Framework for the Identification of Rare Events via Machine Learning and IoT Networks

Abstract

This paper introduces an industrial cyber-physical system (CPS) based on the Internet of Things (IoT) that is designed to detect rare events based on machine learning. The framework follows the following three generic steps: (1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g., aggregator, central controller); (2) Big data fusion: The intelligent node uses machine learning techniques (e.g., data clustering, neural networks) to convert the received ("big") data to useful information to guide short-term operational decisions related to the physical process; (3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analyzed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. Our proposed general framework, which relies on an IoT network, aims at an ultra-reliable detection/prevention of rare events related to a predetermined industrial physical process (modelled by a particular signal). The framework will be processindependent, however, our demonstrated solution will be designed case-by-case. This paper is an introduction to the solution to be developed by the FIREMAN consortium.

Fault detection and classification in Industrial IoT in case of missing sensor data

“…Difficulties related to these tasks originate from the fact that anomalies are rare events within datasets, making it difficult to apply most of the existing algorithms which result in either false alarms or misdetections [13], [14]. The authors of [8] made an attempt at providing a general framework to model a wide range of cases based on the advances in IIoT networks and Machine Learning (ML) algorithms. Similarly, the authors of [15], [16] describe several deployed solutions of cyber-physical systems in an industrial environment.…”

“…This analysis showed that only 12 sensors have a significant impact on the fault classification mechanism. The set S C of critical sensors is 8,12,17,18,20,21,43,44,49, 50, 51}. Some of these sensors impact multiple faults (e.g.…”

“…The work proposed in this article is based on the FIRE-MAN project funded by CHIST-ERA, which focuses on modelling and analysing IIoT networks based on specific machine learning algorithms capable of detecting rare events in industrial setups in an ultra-reliable way [8]. An important M. Dzaferagic, N. Marchetti, and I. Macaluso are with CONNECT centre, Trinity College Dublin, Ireland.…”

Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data

“…The work proposed in this article is based on the FIREMAN project funded by the CHIST-ERA programme, which focuses on modelling and analysing IIoT networks based on specific machine learning algorithms capable of detecting rare events in industrial setups in an ultra-reliable way [8]- [10] important aspect in assuring such ultra-reliability in the IIoT is how to guarantee we have a functioning system in place, even in case some of the measurements are missing due to network or hardware issues. In fact values are often missing from the collected sensor data, and the related issue of missing value imputation becomes then very important.…”

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

“…In this paper we deal with this problem by proposing a general frame to model a wide range of cases based on the advances in Industrial Internet of Things (IIoT) networks and Machine Learning (ML) algorithms [3]. In particular, we approach the problem by using a theory that considers three autonomous (but strongly dependent) layers of cyber-physical systems (CPS), namely physical, data and regulatory.…”