A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis

Abstract

The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic values to probability distributions. In this way, an uncertainty-aware method is explored to achieve reliable machine fault diagnosis. Taking the fault signal of the gearbox output shaft bearing of a wind turbine generator in a wind farm as an example, the diagnostic accuracy of the proposed method can reach 99.14%, and the confidence in diagnostic results is higher than other comparison methods. Experimental results show that the BayesianPDL framework has unique advantages in the fault diagnosis of wind turbine bearings.

Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm

“…Then, the input 𝒙 ∈ ℝ , query 𝒒 ∈ ℝ , key 𝒌 ∈ ℝ , and value 𝒗 ∈ ℝ vectors can be acquired through the linear mapping process. Moreover, for the whole input sequence X, three mapping matrices (i.e., Q, K, and V) and the output matrix 𝑿 can be calculated according to Equations ( 10)- (13). In this study, we selected a scaled dot-product as the attention scoring function.…”

“…In contrast, with the development of acquisition, transmission, and storage technologies, data-driven-based methods have become an attractive choice in the WTCM field, which only expands on the measured data instead of accurate physical or mathematical knowledge. Recently, numerous data-driven-based methods have been proposed in the literature and widely employed for WTCM methods, including vibration signal analysis [13][14][15][16], oil signal analysis [17], acoustic emission signal monitoring [18,19], electrical signal analysis [20], and others. However, the above-mentioned methods require the installation of additional signal acquisition equipment, which would result in a substantial improvement in the investment cost [21].…”

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

“…They are particularly useful time-varying or uncertain feature extraction. For example, a Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm has recently been applied to wind turbine blade bearing fault detection [28] and a Probabilistic Bayesian Parallel deep learning framework as been applied to wind turbine bearing fault diagnosis [29].…”