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Data-driven prognostics and health management is an important part of the future industry. It allows the detection of system faults and estimation of its remaining useful life (RUL) to anticipate failures and schedule appropriate prescriptive maintenance actions. Moreover, with the development of data acquisition tools in such industries, it is possible to collect large amount of data from similar systems, which prompts the use of machine learning algorithms for efficient estimation of the system RUL. However, due to different type of faults and degradation rates that can occur in real processes, there exists high variability of the end-of-life (¿¿¿) time of each system degradation trajectory, making more difficult to fix a failure threshold and consequently generate several uncertainties in the estimation of RUL. To address this situation, this paper proposes a new data-driven approach for estimating the system RUL when dealing with the variability of degradation trends and unknown failure thresholds. Particularly, the proposed approach combines two RUL techniques, recursive and direct RUL estimation. First, the historical collected raw data are fed into a processing algorithm to construct prognostic health indicators (HIs) and choose the one that characterizes well the system’s degradation trajectory. This latter indicator allows identifying the high and low ¿¿¿ amplitude values from the historical data and is used to build a recursive prediction model that estimates in long-term the degradation trend evolution. After that, the trained model forecasts every degradation trend which ¿¿¿ amplitude is less than the predefined high value. Thus, a set of possible RULs of each trajectory can be predicted. Finally, the ensemble of the derived RULs and their HI trajectories are fused to directly estimate the final RUL. The proposed approach is applied to a subway door system with multiple degradation scenarios while taking into account different operating conditions., The project has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain France Andorra programme (POCTEFA 2014–2020). POCTEFA aims to reinforce the economic and social integration of the French-Spanish-Andorran border. Its support is focused on developing economic, social and environmental cross-border activities through joint strategies favoring sustainable territorial development. Also, this work was supported by the ANR DATAZERO (contract ‘‘ANR-15-CE25-0012’’) project and by the EIPHI Graduate school (contract "ANR-19-CE25-0016")., Postprint (published version)

Maintenance is an important activity in industry. It is performed either to revive a machine/component or to prevent it from breaking down. Different strategies have evolved through time, bringing maintenance to its current state: condition-based and predictive maintenances. This evolution was due to the increasing demand of reliability in industry. The key process of condition-based and predictive maintenances is prognostics and health management, and it is a tool to predict the remaining useful life of engineering assets. Nowadays, plants are required to avoid shutdowns while offering safety and reliability. Nevertheless, planning a maintenance activity requires accurate information about the system/component health state. Such information is usually gathered by means of independent sensor nodes. In this study, we consider the case where the nodes are interconnected and form a wireless sensor network. As far as we know, no research work has considered such a case of study for prognostics. Regarding the importance of data accuracy, a good prognostics requires reliable sources of information. This is why, in this paper, we will first discuss the dependability of wireless sensor networks, and then present a state of the art in prognostic and health management activities.

In this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws. Furthermore, the networks' topology often changes, leading to a variability in quality of coverage in the targeted area. Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is ($1$) to show that random forests are relevant in this context, due to their flexibility and robustness, and ($2$) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network.

In condition-based maintenance, real-time observations are crucial for on-line health assessment. When the monitoring system is a wireless sensor network, data loss becomes highly probable and this affects the quality of the remaining useful life prediction. In this paper, we present a fully distributed algorithm that ensures fault tolerance and recovers data loss in wireless sensor networks. We first theoretically analyze the algorithm and give correctness proofs, then provide simulation results and show that the algorithm is (i) able to ensure data recovery with a low failure rate and (ii) preserves the overall energy for dense networks., Comment: The Computer Journal (accepted)