{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:10:45Z","timestamp":1777043445712,"version":"3.51.4"},"reference-count":50,"publisher":"Tech Science Press","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.064090","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T04:01:21Z","timestamp":1746676881000},"page":"5091-5114","source":"Crossref","is-referenced-by-count":1,"title":["A Bayesian Optimized Stacked Long Short-Term Memory Framework for Real-Time Predictive Condition Monitoring of Heavy-Duty Industrial Motors"],"prefix":"10.32604","volume":"83","author":[{"given":"Mudasir","family":"Dilawar","sequence":"first","affiliation":[]},{"given":"Muhammad","family":"Shahbaz","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","first-page":"100822","article-title":"Machine learning and internet of things in Industry 4.0: a review","volume":"28","author":"Rahman","year":"2023","journal-title":"Meas: Sens"},{"key":"ref2","first-page":"90","article-title":"Smart factories in the age of Industry 4.0","volume":"28","author":"Grabowska","year":"2020","journal-title":"Manag Syst Prod Eng"},{"key":"ref3","first-page":"135","author":"Chryssolouris","year":"2023","journal-title":"A perspective on artificial intelligence in manufacturing"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.neucom.2022.04.055","article-title":"Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction","volume":"494","author":"Chen","year":"2022","journal-title":"Neurocomputing"},{"key":"ref5","series-title":"Proceedings of the 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET)","first-page":"412","article-title":"Data analytics for predictive maintenance of industrial robots","author":"Borgi","year":"2017 Jan 14\u201317"},{"key":"ref6","series-title":"Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE\/ITMC)","first-page":"1","article-title":"An Industry 4.0-enabled low cost predictive maintenance approach for smes","author":"Sezer","year":"2018 Jun 17\u201320"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.compind.2018.07.004","article-title":"IDARTS\u2014towards intelligent data analysis and real-time supervision for Industry 4.0","volume":"101","author":"Peres","year":"2018","journal-title":"Comput Ind"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1515\/teme-2016-0072","article-title":"Automatic feature extraction and selection for classification of cyclical time series data","volume":"84","author":"Schneider","year":"2017","journal-title":"TM-Tech Mess"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.cie.2018.12.056","article-title":"Fuzzy early warning systems for condition based maintenance","volume":"128","author":"Vafaei","year":"2019","journal-title":"Comput Ind Eng"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cie.2019.04.051","article-title":"Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures","volume":"133","author":"Dong","year":"2019","journal-title":"Comput Ind Eng"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.cie.2019.02.034","article-title":"Reliability modeling with condition-based maintenance for binary-state deteriorating systems considering zoned shock effects","volume":"130","author":"Wei","year":"2019","journal-title":"Comput Ind Eng"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1080\/0951192X.2019.1571236","article-title":"PriMa: a prescriptive maintenance model for cyber-physical production systems","volume":"32","author":"Ansari","year":"2019","journal-title":"Int J Comput Integr Manuf"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.procs.2022.01.252","article-title":"Maintenance digital twin using vibration data","volume":"200","author":"Abbate","year":"2022","journal-title":"Procedia Comput Sci"},{"key":"ref14","series-title":"Proceedings of the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","first-page":"1","article-title":"Analysis and evaluation of vibration sensors for predictive maintenance of large gears with an appropriate test bench","author":"Strakosch","year":"2021 May 17\u201320"},{"key":"ref15","series-title":"Proceedings of the Machine Learning for Cyber Physical Systems: Selected Papers from the International Conference ML4CPS 2017","first-page":"1","article-title":"Prescriptive maintenance of CPPS by integrating multimodal data with dynamic Bayesian networks","author":"Ansari","year":"2019 Apr 10"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"202","DOI":"10.3390\/info11040202","article-title":"SOPHIA: an event-based IoT and machine learning architecture for predictive maintenance in Industry 4.0","volume":"11","author":"Calabrese","year":"2020","journal-title":"Information"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"103380","DOI":"10.1016\/j.compind.2020.103380","article-title":"A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines","volume":"125","author":"Schwendemann","year":"2021","journal-title":"Comput Ind"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"107393","DOI":"10.1016\/j.measurement.2019.107393","article-title":"Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder","volume":"152","author":"Zhiyi","year":"2020","journal-title":"Measurement"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.3390\/s20071884","article-title":"Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers","volume":"20","author":"Toma","year":"2020","journal-title":"Senssors"},{"key":"ref20","series-title":"Proceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","first-page":"257","article-title":"Deep learning algorithms for bearing fault diagnostics\u2014a review","author":"Zhang","year":"2019 Aug 27\u201330"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep learning algorithms for bearing fault diagnostics\u2014a comprehensive review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","article-title":"Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application","volume":"97","author":"Han","year":"2020","journal-title":"ISA Trans"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"114897","DOI":"10.1016\/j.eswa.2021.114897","article-title":"Adopting machine learning and condition monitoring PF curves in determining and prioritizing high-value assets for life extension","volume":"176","author":"Ochella","year":"2021","journal-title":"Expert Syst Appl"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"106948","DOI":"10.1016\/j.cie.2020.106948","article-title":"The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system","volume":"151","author":"Cakir","year":"2021","journal-title":"Comput Ind Eng"},{"key":"ref25","first-page":"313","article-title":"Time series forecasting using LSTM and