Abstract
AbstractAnomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.
Highlights
Modern industrial asset operations are monitored in real time using a plethora of embedded sensors
This paper addresses the above problem by using a hierarchical model for the asset fleet that systematically identifies similar assets, and formulates higher level distributions of the asset level parameters
For the assets belonging to the low data category, the classifiers obtained using hierarchical modeling show significantly higher area under the ROC curve (AUC) and lower variances than the independent models learning from their own data
Summary
Modern industrial asset operations are monitored in real time using a plethora of embedded sensors. Availability of asset condition time series combined with readily available computing power and communication technologies has extensively automated industrial operations in the recent decade (Xu et al, 2014; Gilchrist and Gilchrist, 2016). As a part of asset health management, detecting anomalies in an asset’s condition data is critical for accurate prognosis. Identifies deviations in real time, and activates the prognosis algorithm to plan timely maintenance. Accurate anomaly detection enables efficient extraction of the failure trajectories from historical condition data. Failure trajectories are the time series ranging from the asset’s deviation from normal behavior till its failure. Since historical failure trajectories constitute the training dataset for prognosis, learning capabilities of the prognosis models primarily depend on accurate anomaly detection. An inefficient anomaly detection algorithm instead could let a failure go undetected, or flag many anomalies that turn out to be benign and not require any intervention (Kang, 2018)
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