An efficient honey badger based Faster region CNN for chronc heart Failure prediction

Sherly, S. Irin; Mathivanan, G.

doi:10.1016/j.bspc.2022.104165

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Our future work will focus on fine-tuning the LSTM model and exploring different architectures (e.g., CNN-based models [87] and Gaussian Processes [88]) and hybrid models that combine multiple machine learning and deep learning techniques [89]. Such a comparison can help identify the most effective approach for different industrial scenarios and enable a better understanding of how different network structures and learning algorithms impact failure prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

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

Pinciroli Vago,

Forbicini,

Fraternali

2024

Preprint

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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 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.

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Section: Discussionmentioning

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

show abstract

“…Our future work will focus on fine-tuning the LSTM model and exploring different architectures (e.g., CNN-based models [92] and Gaussian Processes [93]), hybrid models that combine multiple machine learning and deep learning techniques [94], and digital twin-based approaches that exploit simulations of physical machines. The latter have shown good performance in diverse fields, including aeronautics [95,96], hydraulic systems [97], and facility management [98].…”

Section: Discussionmentioning

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

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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Interval-based multi-objective metaheuristic honey badger algorithm

Huang,

Zhou,

Zhou

et al. 2024

Soft Comput

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An efficient honey badger based Faster region CNN for chronc heart Failure prediction

Cited by 7 publications

References 28 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

Interval-based multi-objective metaheuristic honey badger algorithm

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