Machine Learning Models for Predictive Maintenance

Voronov, Sergii

doi:10.3384/diss.diva-162649

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…15 PdM aims at predicting failure time of a system based on experience, physical laws, and machine learning techniques to replace the faulty components before failure, and as the result minimizing downtime of the systems, reducing the maintenance costs, and improving quality of product. 16 For PdM, a variety of technologies can be used as parts of a comprehensive program including monitoring and diagnostic techniques. These techniques include vibration monitoring, 5,[17][18][19] acoustic emission, 20,21 thermographic inspection, 22 oil analysis, 23,24 Radiographic inspection, 25 shock pulse, 26 ultrasonic leak detectors, 27 performance testing, wear and dimensional measurements, 28 signature analysis, 29 and time and frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

Khorsheed

Beyca

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Bearings are the most widely used mechanical parts in rotating machinery under high load and high rotational speeds. Operating continuously under such harsh conditions, wear and failure are imminent. Developing defects give rise to even-higher vibration and temperature levels. In general, mechanical defects in a machine cause high vibration levels. Therefore, bearing fault identification and early detection enables the maintenance team to repair the problem before it triggers catastrophic failure in the bearing. Machine downtime is thus avoided or minimized. This paper explores the use of Machine Learning (ML) integrated with decision-making techniques to predict possible bearing failures and improve the overall manufacturing operations by applying the correct maintenance actions at the right time. The accuracy of the Predictive Maintenance (PdM) module has been tested on real industrial production datasets. The paper proposes an effective PdM methodology using different ML algorithms to detect failures before they happen and reduce pump downtime. The performance of the tested ML algorithms is based on five performance indicators: accuracy, precision, F-score, recall, and an area under curve (AUC). Experimental results revealed that all tested ML algorithms are successful and effective. Furthermore, decision making with utility theory has been employed to exploit the probability of failures and thus help to perform the appropriate maintenance interventions. This provides a logical framework for decision-makers to identify the optimum action with the maximum expected benefit. As a case study, the model is applied on forwarding pumping stations belonging to the Sewerage Treatment Company (STC), one of the largest sewage stations in Qatar.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

Khorsheed

Beyca

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance

Hakami

2024

Sci Rep

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Predictive maintenance harnesses statistical analysis to preemptively identify equipment and system faults, facilitating cost- effective preventive measures. Machine learning algorithms enable comprehensive analysis of historical data, revealing emerging patterns and accurate predictions of impending system failures. Common hurdles in applying ML algorithms to PdM include data scarcity, data imbalance due to few failure instances, and the temporal dependence nature of PdM data. This study proposes an ML-based approach that adapts to these hurdles through the generation of synthetic data, temporal feature extraction, and the creation of failure horizons. The approach employs Generative Adversarial Networks to generate synthetic data and LSTM layers to extract temporal features. ML algorithms trained on the generated data achieved high accuracies: ANN (88.98%), Random Forest (74.15%), Decision Tree (73.82%), KNN (74.02%), and XGBoost (73.93%).

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Applied Complex Diagnostics and Monitoring of Special Power Transformers

Ivanov

Spasova²,

Mateev

et al. 2023

Energies

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As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

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Machine Learning Models for Predictive Maintenance

Cited by 4 publications

References 22 publications

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance

Applied Complex Diagnostics and Monitoring of Special Power Transformers

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