A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers Giacomo Leone a,⇑ , Loredana Cristaldi a , Simone Turrin b a Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy b ABB AG, Corporate Research Center, Wallstadter Str. 59, 68526 Ladenburg, Germany article info Article history: Received 25 October 2016 Received in revised form 18 January 2017 Accepted 9 February 2017 Available online xxxx Keywords: Data-driven Industrial circuit breakers Prognostics Remaining useful life Sub-fleet Statistical test abstract In this paper, a data-driven prognostic algorithm for the estimation of the Remaining Useful Life (RUL) of a product is proposed. It is based on the acquisition and exploitation of run-to-failure data of homoge- neous products, in the followings referred as fleet of products. The algorithm is able to detect the set of products (sub-fleet of products) showing highest degradation pattern similarity with the one under study and exploits the related monitoring data for a reliable prediction of the RUL. In particular, a novel methodology for the sub-fleet identification is presented and compared with other solution found in lit- erature. The results obtained for a real application case as Medium and High Voltage Circuit Breaker, have shown a high prognostic power for the algorithm, which therefore represents a potential tool for an effec- tive Predictive Maintenance (PdM) strategy. Ó 2017 Elsevier Ltd. All rights reserved. 1. Introduction The Remaining Useful Life (RUL) of a system is defined as the useful life left at a particular time instant, that is the remaining time interval in which it will be able to meet its operating require- ments. RUL estimation represents the core of the Prognostics and Health Management (PHM) programs which aim to a reduction of maintenance and life-cycle management costs, an increase of the systems availability and the adoption of Predictive Mainte- nance (PdM) strategies [1,2]. In the literature, the prognostic algorithms for the RUL estima- tion are usually classified in three different categories. The first class is related to the model-based approaches that refer to phys- ical models describing the behavior of the systems under study. Such models can be very accurate but often require a strong and detailed knowledge of the inherent physics-of-failure. It follows that they are often very specific to the case study and their imple- mentation is not always possible. On the other hand, data-driven algorithms, the second main cat- egory found in the literature, are mainly based on the exploitation of the collected run-to-failure data and usually do not require particu- lar knowledge about the inherent failure mechanisms. They provide a good trade-off between model complexity and results accuracy. Finally, hybrid approaches attempt to leverage the advantages of combining the prognostics models in the aforementioned differ- ent categories for RUL prediction. In the last years, data-driven approaches have experienced a wide diffusion. One reason is their suitability for applications related to complex engineered systems for which the definition of analytical models is a complex and resources demanding task. Another factor is the increasing availability of cheap monitoring systems that allow the collection of condition monitoring data in substantial quantities. A complete and detailed review about them is given in [3]. The same authors of this paper already presented two data- driven prognostic algorithms, one based on the statistical extrac- tion and exploitation through Monte Carlo (MC) simulations of reli- ability and maintenance knowledge [4] and one based on a Machine Learning solution, in particular a Neural Network (NN) architecture [5]. A key and novel differentiator of the proposed approaches was the concept of fleet of products. A fleet of products is a set of homogenous products, with respect to the function for which they are intended, clustered together following different possible crite- ria such as belonging to the same customer, being installed in the same region or same industrial application and so on. The advan- tage of this practice is the possibility to extract fleet-specific usage and degradation profiles that can be exploited for the RUL predic- tion of a specific element (i.e. a specific product) of the selected fleet. In particular, the contribution of the acquisition of knowledge at fleet level on the improvement of the prognostic ability is quite http://dx.doi.org/10.1016/j.measurement.2017.02.017 0263-2241/Ó 2017 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail addresses: giacomo.leone@polimi.it (G. Leone), loredana.cristaldi@polimi. it (L. Cristaldi), simone.turrin@de.abb.com (S. Turrin). Measurement xxx (2017) xxx–xxx Contents lists available at ScienceDirect Measurement journal homepage: www.elsevier.com/locate/measurement Please cite this article in press as: G. Leone et al., A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers, Measurement (2017), http://dx.doi.org/10.1016/j.measurement.2017.02.017