Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern

Wang, Anqi; Pei, Yan; Zhu, Yu; Zheng, Qian

doi:10.1016/j.renene.2023.05.003

Cited by 9 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Layer normalization has the characteristic of weight scaling invariance, which returns the distribution of input features to the non-saturated region of the activation function. It can effectively mitigate gradient disappearance and explosions, and has an accelerated training effect under the training strip of a single sample; thus, it is widely used in deep-learning model training [18,34,35]. The specific operation process of layer normalization includes the calculation and normalization of the mean and variance of a single sample's features.…”

Section: Layer Normalizationmentioning

confidence: 99%

“…The validity of the proposed method was verified using the vibration signals of wind turbine gearboxes from a real wind farm. Wind turbine gearboxes are equipped with a condition monitoring system, which often includes eight vibration sensors to monitor each gearbox component [35]. The wind turbine gearbox always operates under variable operating conditions owing to random wind loads.…”

Section: Description Of Experimental Datamentioning

confidence: 99%

See 1 more Smart Citation

A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*

Yu,

Zhang,

Tang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Wind turbine gearboxes work under random load for extended periods of time, and the fault detection indicator constructed by the existing deep learning models fluctuate constantly due to the load, which is easy to cause frequent false alarms. Therefore, a multihead self-attention autoencoder network is proposed and combined with a dynamic alarm threshold to detect faults in a wind turbine gearbox subjected to random loads. The multiheaded attention mechanism layer enhances the feature-extraction capability of the proposed network by extracting global and local features from input data. Furthermore, to suppress the influence of the random load, a dynamic warning threshold was designed based on the reconstruction error between the inputs and outputs of the proposed network. Finally, the effectiveness of the proposed method was verified using the vibration data of wind turbine gearboxes from an actual wind farm.

show abstract

Section: Layer Normalizationmentioning

confidence: 99%

Section: Description Of Experimental Datamentioning

confidence: 99%

A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*

Yu,

Zhang,

Tang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…C. Zhang et al [15] utilized a new method based on a Bayesian and an adapted Kalman-augmented Lagrangian for filtering signals under time-varying conditions for the fault detection of wind turbine blade bearings. A. Wang et al [16] proposed a novel method and embedded a multivariable query pattern in a self-attention mechanism for quantifying the influences of different features on the anomalies of wind turbines. The experimental results confirmed the effectiveness of the proposed method for the early detection of wind turbine faults and identification of anomaly causes.…”

Section: Introductionmentioning

confidence: 99%

A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data

Wang,

Liu,

Zhou

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

show abstract

“…Yu et al [17] proposed a novel deep transfer learning bearing fault diagnosis model is designed in this work by fusing time-frequency analysis, residual network, and self-attention mechanism. Anqi Wang et al [18] proposed a novel method utilizing a self-attention mechanism embedded with a multivariable query pattern is proposed for the anomaly detection and underlying causes identification. Fang et al [19] designed a lightweight framework with strong robustness, As the first attempt to build a portable mobile detection device based on the deep-learning model, this article provides a new detection scheme for the relevant practitioners of mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

The TSM-net: a new strategy for insulated bearings intelligent faults diagnosis

Yang,

Zhao,

Zhang

et al. 2023

Phys. Scr.

View full text Add to dashboard Cite

With the development of power semiconductor devices, pulse width modulation technology is widely used in high-power frequency conversion control motors, which significantly improves the dynamic performance of variable-speed drive system equipment. However, the high-frequency shaft voltage generated during the drive process acts on the bearing to generate high-frequency current. The damage caused by the shaft current sharply shortens the fatigue wear process of the bearing, which in turn leads to premature failure of the bearing. A high insulating ceramic coating is prepared on the outer surface and side face of the inner and outer rings of the bearing by plasma spraying. That is, an insulating protective film is formed on the outer surface of the bearing, which can effectively isolate or reduce the bearing current, prevent the occurrence of electric erosion, and prolong the service life of the variable speed drive system equipment. However, the vibration excitation generated by the variable-speed drive system equipment will cause cracks or fatigue damage to the insulating bearing, resulting in a very complex fault mechanism of the vibration signal. The fault signal characterization lacks a professional signal analysis method, especially the high-reliability, high-precision and long-life high-performance insulating bearing. There is no qualitative formula or characteristic index to explain its failure. To fill this research gap, a new strategy for optimizing the temporal information fusion model and introducing the self-attention mechanism is innovatively developed, and it is named TSM-Net model, and the first attempt is made to realize intelligent identification of insulated bearing faults. Specifically, a multi-channel insulated bearing time information fusion diagnostic model is designed, and the coarse-grained characteristics with timing law are extracted from the measured insulated bearing fault data. Then, the self-attention mechanism is introduced into the designed insulated bearing time information fusion diagnostic model to optimize, and the weight coefficient is continuously updated to calculate the correlation weight between the insulated bearing fault data and the data, so that the final decision of the TSM-Net model is more focused, so as to improve the diagnostic accuracy. Finally, comparing the proposed TSM-Net model with the current five advanced methods, it is found that the proposed TSM-Net model has good diagnostic accuracy for rail transit motor insulated bearing faults, which verifies the effectiveness and superiority of the strategy, and provides a new way for the fault diagnosis of insulated bearings of high-power inverter control motors.

show abstract

Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern

Cited by 9 publications

References 36 publications

A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*

A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*

A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data

The TSM-net: a new strategy for insulated bearings intelligent faults diagnosis

Contact Info

Product

Resources

About