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

Abstract

With the rapid development of Industrial Internet of Things (IIoT), the category and quantity of industrial equipment will increase gradually. For the centralized monitoring and management of numerous and multivariate equipment in the intelligent manufacturing process, the equipment categories shall be identified first. However, manual labeling electrical equipment needs high costs. For the purpose of recognizing electrical equipment accurately in manufacturing system, this study adopts the Recurrent Neural Network (RRN) Long Short-Term Memory (LSTM) to build a NonIntrusive Load Monitoring (NILM) system, and combines edge computing to implement parallel computing to practice the effect of power equipment identification. Considering the practical popularity, the fairly priced low frequency Smart Meter is used to collect appliance data. According to proposed optimal adjustment strategy of parameter model, the average random recognition rate can be achieved 88%, the average recognition rate of the continuous data of a single electrical equipment can be achieved 83.6%.