Prediction of anomalous behavior in industrial assets based on sensor reading represents a key focus in modern business practice. As a matter of fact, forecast of forthcoming faults is crucial to implement predictive maintenance, i.e. maintenance decision making based on real time information from components and systems, which allows, among other benefits, to reduce maintenance cost, minimize downtime, increase safety, enhance product quality and productivity. However, building a model able to predict the future occurrence of a failure is challenging for various reasons. First, data are usually highly imbalanced, meaning that patterns describing a faulty regime are much less numerous than normal behavior instances, which makes model design difficult. Second, model predictions should be not only accurate (to avoid false alarms and missed detections) but also explainable to operators responsible for scheduling maintenance or control actions. In this paper we introduce a method called Interpretable Anomaly Prediction (IAP) allowing to handle these issues by using regularized logistic regression as core prediction model. In particular, in contrast to anomaly detection algorithms which permit to identify if the current data are anomalous or not, the proposed technique is able to predict the probability that future data will be abnormal. Furthermore, feature extraction and selection mechanisms give insights on the possible root causes leading to failures. The proposed strategy is validated with a large imbalanced multivariate time-series dataset consisting of measurements of several process variables surrounding an high pressure plunger pump situated in a complex chemical plant.

Interpretable Anomaly Prediction: Predicting anomalous behavior in industry 4.0 settings via regularized logistic regression tools

Cuzzocrea A.;
2020-01-01

Abstract

Prediction of anomalous behavior in industrial assets based on sensor reading represents a key focus in modern business practice. As a matter of fact, forecast of forthcoming faults is crucial to implement predictive maintenance, i.e. maintenance decision making based on real time information from components and systems, which allows, among other benefits, to reduce maintenance cost, minimize downtime, increase safety, enhance product quality and productivity. However, building a model able to predict the future occurrence of a failure is challenging for various reasons. First, data are usually highly imbalanced, meaning that patterns describing a faulty regime are much less numerous than normal behavior instances, which makes model design difficult. Second, model predictions should be not only accurate (to avoid false alarms and missed detections) but also explainable to operators responsible for scheduling maintenance or control actions. In this paper we introduce a method called Interpretable Anomaly Prediction (IAP) allowing to handle these issues by using regularized logistic regression as core prediction model. In particular, in contrast to anomaly detection algorithms which permit to identify if the current data are anomalous or not, the proposed technique is able to predict the probability that future data will be abnormal. Furthermore, feature extraction and selection mechanisms give insights on the possible root causes leading to failures. The proposed strategy is validated with a large imbalanced multivariate time-series dataset consisting of measurements of several process variables surrounding an high pressure plunger pump situated in a complex chemical plant.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/312334
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