PULSE: Proactive uncovering of latent severe anomalous events in IIoT using LSTM-RF model

Abstract

In the IIoT, billions of devices continually provide information that is extremely diverse, variable, and large-scale and presents significant hurdles for interpretation and analysis. Additionally, issues about data transmission, scaling, computation, and storage can result in data anomalies that significantly affect IIoT applications. This work presents a novel anomaly detection framework for the IIoT in the context of the challenges posed by vast, heterogeneous, and complex data streams. This paper proposes a two-staged multi-variate approach employing a composition of long short-term memory (LSTM) and a random forest (RF) Classifier. Our approach leverages the LSTM’s superior temporal pattern recognition capabilities in multi-variate time-series data and the exceptional classification accuracy of the RF model. By integrating the strengths of LSTM and RF models, our method provides not only precise predictions but also effectively discriminates between anomalies and normal occurrences, even in imbalanced datasets. We evaluated our model on two real-world datasets comprising periodic and non-periodic, short-term, and long-term temporal dependencies. Comparative studies indicate that our proposed method outperforms well-established alternatives in anomaly detection, highlighting its potential application in the IIoT environment.