Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: The present invention provides an on-line real-time diagnosis system and method for wind turbine blade (WTB) damage. The system includes a four-rotor unmanned aerial vehicle (UAV), a cloud database, and a computer system. The four-rotor UAV captures images of WTBs in real time, and transmits the images to the computer system. The cloud database stores an image library used for a Visual Geometry Group (VGG)-19 net image classification method, where an image in the image library stored in the cloud database is dynamically captured from a network. The computer system is used to perform training by using the image library to obtain an improved VGG-19 net image classification method, and classify, by using the improved VGG-19 net image classification method, the images of the WTBs received from the four-rotor UAV, to obtain a WTB damage diagnosis and classification result and a damage grading result.

Abstract: The present invention provides an on-line real-time diagnosis system and method for wind turbine blade (WTB) damage. The system includes a four-rotor unmanned aerial vehicle (UAV), a cloud database, and a computer system. The four-rotor UAV captures images of WTBs in real time, and transmits the images to the computer system. The cloud database stores an image library used for a Visual Geometry Group (VGG)-19 net image classification method, where an image in the image library stored in the cloud database is dynamically captured from a network. The computer system is used to perform training by using the image library to obtain an improved VGG-19 net image classification method, and classify, by using the improved VGG-19 net image classification method, the images of the WTBs received from the four-rotor UAV, to obtain a WTB damage diagnosis and classification result and a damage grading result.