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Predictive Maintenance of Industrial Equipment using Deep Learning: from sensory data to remaining useful life estimation

The ability to monitor the machine component degradation increases the reliability and minimizes downtime due to failure. An accurate prediction of the remaining useful life is crucial and is used to determine maintenance windows to preempt unforeseen downtime. Many models and comparisons have been investigated and proposed in the literature, but they are usually focused rather on predictive accuracy than on practical use and robust applications. In order to study the real effectiveness of the remaining useful life estimation, it is recommended to apply these methods on real production line data, but few of the existing works consider this important aspect. This work applies a deep learning model approach to estimate equipment remaining useful life from typical raw sensory data of equipment monitored over a time period. The convolutional neural network is highlighted and a performance comparison is realized with a recurrent neural network architecture and a multilayer perceptron.