Log-based predictive maintenance in discrete parts manufacturing

Gutschi, Clemens; Furian, Nikolaus; Suschnigg, Josef; Neubacher, Dietmar; Voessner, Siegfried

doi:10.1016/j.procir.2019.02.098

Cited by 35 publications

(23 citation statements)

References 13 publications

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“…The Run-2-Failure maintenance strategy (also known as the reactive, fault-driven or fire-fighting maintenance strategy) is a maintenance strategy where maintenance activity starts when either an obvious equipment functional failure, malfunction or equipment breakdown occurs. As it is a reactive maintenance strategy, the corrective measurements are governed by random failure events and sometimes these failures lead to very large equipment or machine downtimes, an extensive equipment repair time as well as high repair cost, which decrease production in the manufacturing system [7][8][9][10].…”

Section: Background 21 Maintenance Strategiesmentioning

confidence: 99%

“…Preventive maintenance, also known as time-based maintenance, helps to slow down the equipment deterioration through planned periodic plant inspections and repairs, for example, periodic lubrication and calibration, etc. [8]. In the preventive maintenance strategy, the part for maintenance is replaced on a specific date and this ensures that there is a low possibility of sudden failure.…”

Section: Background 21 Maintenance Strategiesmentioning

confidence: 99%

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Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

et al. 2022

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This paper presents a data-driven approach for the prognosis of the gradual behavioural deterioration of conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems. The approach relies on the knowledge of the power consumption of a conveyor belt motor driver. Data are collected for two separate cases: the static case and dynamic case. In the static case, power consumption data are collected under different loads and belt tension. These data are used by a prognostic model (artificial neural network (ANN)) to learn the conveyor belt motor driver’s power consumption pattern under different belt tensions and load conditions. The data collected during the dynamic case are used to investigate how the belt tension affects the movement of pallets between conveyor zones. During the run time, the trained prognostic model takes real-time power consumption measurements and load information from a testbench (a discrete multirobot mobile assembling line) and predicts a belt tension class. A consecutive mismatch between the predicted belt tension class and optimal belt tension class is an indication of failure, i.e., a gradual loss of belt tension. Hence, maintenance steps must be taken to avoid further catastrophic situations such as belt slippages on head pulleys, material slippages and belt wear and tear.

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Section: Background 21 Maintenance Strategiesmentioning

confidence: 99%

Section: Background 21 Maintenance Strategiesmentioning

confidence: 99%

Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

et al. 2022

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“…They proposed that by collecting more data, specifically for minority classes, the predictive performance of the models can even be further improved. Gutschi et al 47 presented a data-driven approach for estimating the machine breakdown probability during a specified time interval in the future. The authors described applied data-mining, feature-extraction, and ML methods and concluded that machine failures can be reliably predicted up to 168 h in advance.…”

Section: Related Workmentioning

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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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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“…-Classification: diagnose the data to a known failure type or similar working data and then prognosticate a degradation according to the historical data of this class. Despite any classifier can be used for this purpose, the following ones are widely used in literature: feed-forward NN [140], SVM [140], BN [9,85,86], HMM [201], fuzzy logic based [211] and RF [16,62].…”

Section: Prognosismentioning

confidence: 99%

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Serradilla,

Zugasti,

Zurutuza

2020

Preprint

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Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.

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Log-based predictive maintenance in discrete parts manufacturing

Cited by 35 publications

References 13 publications

Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

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

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

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