Parallel Multiple CNNs With Temporal Predictions for Wind Turbine Blade Cracking Early Fault Detection

Detection tasks based on supervisory control and data acquisition (SCADA) data are challenging because of the following problems: 1) high redundancy and high nonlinearity of data complicate the detection task and 2) because the sheer volume of data, the current methods are hard to accurately detect useful early fault information from raw data. This study proposes a wind turbine blade cracking early fault (WTBCEF) detection model, the parallel multiple CNNs with temporal predictions (PMCTPs). The model innovatively integrates temporal convolutional network (TCN), ResNet50, and Xception. Specifically, PMCTP applies the TCN model to SCADA data in a parallel branch to improve detection accuracy by extracting temporal features predicted by the TCN model in parallel. The PMCTP reduces the impact of data redundancy and high data volumes by dividing the wind turbine data into four categories according to different state parameters. In addition, PMCTP combines the advantages of ResNet50 and Xception to perform adaptive feature extraction for each of the four types of state parameter data for a wind turbine. PMCTP introduces multiple fully connected layers into the model, which can provide parallel computation for serializing hidden features. Experimental results show that PMCTP is more accurate than some popular convolutional neural networks (CNNs).