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

Statistical Wiener process model for vibration signals in accelerated aging processes of electric motors

This research describes random process modeling of the accelerated aging process based upon the mechanical degradation in induction motors. In order to show this aging effect, vibration measurements are considered at the end of each aging cycle, which gradually cause to bearing damage in the motor. In this manner, the accelerated aging study comprises seven aging stage sequentially and collected data set is presented as seven aging cycles and one initial cycle. Hence total aging process is represented by a set of the sequential vibration signals for initial and aged cases. Since the vibration signals are random values which represent the Gaussian distribution character in each period of measurement and this character can be conveyed to the next stage with a scaling of the signal related to elapsed time, the process reminds the Brownian motion or the so-called random walk and it is expected that the degradation can be described as a Wiener process. Examining the collected data proves that in the statistical manner, this compact data set reflects the properties of a non-stationary random process and it is expressed by the Wiener Process Model (WPM) as a statistical approach. This property of the process will be helpful to estimate the residual useful life (RUL) of the bearings in induction motors with high accuracy

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

The latent state hazard model, with application to wind turbine reliability

We present a new model for reliability analysis that is able to distinguish the latent internal vulnerability state of the equipment from the vulnerability caused by temporary external sources. Consider a wind farm where each turbine is running under the external effects of temperature, wind speed and direction, etc. The turbine might fail because of the external effects of a spike in temperature. If it does not fail during the temperature spike, it could still fail due to internal degradation, and the spike could cause (or be an indication of) this degradation. The ability to identify the underlying latent state can help better understand the effects of external sources and thus lead to more robust decision-making. We present an experimental study using SCADA sensor measurements from wind turbines in Italy.Comment: Published at http://dx.doi.org/10.1214/15-AOAS859 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org