What question did this study set out to answer?

The aim is to analyze deep learning applications for predictive maintenance in the context of Industry 4.0.

May 7, 2026Concurrent Engineering0 citations

Deep learning models for predictive maintenance in Industry 4.0: An analysis

BVBansi VyasIndian Institute of Information Technology Vadodara PPPragya PranatiIndian Institute of Information Technology Vadodara GPGaurav PareekIndian Institute of Information Technology Vadodara

Key Points

The aim is to analyze deep learning applications for predictive maintenance in the context of Industry 4.0.
Examined a water pump time series dataset using five deep learning models
Compared LSTM, CNN-LSTM, GAF-CNN, BiLSTM, and Time-Series Transformer
Evaluated models based on architecture components and prediction accuracy
The hybrid model combining time series and transformer showed superior prediction accuracy
Demonstrated strong interpretability and balanced performance compared to other models

Abstract

This paper focuses on exploring the use cases and practical applicability of deep learning in Industry 4.0 by studying on a water pump time series dataset with 5 models namely LSTM, CNN-LSTM, GAF-CNN, BiLSTM and Time-Series Transformer. The unplanned downtime due to sudden equipment failure costs the industry huge losses every year. The proposed methodology based on deep learning architectures uses sensor readings and leads to meaningful predictions for cost-cutting and time saving. The study evaluates and compares these models in terms of fine-grained architecture-level components and prediction accuracy. The results demonstrated that the transformer based time series hybrid model is more accurate in prediction with balanced performance and strong interpretability than other models.

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