SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

Abstract

In this article, a bi-directional long-short term memory (BiLSTM) network algorithm combined with a support vector machine (SVM), SVM-BiLSTM, is proposed to detect faults in the Gas Station Internet of Things (GS-IoT) system. The operational process data in the GS-IoT System, which is collected from the edge of the IoT gateways, is compared with the human emotional reaction behavioral mechanism data. A word segmentation method is invented to map the collected data to a low dimensional space, which makes the data processing relatively easier while retaining the intrinsic information of the data. In order to deal with a certain correlation among the data of the GS-IoT system, the BiLSTM algorithm is used to analyze the abnormal data and find types of faults. Since the structure of the BiLSTM is complex and its calculation is slow, we design a novel method which leverages SVM to increase the detection efficiency. We also compare the performance of the proposed algorithm with Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Knowledge-Based System (KBS), pure SVM and BiLSTM. The results show that the proposed algorithm is able to detect faults with more efficiency and accuracy in the GS-IoT system.

Keywords

• Deep learning
• SVM-BiLSTM
• fault detection
• sentiment analysis
• IoT system