Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Abstract

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines.

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

“…Compared with traditional maintenance technologies, wind power intelligent O&M technology uses big data, artificial intelligence, and other technologies to manage systems more efficiently and to ensure their safety and stability through automatic monitoring and preventive measures. In addition, this O&M technology can address issues faster and more efficiently, thus reducing downtime and losses, lowering maintenance and repair costs, and improving the sustainability of wind power generation systems [105,106].…”

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

“…A key feature in planning the industrial operation and maintenance activities is the detail of the progression of the faults along with the RUL of the equipment. Herp et al [60] presented models for the estimation of RUL of wind turbine main bearing using the likelihood functions based on the very concepts of health management, prognostics, and survival analysis. This study, after a thorough analysis of 67 different wind turbines, concluded that they discontinue functioning 13 days before their failures thereby accumulating a total of 786 days of potential non-operations.…”

Remaining Useful Life Estimation Framework for the Main Bearing of Wind Turbines Operating in Real Time

“…From this perspective, there has been an explosion of articles since 2014 investigating the use of datadriven methods and models for the assessment of remaining useful life (RUL), as shown in Figure 1, addressing different failure prognosis techniques in the kinematic assembly, mainly in wind turbine speed reducers. Several authors [6][7][8][9][10] have used machine learning techniques or artificial neural networks (ANN) to estimate the RUL of gears and bearings in speed reducers and carry out failure prognoses. Still, the number of studies for main bearing prognosis is significantly lower [2,11], as shown in Figure 1.…”

“…Some studies [12,13] employing hybrid models with deep neural networks have focused on main bearing fatigue estimates. Other studies advance the estimation of the RUL of the main bearing using natural neural network models (e.g., LSTM) [9]. The authors emphasize the need for data manipulation, data augmentation [13], and resampling [9] to estimate fatigue or RUL.…”