LSTM and Edge Computing for Big Data Feature Recognition of Industrial Electrical Equipment

With the rapid development of Industrial Internet of Things, the category and quantity of industrial equipment will increase gradually. For centralized monitoring and management of numerous and multivariate equipment in the intelligent manufacturing process, the equipment categories shall be identified first. However, manual labeling of electrical equipment needs high costs. For the purpose of recognizing industrial equipment accurately in manufacturing systems, this study adopts the long short-term memory to analyze big data features and build a nonintrusive load monitoring system. Edge computing is used to implement parallel computing to improve the efficiency of equipment identification. Considering the practical popularity, the fairly priced low-frequency Smart Meter is used to collect the appliance data. According to the proposed optimal adjustment strategy of parameter model, the average random recognition rate can achieve 88% and the average recognition rate of the continuous data of a single electrical equipment can achieve 83.6%.