A data-driven failure prognostics method based on mixture of gaussians hidden markov models

Diego Tobon-Mejia (1) , Kamal Medjaher (1) , Noureddine Zerhouni (1) , Gérard Tripot (2)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 Research and Development Department - Alstom Transport - Research and Development Department - Alstom Transport

This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the "NASA prognostics data repository" related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance.

Mots clés: Prognostics and health management; condition monitoring; hidden Markov; remaining useful life.; remaining useful life

Domaines: Automatique / Robotique

Diego Tobon-Mejia, Kamal Medjaher, Noureddine Zerhouni, Gérard Tripot. A data-driven failure prognostics method based on mixture of gaussians hidden markov models. IEEE Transactions on Reliability, 2012, 61 (2), pp.491-503. ⟨10.1109/TR.2012.2194177⟩. ⟨hal-00737585⟩