scholarly journals The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6348
Author(s):  
Chao Zhang ◽  
Haoran Duan ◽  
Yu Xue ◽  
Biao Zhang ◽  
Bin Fan ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Stochastic Resonance ◽  
Operating Conditions ◽  
Detection Accuracy ◽  
Wind Turbine Gearbox ◽  
Resonance Model ◽  
Function Type ◽  
Vibration Signals ◽  
Fourier Decomposition ◽  
Bearing Fault

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

The Effect of Operational Parameters on Vibration Signals of Wind Turbine Gearboxes

2019 ◽  
Author(s):  
Sofia Koukoura ◽  
Eric Bechhoefer ◽  
James Carroll ◽  
Alasdair McDonald
Keyword(s):  
Wind Turbine ◽  
Operating Conditions ◽  
Operating Costs ◽  
Normal Operation ◽  
Operational Parameters ◽  
Wind Turbine Gearbox ◽  
Vibration Signals ◽  
Gearbox Vibration ◽  
Maintenance Decisions ◽  
Yaw Angle

Abstract Vibration signals are widely used in wind turbine drivetrain condition monitoring with the aim of fault detection, optimization of maintenance actions and therefore reduction of operating costs. Signals are most commonly sampled by accelerometers at high frequency for a few seconds. The behavior of these signals varies significantly, even within the same turbine and depends on different parameters. The aim of this paper is to explore the effect of operational and environmental conditions on the vibration signals of wind turbine gearboxes. Parameters such as speed, power and yaw angle are taken into account and the change in vibration signals is examined. The study includes examples from real wind turbines of both normal operation and operation with known gearbox faults. The effects of varying operating conditions are removed using kalman filtering as a state observer. The findings of this paper will aid in understanding wind turbine gearbox vibration signals, making more informed decisions in the presence of faults and improving maintenance decisions.

scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

Wind Turbine Gearbox Fault Detection Based on Multifractal Analysis

2013 ◽  
Vol 644 ◽  
pp. 312-316
Author(s):  
Chang Zheng Chen ◽  
Ping Ping Pan ◽  
Qiang Meng ◽  
Yan Ling Gu
Keyword(s):  
Wind Turbine ◽  
Structural Vibrations ◽  
Wind Turbine Gearbox ◽  
Vibration Signals ◽  
The Real ◽  
Signal Processing Method ◽  
Fault Features ◽  
Incipient Faults ◽  
Weak Fault ◽  
Multifractal Method

The presence of irregularity in periodical vibration signals usually indicates the occurrence of wind turbine gearbox faults. Unfortunately, detecting the incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We used the wavelet-based multifractal method to extract the impulsive features buried in noisy vibration signals. We first calculated the wavelet transform modulo maxima lines from the real vibration signals, then, obtained the singularity spectrum from the lines. The analysis results of the real signals showed that the proposed method can effectively extract weak fault features.

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

2016 ◽  
Vol 231 (17) ◽  
pp. 3228-3237 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Nearest Neighbor ◽  
Roller Bearing ◽  
Approximate Entropy ◽  
Vibration Signals ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Iterative Filtering

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

An enhanced stochastic resonance method for weak feature extraction from vibration signals in bearing fault detection

2013 ◽  
Vol 228 (5) ◽  
pp. 815-827 ◽  
Author(s):  
Yaguo Lei ◽  
Jing Lin ◽  
Dong Han ◽  
Zhengjia He
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Industrial Applications ◽  
Wheel Pair ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Fault Features ◽  
Bearing Vibration

Rolling element bearings are widely used in modern machinery and play an important role in industrial applications. Tough environments under which they work make them subject to failure. The classical strategy is to collect bearing vibration signals and denoise the signals to detect fault features by using signal processing techniques. Although the noise is reduced with this strategy, the fault features may be weakened or even destroyed as well. Different from the classical denoising techniques, stochastic resonance is able to extract weak features embedded in heavy noise by utilizing noise instead of eliminating noise. The single stochastic resonance, however, fails to extract the fault features when the signal-to-noise ratio of the bearing vibration signals is very low. To address this problem, this paper investigates the enhancement methods of stochastic resonance and develops a cascaded stochastic resonance-based weak feature extraction method for bearing fault detection. Two sets of vibration signals collected respectively from an experimental bearing and a bearing inside a train wheel pair are utilized to demonstrate the proposed method. The results show that the method is superior to the other enhancement methods in extracting weak features of bearing faults.

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