

[2]  A. Mokhtar,et al.  Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review , 2022, Applied Sciences.

[3]  E. Kraker,et al.  Bayesian Hierarchical Modelling for Uncertainty Quantification in Operational Thermal Resistance of LED Systems , 2022, Applied Sciences.

[4]  M. Adda,et al.  On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges , 2022, Applied Sciences.

[5]  V. Vakharia,et al.  A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models , 2022, Machines.

[6]  Temitope O Awodiji,et al.  Industrial Big Data Analytics and Cyber-Physical Systems for Future Maintenance & Service Innovation , 2021 .

[7]  Rajeev Agrawal,et al.  Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions , 2021, Applied Sciences.

[8]  R. Miśkiewicz,et al.  Practical Application of the Industry 4.0 Concept in a Steel Company , 2020, Sustainability.

[9]  Fabio Sgarbossa,et al.  Digital Facility Layout Planning , 2020, Sustainability.

[10]  Stamatis Voliotis,et al.  Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects , 2019, Sensors.

[11]  S. Tayebati,et al.  Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods , 2019, Machines.

[12]  Mukund Nilakantan Janardhanan,et al.  A predictive maintenance cost model for CNC SMEs in the era of industry 4.0 , 2019, The International Journal of Advanced Manufacturing Technology.

[13]  Stefan Wrobel,et al.  A review of machine learning for the optimization of production processes , 2019, The International Journal of Advanced Manufacturing Technology.

[14]  Silvestro Vespoli,et al.  An electrical DC Motor Equivalent Circuit testbed for the battery Prognostic Health and Management , 2019, 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT).

[15]  Hao Lu,et al.  A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).

[16]  Juan M. Corchado,et al.  A Predictive Maintenance Model Using Recurrent Neural Networks , 2019, SOCO.

[17]  Ricardo S. Alonso,et al.  Edge Computing Architectures in Industry 4.0: A General Survey and Comparison , 2019, SOCO.

[18]  Alessandro Ceruti,et al.  Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing , 2019, J. Comput. Des. Eng..

[19]  Kai Goebel,et al.  A neural network filtering approach for similarity-based remaining useful life estimation , 2018, The International Journal of Advanced Manufacturing Technology.

[20]  Eduardo Alves Portela Santos,et al.  Industrial maintenance decision-making: A systematic literature review , 2017 .

[21]  Minvydas Ragulskis,et al.  Machine component health prognostics with only truncated histories using geometrical metric approach , 2017, Mechanical Systems and Signal Processing.

[22]  Kin Keung Lai,et al.  Multi-Scale Volatility Feature Analysis and Prediction of Gold Price , 2017, Int. J. Inf. Technol. Decis. Mak..

[23]  Péter Horváth,et al.  Industrie 4.0 - Volkswirtschaftliches Potenzial für Deutschland , 2015 .

[24]  Felix Wortmann,et al.  Internet of Things , 2015, Bus. Inf. Syst. Eng..

[25]  Daniel D. Giusto,et al.  The Internet of Things: 20th Tyrrhenian Workshop on Digital Communications , 2014 .

[26]  Noureddine Zerhouni,et al.  Hybrid prognostic method applied to mechatronic systems , 2013 .

[27]  Bo-Suk Yang,et al.  Application of relevance vector machine and logistic regression for machine degradation assessment , 2010 .

[28]  Detlef Zühlke,et al.  SmartFactory - Towards a factory-of-things , 2010, Annu. Rev. Control..

[29]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[30]  Thomas Hess,et al.  Internet of Services , 2009, Bus. Inf. Syst. Eng..

[31]  Noureddine Zerhouni,et al.  Review of prognostic problem in condition-based maintenance , 2009, 2009 European Control Conference (ECC).

[32]  Armando Fox,et al.  Improving Machine Tool Interoperability Using Standardized Interface Protocols: MT Connect , 2008 .

[33]  Nagi Gebraeel,et al.  A Neural Network Degradation Model for Computing and Updating Residual Life Distributions , 2008, IEEE Transactions on Automation Science and Engineering.

[34]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[35]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[36]  Jay Lee,et al.  A prognostic algorithm for machine performance assessment and its application , 2004 .

[37]  C. James Li,et al.  DIAGNOSTIC RULE EXTRACTION FROM TRAINED FEEDFORWARD NEURAL NETWORKS , 2002 .

[38]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[39]  Yonghong Tan,et al.  Neural-network-based d-step-ahead predictors for nonlinear systems with time delay , 1999 .

[40]  Dawei W. Dong,et al.  Neural networks for engine fault diagnostics , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[41]  R. Ganesan,et al.  Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery , 1997 .

[42]  Michael J. Roemer,et al.  Machine health monitoring and life management using finite-element-based neural networks , 1996 .

[43]  José-Raúl Ruiz-Sarmiento,et al.  A predictive model for the maintenance of industrial machinery in the context of industry 4.0 , 2020, Eng. Appl. Artif. Intell..

[44]  Marco Macchi,et al.  A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms , 2019, IFAC-PapersOnLine.

[45]  Yasnitsky Leonid.  Advances in Intelligent Systems and Computing , 2019 .

[46]  A. Duyar,et al.  Predictive Maintenance , 2016 .

[47]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[48]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[49]  Zongxue Xu,et al.  Temporal variations of reference evapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China , 2015 .

[50]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[51]  Carmen Constantinescu,et al.  Smart Factory - A Step towards the Next Generation of Manufacturing , 2008 .

[52]  H. Kagermann,et al.  Strategic Enterprise Management (SEM) , 1999 .

[53]  Geometric Modeling,et al.  Theory and Implementation , 2022 .

[54]  S. Czepiel,et al.  Maximum Likelihood Estimation of Logistic Regression Models : Theory and Implementation , 2022 .