Version 1
: Received: 15 April 2024 / Approved: 17 April 2024 / Online: 17 April 2024 (11:41:43 CEST)
How to cite:
Pinciroli Vago, N.O.; Forbicini, F.; Fraternali, P. Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study. Preprints2024, 2024041111. https://doi.org/10.20944/preprints202404.1111.v1
Pinciroli Vago, N.O.; Forbicini, F.; Fraternali, P. Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study. Preprints 2024, 2024041111. https://doi.org/10.20944/preprints202404.1111.v1
Pinciroli Vago, N.O.; Forbicini, F.; Fraternali, P. Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study. Preprints2024, 2024041111. https://doi.org/10.20944/preprints202404.1111.v1
APA Style
Pinciroli Vago, N.O., Forbicini, F., & Fraternali, P. (2024). Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study. Preprints. https://doi.org/10.20944/preprints202404.1111.v1
Chicago/Turabian Style
Pinciroli Vago, N.O., Francesca Forbicini and Piero Fraternali. 2024 "Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study" Preprints. https://doi.org/10.20944/preprints202404.1111.v1
Abstract
Non-neural Machine Learning (ML) and Deep Learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM and Transformers) are compared using multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
