A2-LSTM for predictive maintenance of industrial equipment based on machine learning

Predictive maintenance (PdM) is a prominent anomaly prediction strategy in the manufacturing system given the increasing need to minimize downtime and economic losses. It is available for PdM to monitor industrial equipment continuously with smart electrical sensors and predict the health condition with machine learning algorithms. However, the performance of previous algorithms is often limited by lacking consideration of both attribute contribution to final results and temporal dependence. To solve the problem, this article introduces a general PdM framework based on Internet-of-Things technology, cloud computing, and total productive maintenance. In this framework, an attribute attentioned long short-term memory network (A2-LSTM) is proposed. The A2-LSTM takes a sequence of electrical records as input to extract attributes. Afterwards, different attributes are fused into the attribute attention network, which can adjust the importance of each attribute automatically. Next, the reweighted attributes are fed into the health prediction component to establish temporal dependence for the manufacturing system. Finally, the output of A2-LSTM, i.e., remaining useful life, can support workers to carry out equipment maintenance. The effectiveness of the method is verified by real-world cases and the comparison results show that the A2-LSTM is promising.