Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning

Abstract

This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed.

Information Processing and Data Visualization in Networked Industrial Systems

“…Despite its generality, the proposed information processing and data visualization framework is designed to improve the operation of industrial plants. The modular incorporation of networking, computing and decision-making components aims at bridging the gap between the physical and digital worlds, and extends the preliminary conceptualization introduced in [19], as illustrated in Fig. 6.…”

“…The three architectural layers of an IIoT system are abstractions but can be easily particularized by defining the boundary conditions of the actual use case to be studied, as carefully described in [19]. The idea is that the data flows from physical processes through a well-defined acquisition method to produce a dataset (including fused, aggregated and/or imputed measurements) that will be used by a decisionmaking process related to possible actions to be taken for the operation of industrial plants.…”

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber‐physical systems

“…We continue our literature analysis by pausing at Gutierrez‐Rojas et al (2020), which proposes a framework capable of providing effective anomaly detection rates with a clear orientation towards generalized application in various domains. The solution is based on a three‐layer architecture: physical layer, data layer and decision layer.…”

Advancing Spectrum Anomaly Detection through Digital Twins

“…Anomaly detection aims to identify such samples to recognize unexpected changes in a system. However, due to the complexity of modern communication systems and the rarity of anomalies, developing a robust detection mechanism is challenging [6].…”