Engineering topics

Sheng, Shawn (ORCID:0000000301340907)

Publications and source records attributed to Sheng, Shawn (ORCID:0000000301340907).

10 records

DOE OSTI2024

Wind Turbine Maintenance Costs: Assessing the Potential of Gear Oil Improvements

Wind turbine operations & maintenance (O&M) costs constitute a sizable portion of total energy cost for wind power. There are many components in a utility-scale wind turbine that need to be lubricated with either oil or grease. This study uses gearbox oil as an example and assesses how lubricant technology improvements may impact wind turbine power production and levelized cost of energy. Using the modeling tools (i.e., WOMBAT, reV, and SAM) developed at National Renewable Energy Laboratory and lubrication oil technology scenarios defined based on inputs provided by ExxonMobil and industry stakeholders, we quantify the potential for increasing energy production and reducing maintenance costs across the current and future U.S. fleet of wind turbines as a result of improvements in lubrication technologies. The modeled improvements reduce the median levelized cost of energy by 1-2% across the U.S. fleet. Cumulative saving from gear oil technology improvements in 2050 for U.S. fleet is estimated to be approximately $6 billion. Extending the lubricant replacement interval has a larger impact on total cost and energy production than reducing lubricant cost.

costs↗

DOE OSTI2024

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

Vibration-based condition monitoring techniques are widely used for diagnosing faults in rotating machines. These techniques are implemented in the time domain, the frequency domain, or both. However, the composite and noisy nature of the raw data collected requires a preprocessing stage such as filtering and decomposition using in-depth processing techniques. Moreover, these methods require good frequency resolution and involve examining a broad frequency range to discern both healthy and faulty cases. In this work, we introduce a simple and fast diagnostic scheme for wind turbine gear teeth wear based on time domain analysis. The proposed method is based on the local minima interpolation of a filtered version of the vibration signal following time synchronous averaging (TSA) technique. Given tachometer signal, the TSA of the vibration data is performed using MTALAB software. Then, local minima of the filtered signal are interpolated using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function. The variance of the interpolated curve built a gear fault index. The derived fault index resulting of the proposed technique allows a substantial distinction between the healthy and faulty cases. Its efficiency is validated using 10 real-world datasets of vibration stemmed from a wind turbine planetary gearbox. The proposed method boasts a low computation time and ease of interpretation, specifically beneficial for gearbox fault diagnosis purposes.

fault diagnosis↗

DOE OSTI2023

Assessing the Potential for Improved Lubricants to Reduce Wind O&M Costs

Lubrication is a key aspect of maintaining a wind turbine in operational condition. In this study, we quantify the potential for increasing energy production and reducing maintenance costs across the current and future U.S. fleet of wind turbines as a result of improvements in lubrication performance. The modeled improvements reduce the median levelized cost of energy by 1-2% across the U.S. fleet. Extending the lubricant replacement interval has a larger impact on total cost and energy production than reducing lubricant cost.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗

DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗

DOE OSTI2023

Wind Turbine Drivetrain Reliability Research - Gearbox Bearing Axial Cracking Failure Mode Example

The U.S. Department of Energy's National Renewable Energy Laboratory and Argonne National Laboratory have been conducting wind turbine drivetrain (formerly gearbox) reliability research for many years. Although the drivetrain focus has not changed, detailed projects are adjusted every few years based on dynamic needs seen in the field across the wind industry. This webinar will walk through the research methodology by using wind turbine gearbox bearing axial cracking failure mode as an example. The detailed steps include: 1) top failure mode identification based on actual failure data collected from project partners, 2) bench-top testing to identify possible contributing factors and formulate a damage metric, 3) physics domain modeling and validation through testing, 4) reliability assessment and prognosis based on the physics domain model and data domain inputs, and further enhancement through machine learning algorithms, using actual wind plant operational and failure event data. Hopefully, the presented work is of interest to the IISE community, and some members can apply their expertise to wind turbine and plant applications, helping enhance wind power generation technology advancement and its broader deployment.

axial cracking↗

DOE OSTI2023

Wind Plant Operations and Maintenance Challenges and Research Opportunities

Global wind industry has experienced tremendous growth during the past two decades and the trend does not appear changing in near future. However, the industry is still challenged by premature component failures and high operations & maintenance (O&M) costs, which can account for up to 35% of levelized cost of energy. It is imperative for the industry to improve performance, reliability and reduce O&M costs through advanced technologies, enabled by research in related disciplines, to be competitive. This talk will first briefly discuss the challenges with wind plant O&M, then give an overview of related NREL research in the areas of performance, reliability, and O&M cost modeling, finally touch on future R&D opportunities in related areas. The authors hope some of these challenges are of interested to and can be addressed by the INFORMS community in future.

costs↗

DOE OSTI2022

Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. A recently proposed method to capture the rolling element bearing degradation provides the groundwork for new indicator families utilizing the generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the National Renewable Energy Laboratory Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox; hence the complexity of the vibration signals is similar to a real case. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.

condition monitoring↗

DOE OSTI2022

Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators: Preprint

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. The underlying idea behind traditional indicators often revolves around tracking both cyclostationarity and abnormal impulses in the vibration signals without distinguishing the two. A recently proposed method to capture the rolling element bearing degradation lays out the groundwork for new indicator families utilizing generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the NREL Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox, hence the complexity of the vibration signals is similar to a real case. Furthermore, the new indicators are also tested with signals that carry multiple fault signatures. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.

condition monitoring↗

DOE OSTI2022

Drivetrain Reliability Collaborative Update

Pitch bearings, main bearings, and gearboxes in conventional wind turbine drivetrains often do not meet their 20-year minimum specified lifetime, resulting in turbine downtime as well as expensive, time-consuming repairs or replacements. The dominant failure modes of the drivetrain components and the conditions that lead to their failure are not fully accounted for during product design or routinely modeled for life management. Drivetrain reliability improvements and O&M cost reductions remain top priorities for both land-based and offshore wind turbines, especially as wind turbines continue to be deployed in increasingly remote and offshore locations, continue to increase in size, and are becoming expected to be in service beyond their original design life, all of which correspond to an increase in the impact of any reliability issues on O&M costs. This presentation summarizes the most recent activities by NREL and ANL on drivetrain reliability.

bearing↗

DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