The damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images. The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone. Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed. Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression. In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model. The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods.INDEX TERMS Wind turbine blade; damage detection; SSD; dense connection; BiFPN.
The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely and reliable response decisions. Because practical industrial processes have multimodal operating conditions, the data distributions of process variables are different. The different data distributions may cause the fault detection model to be invalid. In addition, the fault diagnosis model cannot find the correct root cause variable of system failure by only identifying abnormal variables. There are correlations between the trend states of the process variables. If we do not consider these correlations, this may result in an incorrect fault root cause. Therefore, multimodal industrial process monitoring is a tough issue. In this paper, we propose a three-step framework for multimodal industrial process monitoring. The framework aims for multimodal industrial processes to detect the faulty status timely and then find the correct root variable that causes the failure. We present deep local adaptive network (DLAN), two-stage qualitative trend analysis (TSQTA), and five-state Bayesian network (FSBN) to implement fault detection, identification, and diagnosis step by step. This framework can detect the system failure timely, identify abnormal variables, and find the root cause variable and the fault propagation path. The case studies on the Tennessee Eastman simulation and a practical chlorobenzene production process are provided to verify the effectiveness of the proposed framework in multimodal industrial process monitoring.
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