Wind turbine fault diagnosis method and system based on svd-ssa-lstm

A wind turbine fault diagnosis method based on SVD-SSA-LSTM, comprising:

• setting a plurality of monitoring points according to equipment parameters of a wind turbine tower;
• acquiring vibration signals of each monitoring point according to preset monitoring time nodes, and generating a fault feature data packet according to all the vibration signals;
• and
• establishing a fault diagnosis model, and generating a fault diagnosis result according to the fault diagnosis model and the fault feature data packet;
• wherein
• said setting a plurality of monitoring points comprises:
• establishing a monitoring point sequence A, where A=(a1, a2, . . . , ai, . . . , an), ai is an i-th monitoring point, and n is a number of the monitoring points;
• said generating a fault feature data packet according to all the vibration signals comprises:
• acquiring the vibration signal of each monitoring point at a current monitoring time node;
• processing all the vibration signals according to a preset calibration model;
• generating primary vibration signals according to a processing result;
• decomposing the primary vibration signals according to a preset SVD model, and generating fault time-frequency domain feature information according to a decomposition result;
• and
• generating the fault feature data packet at the current monitoring time node according to the fault time-frequency domain feature information;
• said establishing a fault diagnosis model comprises:
• establishing training set data according to historical vibration data;
• establishing an LSTM network, and setting a plurality of groups of parameter configuration strategies for the LSTM network;
• establishing a sparrow individual sequence P, where P=(p1, p2 . . . , pi, . . . , pm), a single sparrow represents a group of parameter configuration strategies, pi is an i-th sparrow individual, and m is a number of the parameter configuration strategies;
• generating initial fitness of each sparrow individual according to the training set data;
• setting a sparrow position iteration strategy according to a preset SSA optimization model;
• outputting primary fitness of each sparrow according to the iteration strategy;
• establishing a primary fitness sequence B, where B=(b1, b2, . . . , bi, . . . , bm), bi is the primary fitness of the i-th sparrow individual, and m is a number of the sparrow individuals;
• setting the sparrow individual corresponding to a maximum value bmax in the primary fitness sequence B as a target sparrow individual;
• and
• generating the fault diagnosis model according to the parameter configuration strategy corresponding to the target sparrow individual and the LSTM network.