DEEP NEURAL NETWORKS FOR PREDICTIVE MAINTENANCE

At present, deep neural networks are becoming one of the most popular approaches in solving various practical problems from a wide variety of fields, such as image and speech recognition, natural language processing, computer vision, medical informatics, etc. The article considers the possibility of using deep neural networks in the implementation of proactive maintenance strategy – predictive maintenance (PdM). Various methods of constructing predictive models for PdM are considered. Currently, the data-driven approaches using deep neural networks for constructing predictive models for PdM are most promising methods. One of the reasons for the successful application of deep neural networks is that the networks automatically selects important features from the data needed to solve the problem. The most commonly used neural networks for PdM are considered: Long short-term memory (LSTM), convolutional neural networks (CNN) and autoencoders. An overview of powerful frameworks for the design and training of neural networks is given.

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