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

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.