Wind turbine blade fault classification method based on radar echo characteristics and random forest

The invention discloses a wind turbine blade fault classification method based on radar echo characteristics and a random forest. The method is used for accurate classification of eight types of blade targets (healthy blades, 5% of blade breakage, 10% of blade breakage, 20% of blade breakage, 30% of blade breakage, blade bending, blade falling and blade abrasion). The method comprises the following steps: (1) constructing a mathematical echo model of near-field radar and a wind turbine blade to obtain near-field radar echo micro-Doppler signals of eight types of blade targets, and performing high-pass filtering; (2) extracting time domain waveform characteristics of the time domain echo signals; (3) extracting frequency domain features from the frequency spectrum of the echo signal; (4) carrying out time-frequency transformation on the time domain signal by adopting an improved generalized S transformation method; (5) extracting time-frequency features from the time-frequency result; (6) considering the extra