Leveraging Deep Learning for Multimodal Predictive Maintenance: A Hybrid CNN-LSTM Approach for Enhanced Equipment Failure Prediction in Automated Assembly Lines

The exploration of deep learning techniques for multimodal predictive maintenance in automated production lines is undertaken, presenting a hybrid model that synergizes Convolutional Neural Networks (CNNs) for visual analytics and Long Short-Term Memory (LSTM) networks for temporal sensor data processing. The CNN is designed to pinpoint signs of deterioration and anomalies within visual inputs, while the LSTM extracts predictive patterns from sensor data that indicate equipment failure. Drawing upon a blend of publicly accessible sensor datasets from reputable sources such as the UCI Machine Learning Repository and Kaggle, enriched by an exclusive collection of imagery from a diverse set of 100 Chinese assembly plants, the integrated analysis from both models results in a comprehensive predictive mechanism for equipment malfunction. This pioneering method is intended to enhance the operational efficiency and precision of automated assembly processes through the empowerment of pre-emptive maintenance actions.