Acoustic emission-based diagnostics and prognostics of slow rotating bearings using Bayesian techniques

Abstract

Diagnostics and prognostics in rotating machinery is a subject of much on-going research. There are three approaches to diagnostics and prognostics. These include experience-based approaches, data-driven techniques and model-based techniques. Bayesian data-driven techniques are gaining widespread application in diagnostics and prognostics of mechanical and allied systems including slow rotating bearings, as a result of their ability to handle the stochastic nature of the measured data well. The aim of the study is to detect incipient damage of slow rotating bearings and develop diagnostics which will be robust under changing operating conditions. Further it is required to explore and develop an optimal prognostic model for the prediction of remaining useful life (RUL) of slow rotating bearings. This research develops a novel integrated nonlinear method for the effective feature extraction from acoustic emission (AE) signals and the construction of a degradation assessment index (DAI), which is subsequently used for the fault diagnostics of slow rotating bearings. A slow rotating bearing test rig was developed to measure AE data under variable operational conditions. The proposed novel DAI obtained by the integration of the PKPCA (polynomial kernel principal component analysis), a Gaussian mixture model (GMM) and an exponentially weighted moving average (EWMA) is shown to be effective and suitable for monitoring the degradation of slow rotating bearings and is robust under variable operating conditions. Furthermore, this study integrates the novel DAI into alternative Bayesian methods for the prediction of RUL. The DAI is used as input in several Bayesian regression models such as the multi-layer perceptron (MLP), radial basis function (RBF), Bayesian linear regression (BLR), Gaussian mixture regression (GMR) and the Gaussian process regression (GPR) for RUL prediction. The combination of the DAI with the GPR model, otherwise, known as the DAI-GPR gives the best prediction. The findings show that the GPR model is suitable and effective in the prediction of RUL of slow rotating bearings and robust to varying operating conditions. Further, the models are also robust when the training and tests sets are obtained from dependent and independent samples. Finally, an optimal GPR for the prediction of RUL of slow rotating bearings based on a DAI is developed. The model performance is evaluated for cases where the training and test samples from cross validation approach are dependent as well as when they are independent. The optimal GPR is obtained from the integration or combination of existing simple mean and covariance functions in order to capture the observed trend of the bearing degradation as well as the irregularities in the data. The resulting integrated GPR model provides an excellent fit to the data and improvements over the simple GPR models that are based on simple mean and covariance functions. In addition, it achieves a near zero percentage error prediction of the RUL of slow rotating bearings when the training and test sets are from dependent samples but slightly different values when the estimation is based on independent samples. These findings are robust under varying operating conditions such as loading and speed. The proposed methodology can be applied to nonlinear and non-stationary machine response signals and is useful for preventive machine maintenance purposes. Keywords: acoustic emission, Bayesian linear regression, Bayesian techniques, covariance function, data-driven, degradation assessment index, diagnostics, experience-based, exponentially weighted moving average, Gaussian mixture model, Gaussian mixture regression, Gaussian process regression, integration, mean function, model-based, multi-layer perceptron, polynomial kernel principal component analysis, prognostics, radial basis function, remaining useful life.