What question did this study set out to answer?

Develop and implement an intelligent predictive maintenance system driven by machine learning to anticipate industrial equipment failures before operational breakdowns occur.

July 8, 2025Bulletin of Shakarim University Technical SciencesOpen Access

Development of a Predictive Maintenance System Based on Machine Learning

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Authors

MKM. K. Koibagarov ZIZhanibek Issabekov LKLazzat Kurmangaliyeva

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Overview

Systems development study demonstrates automated machine failure forecasting in industrial equipment, highlighting scalable predictive maintenance for aging infrastructure.

Key Points

Develop and implement an intelligent predictive maintenance system driven by machine learning to anticipate industrial equipment failures before operational breakdowns occur.
Captured multi-sensor data (current, temperature, pressure, vibration) using Siemens PLCs and integrated optical data from computer vision via the OPC UA protocol into an industrial IoT pipeline.
Trained predictive algorithms—including neural networks, linear regression, and autoencoders—with continuous model refinement using Bayesian updating.
Built operational interfaces using SCADA for plant engineers and Power BI dashboards for operational management.
Demonstrated qualitative end-to-end viability for real-time equipment condition monitoring and anomaly detection, outperforming static repair schedules.
Identified practical pathways to reduce downtime, cut operating costs, and alleviate engineering personnel shortages in aging manufacturing facilities.

Cite This Study

Koibagarov et al. (2025) studied this question.

synapsesocial.com/papers/6a0eff5a25c30b2cc7fa0556 https://doi.org/10.53360/2788-7995-2025-2(18)-14

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July 8, 2025Bulletin of Shakarim University Technical SciencesOpen Access

Development of a Predictive Maintenance System Based on Machine Learning

View Full Paper

Ask AI

Bookmark

Authors

MKM. K. Koibagarov ZIZhanibek Issabekov LKLazzat Kurmangaliyeva

Discussion

Member takes

Overview

Systems development study demonstrates automated machine failure forecasting in industrial equipment, highlighting scalable predictive maintenance for aging infrastructure.

Key Points

Develop and implement an intelligent predictive maintenance system driven by machine learning to anticipate industrial equipment failures before operational breakdowns occur.
Captured multi-sensor data (current, temperature, pressure, vibration) using Siemens PLCs and integrated optical data from computer vision via the OPC UA protocol into an industrial IoT pipeline.
Trained predictive algorithms—including neural networks, linear regression, and autoencoders—with continuous model refinement using Bayesian updating.
Built operational interfaces using SCADA for plant engineers and Power BI dashboards for operational management.
Demonstrated qualitative end-to-end viability for real-time equipment condition monitoring and anomaly detection, outperforming static repair schedules.
Identified practical pathways to reduce downtime, cut operating costs, and alleviate engineering personnel shortages in aging manufacturing facilities.

Cite This Study

Koibagarov et al. (2025) studied this question.

synapsesocial.com/papers/6a0eff5a25c30b2cc7fa0556 https://doi.org/10.53360/2788-7995-2025-2(18)-14

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