Predictive maintenance scheduling for multiple power equipment based on data-driven fault prediction

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

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Data-Driven Predictive Maintenance: A Methodology Primer

10.1007/978-981-16-2940-2_3 ◽

2021 ◽

pp. 39-73

Author(s):

Tania Cerquitelli ◽

Nikolaos Nikolakis ◽

Lia Morra ◽

Andrea Bellagarda ◽

Matteo Orlando ◽

...

Keyword(s):

Predictive Maintenance ◽

Data Driven

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Anomaly detection and event mining in cold forming manufacturing processes

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06156-2 ◽

2020 ◽

Author(s):

Diego Nieves Avendano ◽

Daniel Caljouw ◽

Dirk Deschrijver ◽

Sofie Van Hoecke

Keyword(s):

Anomaly Detection ◽

Missing Values ◽

Acoustic Emissions ◽

Irregular Sampling ◽

Predictive Maintenance ◽

Data Driven ◽

Normal Operation ◽

Cold Forming ◽

Event Prediction ◽

Event Mining

Abstract Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.

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Pipe Fault Prediction for Water Transmission Mains

Water ◽

10.3390/w12102861 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2861

Author(s):

Ariel Gorenstein ◽

Meir Kalech ◽

Daniela Fuchs Hanusch ◽

Sharon Hassid

Keyword(s):

Prediction Model ◽

Real World ◽

Fault Prediction ◽

Data Driven ◽

Segmentation Method ◽

Reliable Prediction ◽

Rule Based ◽

Maintenance Strategies ◽

Water Transmission ◽

Proactive Maintenance

Every network of supply waterlines experiences thousands of yearly bursts, breaks, leakages, and other failures. These failures waste a great amount of resources, as not only the waterlines need to be repaired, but also water is wasted and the distribution service is interrupted. For that reason, many water facilities employ proactive maintenance strategies in their networks, where they replace likely-to-fail pipes in advance to prevent the failures. In this paper, we aim to establish a reliable prediction model that can accurately predict faults in waterlines prior to their occurrence. We propose a specific segmentation method for long transmission mains, as well as three data-driven models and one rule-based prediction model. We evaluate a real world waterline network used in Israel, operated by Mekorot company, using three common metrics. The results show that the data-driven algorithms outperform the rule-based model by at least 5% in each of the metrics. Additionally, their prediction becomes more accurate as they are trained with more data, but enhancing these data with geographically related features does not improve the accuracy further.

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