MuLDOM: Forecasting Multivariate Anomalies on Edge Devices in IIoT Using Multibranch LSTM and Differential Overfitting Mitigation Model

Abstract

In the Industrial Internet of Things (IIoT) environment, there is a multitude of heterogeneous industrial edge devices (IEDs) from various sources. Real-time monitoring and precise prediction of its operational status are typically essential. However, existing deep learning-based models often encounter overfitting issues due to complex parameter configurations. Furthermore, ensuring the comprehensive performance of anomaly event forecasts for IEDs has emerged as a pressing issue requiring resolution to accommodate a wider range of practical applications. In this article, we introduce a novel multibranch long short term memory and differential overfitting mitigation scheme (MuLDOM). This scheme is designed to achieve two primary objectives: 1) to extract features and denoise multivariate time series adaptively and 2) to implement the differential overfitting mitigation algorithm for the first time, thereby enabling robust intelligent anomaly detection and forecast (IADF). Expanding on this framework, we provide detailed information on the development of an online prediction scoring mechanism based on multivariate time series data. This mechanism aims to enhance the efficiency of quantitatively estimating the spatial and temporal characteristics associated with IEDs. We conducted extensive experiments on four publicly available industrial data sets and compared our approach with nine recent baseline methods. The results indicate that our method surpasses the recent state-of-the-art methods, validating its effectiveness. These findings underscore its significant potential for real-world applications.