Fault Prediction in Electric Power Communication Network Based on Improved DenseNet

Gao, Peng; Guan, Li; Hao, Jiakai; Chen, Qian; Yang, Yang; Qu, Zhenying; Jin, Ming

doi:10.1109/bmsb58369.2023.10211144

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Transformer-based architectures have been applied to anomaly detection [57,58], and fault diagnosis [59,60]. The work in [61] shows the effectiveness of a Transformer-based architecture for the fault prediction task in an Electric Power Communication Network. In contrast to LSTM-based architectures, Transformers can capture long-range dependencies and offer parallel processing capabilities, which also makes them suitable for time series tasks over long intervals.…”

Section: Related Workmentioning

confidence: 99%

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Pinciroli Vago,

Forbicini,

Fraternali

2024

Machines

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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 datasets 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 on 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.

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Section: Related Workmentioning

confidence: 99%

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Pinciroli Vago,

Forbicini,

Fraternali

2024

Machines

View full text Add to dashboard Cite

show abstract

“…Transformerbased architectures have been applied to anomaly detection [57,58], and fault diagnosis [59,60]. The work in [61] shows the effectiveness of a Transformer-based architecture for the fault prediction task in an Electric Power Communication Network. In contrast to LSTM-based architectures, Transformers can capture long-range dependencies and offer parallel processing capabilities, which makes them suitable also for time series tasks over long intervals.…”

Section: Introductionmentioning

confidence: 99%

Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study

Pinciroli Vago,

Forbicini,

Fraternali

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Fault Prediction in Electric Power Communication Network Based on Improved DenseNet

Cited by 2 publications

References 10 publications

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study

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