ARIMA","volume":"14","author":"Albeladi","year":"2023","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3390\/fi15080255","article-title":"A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks","volume":"15","author":"Kontopoulou","year":"2023","journal-title":"Future Internet"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"110489","DOI":"10.1016\/j.knosys.2023.110489","article-title":"Self-paced ARIMA for robust time series prediction","volume":"269","author":"Li","year":"2023","journal-title":"Knowl-Based Syst"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"4778","DOI":"10.3390\/electronics12234778","article-title":"Comparative performance analysis of RNN techniques for predicting concatenated normal and abnormal vibrations","volume":"12","author":"Lee","year":"2023","journal-title":"Electronics"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"531","DOI":"10.3390\/machines11050531","article-title":"LSTM-based condition monitoring and fault prognostics of rolling element bearings using raw vibrational data","volume":"11","author":"Afridi","year":"2023","journal-title":"Machines"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"13360","DOI":"10.1109\/JSEN.2023.3273279","article-title":"Condition monitoring and fault detection of wind turbine driveline with the implementation of deep residual long short-term memory network","volume":"23","author":"He","year":"2023","journal-title":"IEEE Sens J"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"22465","DOI":"10.1109\/ACCESS.2024.3364395","article-title":"Short-term fault prediction of wind turbines based on integrated RNN-LSTM","volume":"12","author":"Rama","year":"2024","journal-title":"IEEE Access"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.3390\/s24103215","article-title":"Condition monitoring and predictive maintenance of assets in manufacturing using LSTM-autoencoders and transformer encoders","volume":"24","author":"Bampoula","year":"2024","journal-title":"Sensors"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s11053-019-09597-z","article-title":"prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms","volume":"29","author":"Yang","year":"2020","journal-title":"Nat Resour Res"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"31826","DOI":"10.1109\/ACCESS.2021.3059018","article-title":"Development of an optimized regression model to predict blast-driven ground vibrations","volume":"9","author":"Moustafa","year":"2021","journal-title":"IEEE Access"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"13305","DOI":"10.1007\/s00521-022-06949-4","article-title":"Recurrent neural network model for high-speed train vibration prediction from time series","volume":"34","author":"Si\u0142ka","year":"2022","journal-title":"Neural Comput Appl"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"106706","DOI":"10.1016\/j.asoc.2020.106706","article-title":"Prediction and analysis of cold rolling mill vibration based on a data-driven method","volume":"96","author":"Lu","year":"2020","journal-title":"Appl Soft Comput"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1038\/s41598-024-53868-6","article-title":"Vibration prediction and analysis of the main beam of the TBM based on a multiple linear regression model","volume":"14","author":"Yang","year":"2024","journal-title":"Sci Rep"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"5858","DOI":"10.3390\/s22155858","article-title":"Time series forecasting of motor bearing vibration based on informer","volume":"22","author":"Yang","year":"2022","journal-title":"Sensors"},{"key":"ref39","series-title":"Proceedings of the 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","first-page":"1","article-title":"Bayesian optimized autoencoder for predictive maintenance of smart packaging machines","author":"Arifeen","year":"2023 May 8\u201311"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"091002","DOI":"10.1115\/1.4065777","article-title":"An enhanced modeling framework for bearing fault simulation and machine learning-based identification with Bayesian-optimized hyperparameter tuning","volume":"24","author":"Ortiz","year":"2024","journal-title":"J Comput Inf Sci Eng"},{"key":"ref41","series-title":"Proceedings of the 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","first-page":"542","article-title":"Optimal feature selection via bayesian optimisation for acoustic condition monitoring","author":"Zhang","year":"2023 Aug 28\u201331"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"5058","DOI":"10.3390\/s23115058","article-title":"Bayesian-based hyperparameter optimization of 1D-CNN for structural anomaly detection","volume":"23","author":"Li","year":"2023","journal-title":"Sensors"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"15","DOI":"10.57062\/ijpem-st.2023.0115","article-title":"Bayesian hyper-parameter optimization in one-dimensional convolutional autoencoder for monitoring bearing health status","volume":"2","author":"Chae","year":"2024","journal-title":"Int J Precis Eng Manuf-Smart Technol"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1177\/09702385241280813","article-title":"Emerging trends in industry 4.0 and predictive maintenance","volume":"43","author":"Kaur","year":"2025","journal-title":"Abhigyan"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1177\/0309524X221124031","article-title":"SCADA data for wind turbine data-driven condition\/performance monitoring: a review on state-of-art, challenges and future trends","volume":"47","author":"Pandit","year":"2023","journal-title":"Wind Eng"},{"key":"ref46","first-page":"219","author":"Hutter","year":"2019","journal-title":"Automated machine learning: methods, systems, challenges"},{"key":"ref47","unstructured":"Zoph B, Le QV. Neural architecture search with reinforcement learning. arXiv:1611.01578. 2016."},{"key":"ref48","first-page":"1","article-title":"Neural architecture search: a survey","volume":"20","author":"Elsken","year":"2019","journal-title":"J Mach Learn Res"},{"key":"ref49","first-page":"26","article-title":"Hyperparameter optimization for machine learning models based on Bayesian optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J Electron Sci Technol"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s12530-020-09345-2","article-title":"Automatic tuning of hyperparameters using Bayesian optimization","volume":"12","author":"Victoria","year":"2021","journal-title":"Evol Syst"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-3\/TSP_CMC_64090\/TSP_CMC_64090.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:32:00Z","timestamp":1763343120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n3\/61067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":50,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.064090","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}