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Nonparametric time series modelling for industrial prognostics and health management

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Abstract

Prognostics and health management (PHM) methods aim at detecting the degradation, diagnosing the faults and predicting the time at which a system or a component will no longer perform its desired function. PHM is based on access to a model of a system or a component using one or combination of physical or data-driven models. In physical-based models, one has to gather a lot of knowledge about the desired system and then build an analytical model of the system function of the degradation mechanism that is used as a reference during system operation. On the other hand, data-driven models are based on the exploitation of symptoms or indicators of degradations using statistical or artificial intelligence methods on the monitored system once it is operational and learn the normal behaviour. Trend extraction is one of the methods used to extract important information contained in the sensory signals, which can be used for data-driven models. However, extraction of such information from the collected data in a practical working environment is always a great challenge as sensory signals are usually multidimensional and obscured by noise. Also, the extracted trends should represent the nominal behaviour of the system as well as the health status evolution. This paper presents a method for nonparametric trend modelling from multidimensional sensory data so as to use such trends in machinery health prognostics. The goal of this work is to develop a method that can extract features representing the nominal behaviour of the monitored component, and from these features, smooth trends are extracted to represent the critical component’s health evolution over the time. The proposed method starts by multidimensional feature extraction from machinery sensory signals. Then, unsupervised feature selection on the features’ domain is applied without making any assumptions concerning the number of the extracted features. The selected features can be used to represent the nominal behaviour of the system and hence detect any deviation. Then, empirical mode decomposition algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Finally, ridge regression is applied to the extracted trend for modelling and can be used later for the remaining useful life prediction. The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and another data set downloaded from NASA repository where it is shown to be able to extract signal trends.

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Authors and Affiliations

  1. FEMTO-ST Institute, AS2M Department, University of Franche-Comté/CNRS/ENSMM/UTBM, 24 rue Alain Savary, 25000, Besançon, France

    Ahmed Mosallam, Kamal Medjaher & Noureddine Zerhouni

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  1. Ahmed Mosallam
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  2. Kamal Medjaher
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  3. Noureddine Zerhouni
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Correspondence to Kamal Medjaher.

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Mosallam, A., Medjaher, K. & Zerhouni, N. Nonparametric time series modelling for industrial prognostics and health management. Int J Adv Manuf Technol 69, 1685–1699 (2013). https://doi.org/10.1007/s00170-013-5065-z

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  • DOI: https://doi.org/10.1007/s00170-013-5065-z

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