Bearing fault prognostics based on signal complexity and Gaussian process models

Standard bearing fault detection features are shown to be ineffective for estimating bearings remaining useful life (RUL). Addressing this issue, in this paper we propose an approach for bearing fault prognostics based on features describing the statistical complexity of the envelope of the generated vibrations and a set of Gaussian process (GP) models. The proposed feature set exhibits continuous trend which can be directly related to the deterioration of bearing condition. Gaussian process models are non-parametric black-box models which differ from most other frequently used black-box identification approaches as they search for the relationships among measured data rather than trying to approximate the modeled system by fitting the parameters of the selected basis functions. Their output is normal distribution, expressed in terms of mean and variance, which can be interpreted as a confidence in prediction. In this paper the GP models are used for filtering noisy features and estimating the RUL based on filtered features. The proposed approach was evaluated on the data set provided for the IEEE PHM 2012 Prognostic Challenge.