Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms

This work has been partially supported by the Ministry of Science and Technology under project TIN2017-89517-P, the Contract UGR-AM OTRI-4260 and the Andalusian Excellence project P18-FR-4961. J. Carrasco was supported by the Spanish Ministry of Science under the FPU Programme 998758-2016. D. Garcia-Gil holds a contract co-financed by the European Social Fund and the Administration of the Junta de Andalucia, reference DOC_01137.The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time series research as outlier and novelty detection or time series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios. To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROC, a generalisation for the calculation of ROC curves for time series scenarios. We also adapt the mechanism from a established time series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big Data algorithms with a real-world time series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method.Ministry of Science and Technology TIN2017-89517-PContract UGR-AM OTRI-4260Andalusian Excellence project P18-FR-4961Spanish Government 998758-2016European Social Fund (ESF)Junta de Andalucia DOC_0113