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The work presents a data-driven model that integrates machine learning, sensor technologies, and Industry 4.0 data acquisition. Vibration and temperature signals are collected from industrial electrical motors using Bluetooth sensors, transmitted to a central storage system, and prepared through preprocessing. Isolation Forest is applied for outlier filtering and reliability improvement. Optimized ARIMA, Random Forest, and LSTM models predict future vibration levels with hyperparameter tuning via SMBO-TPE and residual monitoring to detect emerging discrepancies.","Advancements in predictive maintenance modelling for industrial electrical motors: integrating machine learning and sensor technologies  \nHanifi, Shahram; Alkali, Babakalli; Lindsay, Gordon; Waters, Mark; McGlinchey, Don  \nPublished in:  \nMeasurement: Sensors  \nDOI:  \n10.1016/j.measen.2024.101473  \nPublication date:  \n2025  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nHanifi, S, Alkali, B, Lindsay, G, Waters, M & McGlinchey, D 2025, 'Advancements in predictive maintenance modelling for industrial electrical motors: integrating machine learning and sensor technologies', Measurement:  \nSensors, vol. 38, 101473. [https://doi.org/10.1016/j.measen.2024.101473](https://doi.org/10.1016/j.measen.2024.101473)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 31. Jul. 2025  \nMeasurement: Sensors xxx (xxxx) xxx  \nContents lists available at ScienceDirect  \nMeasurement: Sensors  \njournal [homepage: www.sciencedirect.com/journal/measurement-sensors](homepage: www.sciencedirect.com/journal/measurement-sensors)  \nAdvancements in predictive maintenance modelling for industrial electrical motors: Integrating machine learning and sensor technologies  \nA R T I C L E I N F O  \nKeywords:  \nPredictive maintenance Machine learning  \nSensor technologies Vibration analysis Hyperparameter optimisation  \nA B S T R A C T  \nPredictive maintenance is crucial in modern industrial settings, aiming to optimise performance, minimise downtime, and prevent costly equipment failures. The advancements in the use of machine learning techniques, sensor technologies, and data acquisition systems within Industry 4.0 has generated a lot of interest lately. In this paper, a novel predictive maintenance model is developed using machine learning approach for modelling vibration and temperature data collection for electrical motors of power press machines in Mitsubishi Electric Air Conditioning production (MACE) factory in the United Kingdom. Vibration and temperature data were collected using Bluetooth sensors installed on the motors, transmitted to a central data storage system for further analysis. To ensure the accuracy as well as the quality and reliability of the collected data sets for further analysis, preprocessing of the data was conducted, and the Isolation Forest (IF) outlier detection method was employed to filter out the anomalies. Machine learning algorithms including Auto-Regressive Integrated Moving Average (ARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM) networks were employed for predicting vibration signals, with hyperparameter tuning conducted using Sequential Model-Based optimisation (SMBO) with the Tree Parzen Estimator (TPE).  \nThe results and concluding remarks presented in this paper show how the performance of the optimized ARIMA model can be used in predicting future vibration levels of the electrical motor. The residual analysis is also used to monitor discrepancies between predicted and observed vibration values, enabling proactive identification of emerging issues.  \n1. Introduction  \nIn modern industrial settings, the significance of predictive maintenance strategies has escalated, driven by the imperative to ensure optimal performance, minimise downtime, and avert costly equipment failures.  \nPredictive maintenance relies on the analysis of various types of data gene","cbCaivqcOmCJ46kL","https://ap.wps.com/l/cbCaivqcOmCJ46kL","pdf",2047030,1,5,"English","en",105,"# Introduction\n## Predictive maintenance and data sources\n## Role of vibration analysis\n## Machine learning for fault detection\n# Methodology\n## Industrial asset and data collection\n## Data preprocessing and outlier detection\n## Modeling and hyperparameter optimisation","[{\"question\":\"What problem does the predictive maintenance approach address?\",\"answer\":\"It aims to optimize industrial performance, minimize downtime, and prevent expensive equipment failures by forecasting issues before critical breakdowns.\"},{\"question\":\"How is the sensor data collected and prepared for modelling?\",\"answer\":\"Vibration and temperature data are gathered using Bluetooth sensors on the motors, sent to central storage, then preprocessed and screened for anomalies using Isolation Forest.\"},{\"question\":\"Which machine learning models are used to predict vibration signals?\",\"answer\":\"The study uses ARIMA, Random Forest, and LSTM for vibration prediction, with hyperparameter tuning performed using SMBO with the Tree Parzen Estimator (TPE).\"},{\"question\":\"How are prediction errors monitored for early issue detection?\",\"answer\":\"Residual analysis compares predicted and observed vibration values to highlight discrepancies, enabling proactive identification of emerging problems.\"}]","Advancements in Predictive Maintenance Modelling for Industrial Electrical Motors - 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