Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models

Fernandes, Marta; Canito, Alda; Corchado, Juan M.; Marreiros, Goreti

doi:10.1007/978-3-030-23887-2_20

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…The fault detection method of a predictive maintenance system developed for a mechanical metallurgy company is described in another study published in 2020 [48]. Since labeled data was unavailable, the authors used a predictionbased anomaly detection technique to discover unusual occurrences in sensor data obtained from monitoring different CNC machines.…”

Section: Other Approachesmentioning

confidence: 99%

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

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Section: Other Approachesmentioning

confidence: 99%

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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“…They somehow connect to maintenance activities [13]. Additionally, malfunction detection is crucial components of PM as it is essential in industries to perceive faults at early stage [14]. Methodologies for maintenance procedures can be classified: a) Corrective maintenance (CM): it is non-scheduled maintenance but it is the straightforward among maintenance approaches which is done only when the machine fails.…”

Section: Introductionmentioning

confidence: 99%

Simulation for predictive maintenance using weighted training algorithms in machine learning

Jittawiriyanukoon¹,

Srisarkun²

2022

IJECE

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<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artiﬁcial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classiﬁcation, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

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“…Autoregressive (AR) models the autocorrelation of time series variables that depend linearly on the values of the previous variables [5]. Moving Average (MA) models the autocorrelation of previous errors in the time series [6]. While the Gegenbauer autoregressive moving average (GARMA) is a generalized model of the generalized ARFIMA model [4], which this model was introduced by Granger, Joyeux amd Jonathan Hosking in 1981.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Arima and Garma's Performance on Data on Positive Covid-19 Cases in Indonesia

Sofro

Khikmah²

2022

BAREKENG: J. Math. & App.

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The development of methods in statistics, one of which is used for prediction, is overgrowing. So it requires further analysis related to the goodness of the method. One of the comparisons made to the goodness of this model can be seen by applying it to actual cases around us. The real case still being faced by people worldwide, including in Indonesia, is Covid-19. Therefore, research comparing the autoregressive integrated moving average (ARIMA) and the Gegenbauer autoregressive moving average (GARMA) method in positive confirmed cases of Covid-19 in Indonesia is essential. Based on the results of this research analysis, it was found that the best model with the Aikake's Information Criterion measure of goodness that was used to predict positive confirmed cases of Covid-19 in Indonesia was the Gegenbauer autoregressive moving average (GARMA) model.

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Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models

Cited by 17 publications

References 16 publications

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Simulation for predictive maintenance using weighted training algorithms in machine learning

Comparison of Arima and Garma's Performance on Data on Positive Covid-19 Cases in Indonesia

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