A Bayesian hierarchical model (BHM) is developed to predict bearing life using envelope acceleration data in combination with a degradation model and prior knowledge of the bearing rating life. The BHM enables the inference of individual bearings, groups of bearings, or bearings operating under certain conditions. The key benefit of the BHM approach is that the relationships between the bearing model parameters and their prior distributions can be expressed at different hierarchical levels. We begin our analysis using a bearing rating life calculation L10h and an estimate of its associated failure time distribution. Realistic variations to constrain our prior distribution of the failure time are then applied before measurements are available. When data become available, estimates more representative of our specific batch and operating conditions are inferred, both on the individual bearing level and the bearing group level. The proposed prognostics methodology can be used in situations with varying amounts of data. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.

The prediction of the remaining useful life of electronic components is a crucial aspect for predictive maintenance and system reliability across multiple fields and applications. Data-driven approaches, particularly those methods based on machine learning, are currently being used due to their ability to model complex degradation patterns without the need for explicit physical modeling. However, several challenges are still present, such as the availability and quality of data and the uncertainty quantification of the results. To tackle these obstacles, this work explores the use of synthetic data for augmenting datasets, as well as feature selection and the assessment of different neural network architectures, including models with recurrent layers and probabilistic output. The proposed approach was evaluated on a silicon carbide metal-oxide- semiconductor field-effect transistor dataset. The best results for modeling the remaining useful life of these devices were obtained with a model trained using an augmented dataset with synthetic data. This model’s probabilistic output allows building a confidence interval for the predictions, which is helpful to identify outliers. This model outperformed other state-of-the-art algorithms using only 4 out of 22 features, demonstrating the effectiveness of the feature selection procedure, the data augmentation method, and the neural network architecture for this case study.