Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms. Coelho, D., Costa, D., Rocha, E. M., Almeida, D., & Santos, J. P. Procedia Computer Science, 200:1184–1193, January, 2022. ![Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection; and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch Thermo Technology, is discussed.
@article{coelho_predictive_2022,
series = {3rd {International} {Conference} on {Industry} 4.0 and {Smart} {Manufacturing}},
title = {Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms},
volume = {200},
issn = {1877-0509},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922003271},
doi = {10.1016/j.procs.2022.01.318},
abstract = {Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection; and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971\% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96\% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch Thermo Technology, is discussed.},
language = {en},
urldate = {2022-03-14},
journal = {Procedia Computer Science},
author = {Coelho, Daniel and Costa, Diogo and Rocha, Eugénio M. and Almeida, Duarte and Santos, José P.},
month = jan,
year = {2022},
keywords = {Anomaly Detection, Machine Learning, Predictive Maintenance, Time Segmentation},
pages = {1184--1193},
}
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