Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System

Abstract

With the rapid development of the Industrial Internet of Things (IIoT) and edge computing techniques, in situ intelligent sensors are continuously generating increasing and vast amounts of time-series data. In many industrial applications, particularly highly distributed systems positioned in remote areas, repeated transmission of large amounts of raw data onto the remote server is not possible. This poses a significant challenge to the timely processing of these data in IIoT. Analyzing and processing all the raw data remotely in the cloud server is impractical and has very low efficiency owing to network latency and the limited cloud computing resources. Failure of detecting abnormal data may result in major production safety problems. Therefore, businesses are moving machine learning capabilities to the edge to enable real-time actions in the field. In this study, we present a machine-learning-based edge-cloud framework to solve this problem. First, robust random cut forest and isolation forest algorithms are employed at the edge gateway to the collected data for the detection of anomalously changing data. Subsequently, these preprocessed time-series data are transmitted to cloud services for data trend prediction and missing data completion using the long short-term memory recurrent neural network method feed in conjunction with the original sequence of historical data combined with the first-order forward difference data. The experimental results show that the machine-learning-based edge-cloud-assisted oil production IIoT system can improve substantially the efficiency and accuracy of time-series data analyses.