A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

2021 ◽  
Vol 161 ◽  
pp. 107963
Author(s):  
Kai Zhang ◽  
Baoping Tang ◽  
Lei Deng ◽  
Qian Tan ◽  
Haoshuai Yu
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Diagnosis Method ◽  
Noisy Labels

Fault Diagnosis for Wind Turbines Based on Vibration Signal Analysis

2011 ◽  
Vol 354-355 ◽  
pp. 458-461 ◽  
Author(s):  
Cheng Gang Zhen ◽  
Yin Yin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Signal Analysis ◽  
Vibration Signal ◽  
The Other ◽  
Safety Index ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Diagnosis Method ◽  
Vibration Signal Analysis

Fault diagnosis is an important technical method to improve the safety index and economic effectiveness of wind turbines, it also provide support to advanced maintenance and design in wind power equipment. In this paper, we have done vibration testing on two wind turbines, one is in normal, the other is with faulty, and then carried on comparative analysis of vibration signal to the experimental data, finally designed a fault diagnosis method for direct-driven wind turbine generators.

Pitch fault diagnosis of wind turbines in multiple operational states using supervisory control and data acquisition data

2018 ◽  
Vol 43 (5) ◽  
pp. 443-458 ◽  
Author(s):  
Lu Wei ◽  
Zheng Qian ◽  
Cong Yang ◽  
Yan Pei
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Wind Turbines ◽  
Supervisory Control ◽  
Domain Knowledge ◽  
Gaussian Mixture ◽  
Abnormal Behavior ◽  
Diagnosis Method ◽  
Pitch System ◽  
Relationship Of

Supervisory control and data acquisition data including comprehensive signal information have been widely applied to fault diagnosis. However, because of the complex operational condition of wind turbines, supervisory control and data acquisition data become complicated and abstract to study. This article proposes a pitch fault diagnosis method of wind turbines in multiple operational states using supervisory control and data acquisition data. According to the performance of characteristic parameters in nine operational states of wind turbines, Gaussian mixture model clustering and the analysis of normal performance curves are applied to model the relationship of pitch angle, rotor speed, and wind speed. Four cases have been studied to demonstrate the feasibility of the proposed method. The advantages of the proposed approach are as follows: (1) simplifying the analysis of supervisory control and data acquisition data through dividing the data into nine parts; (2) detecting pitch faults earlier than supervisory control and data acquisition monitoring system; (3) visualizing the abnormal behavior of the pitch system; and (4) improving the interpretability of the method with the incorporation of domain knowledge.

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Measurement ◽  
2021 ◽  
pp. 109491
Author(s):  
Kai Zhang ◽  
Baoping Tang ◽  
Lei Deng ◽  
Xiaoli Liu
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Diagnosis Method

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

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