Research on Bearing Fault Diagnosis of Wind Turbine Gearbox Based on 1DCNN-PSO-SVM

Zhang, Xiaolin; Han, Peng; Liu, Xü; Zhang, Fei; Wang, Yongping; Gao, Lu

doi:10.1109/access.2020.3032719

Cited by 67 publications

(37 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In data acquisition, samples intended to convey health patterns are in the form of signals that have been collected using various types of sensors. Here, particular sensor type(s) may be used in specific diagnostic applications, such as, e.g., the accelerometers are generally used to collect vibration signals from the WT drivetrain including bearings, gearbox, and shafts [84][85][86]. Similarly, microphones can be used to record acoustic emissions in harsh environments where it is difficult to implement accelerometers [87], and thermocouples can also be used for the same purpose as accelerometers [6,[88][89][90].…”

Section: Data Acquisitionmentioning

confidence: 99%

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

View full text Add to dashboard Cite

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recently, a hybrid PSO-SVM technique with good efficiency and higher accuracy [43] is used for the distribution system series arc fault detection under different load type conditions. A fault diagnostic system for bearing wind turbine gearbox based on PSO-SVM is proposed in [44].…”

Section: A Related Workmentioning

confidence: 99%

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

et al. 2021

View full text Add to dashboard Cite

This paper proposes a protection scheme for multi-terminal high voltage direct current (MT-HVDC) transmission grids. The proposed scheme is based on three protection levels; di/dt overcurrent protection as the main protection, dv/dt undervoltage protection as a backup one protection. If the main and backup one fails to detect and clear the fault current, then the fault tripping and clearing is accomplished using the overcurrent protection by AC circuit breakers (ACCBs) in the AC grid side after a coordinated time. The tripping in the main and backup one protection levels are based on using fault isolation devices (FIDs) tripping. Support vector machine (SVM) is proposed for the fault detection and discrimination in the MT-HVDC using the backup two protection system. The Internet of things (IoT) has been used for monitoring and coordination between the AC grid and MT-HVDC grid. The proposed protection scheme is applied and verified on the MT-HVDC network under different transmission system faults using the MATLAB/Simulink ® environment. The results show that the proposed scheme's effectiveness as a fast and reliable scheme in fault detection and discrimination for the MT-HVDC grids.

show abstract

“…Udmale et al [21] adopted the TL technique to transfer knowledge from vibration datasets, including massive data to situations without enough sample data. A hybrid DL method involving CNN, SVM, and PSO was designed by Zhang et al [22] to realize feature extraction and classification of wind turbine gearbox bearing faults. Zhu et al [23] combined DL and TL to deal with the unlabeled data in the application.…”

Section: Fault Identification Aspectmentioning

confidence: 99%

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

et al. 2021

View full text Add to dashboard Cite

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

show abstract

Research on Bearing Fault Diagnosis of Wind Turbine Gearbox Based on 1DCNN-PSO-SVM

Cited by 67 publications

References 43 publications

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

Contact Info

Product

Resources

About