Deep feature learning network for fault detection and isolation

Prognostics and Health Management (PHM) approaches typically involve several signal processing and feature engineering steps. The state of the art on feature engineering, comprising feature extraction and feature dimensionality reduction, often only provides specific solutions for specific problems, but rarely supports transferability or generalization: it often requires expert knowledge and extensive intervention. In this paper, we propose a new integrated feature learning approach for jointly achieving fault detection and fault isolation in high-dimensional condition monitoring data. The proposed approach, based on Hierarchical Extreme Learning Machines (HELM) demonstrates a good ability to detect and isolate faults in large datasets comprising signals of different natures, non-informative signals, non-linear relationships and noise. The method includes stacked auto-encoders that are able to learn the underlying high-level features, and a one-class classifier to combine the learned features in an indicator that represents the deviation from the normal system behavior. Once a deviation is identified, features are used to isolate the most deviating signal components. Two case studies highlight the benefits of the approach: First, a synthetic dataset with the typical characteristics of condition monitoring data and different types of faults is applied to evaluate the performance with objective metrics. Second, the approach is tested on data stemming from a power plant generator interturn failure. In both cases, the results are compared to other commonly applied approaches for fault isolation

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions