Neural Network Models for High-Precision Predictive Maintenance

Abstract

Predictive maintenance (PdM) has emerged as one of the key strategies in the contemporary industrial set-ups, with the ambition of improving the reliability of the machineries, minimizing downtimes, and optimizing the cost operational patterns. The conventional methods of maintenance such as preventive and corrective methods are usually reactive and ineffective leading to unnecessary costs, and breakdown of the system without any prior issues. This study reports on the use of highly-developed neural network (NN) models to reach the desired high-precision predictive maintenance by accurately predicting equipment failures and anomalies. We explore various types of neural networks, such as feedforward neural network (FNNs), recurrent neural networks (RNNs), and convolutional neural network (CNNs), to capture more complicated industrial data. It is proposed that the sensor data processing, feature extraction, and model training will be linked in a framework enabling the prediction of the failure events with a high degree of accuracy. Large-scale evaluations are performed on publicly available predictive maintenance data and the performance is measured in terms of accuracy, precision, recall, F1-score and mean absolute error (MAE). It has been found that deep learning models, especially LSTM-based RNNs, are more effective at revealing temporal correlations in time-series sensor data, thereby providing data with a better predictive aspect. This research gives a complete methodology of implementing neural network-based predictive maintenance systems such as the architecture, preprocessing of data techniques, hyperparameter optimization, and the evaluation of the model. The results support the fact that neural network models have a tremendous potential to revolutionize the predictive maintenance practice, which can be applied in practice by industrial practitioners and scholars. The suggested solution will help to reduce downtimes, make equipment operational longer, and decrease operations costs in industries significantly.