Hydraulic turbine cavitation acoustic signal identification method based on big data machine learning

A hydraulic turbine cavitation acoustic signal identification method based on big data learning, comprising the following steps:

• S1, obtaining latest acoustic signal time sequence data of each measuring point in real time through measuring points arranged on a hydraulic turbine set, and partitioning the acoustic signal time sequence data of each measuring point into multiple normalized acoustic signal subsequences, wherein a latest recorded acoustic signal subsequence of each measuring point is used as a real-time signal subsequence;
• S2, inputting the multiple normalized acoustic signal subsequences of all measuring points obtained in S1 into a self-organizing maps (SOM) neural network, clustering the multiple normalized acoustic signal subsequences into multiple clusters according to a corresponding operating condition of the hydraulic turbine set, and then dividing each of the multiple clusters into a steady-state cluster and an unsteady-state cluster according to a signal fluctuation degree of the multiple normalized acoustic signal subsequences in each of the multiple clusters;
• S3, traversing distribution of the real-time signal subsequences of all measuring points in the multiple clusters;
• if a number of the real-time signal subsequences contained in the steady-state cluster is not lower than a minimum number threshold, it is judged that the hydraulic turbine is in a steady condition and an incipient cavitation warning process proceeds according to S4-S8;
• otherwise, the current incipient cavitation warning process is interrupted;
• S4, performing feature screening on the real-time signal subsequences contained in the steady-state cluster by a random forest (RF) algorithm, and extracting optimal feature measuring points which can sensitively reflect changes in the corresponding operating condition of the hydraulic turbine set and optimal feature subsets of each of the optimal feature measuring points;
• S5, normalizing the optimal feature subsets of each of the optimal feature measuring points and calculating information entropy, and with the information entropy as an input, predicting a future trend of the hydraulic turbine set in a healthy state by using a health state prediction model constructed based on multilayer gate recurrent units (GRUs) to obtain predictive information entropy of the acoustic signal of each of the optimal feature measuring points in a next predictive step;
• S6, obtaining acoustic signal time sequence data actually acquired from each of the optimal feature measuring points on the hydraulic turbine set in the next predictive step and calculating actual information entropy, and calculating a dynamic tolerance of each of the optimal feature measuring points from the predictive information entropy and the actual information entropy;
• S7, based on a current output condition of the hydraulic turbine set, obtaining acoustic signal information entropy with incipient cavitation present) of the hydraulic turbine set in the next predictive step through prediction using a pre-constructed SOM network, and calculating a dynamic tolerance alarm threshold of each of the optimal feature measuring points from the predictive information entropy and the acoustic signal information entropy with the incipient cavitation present;
• and
• S8, comparing a sum of the dynamic tolerances of all the optimal feature measuring points with a sum of the dynamic tolerance alarm thresholds based on a threshold method, and judging whether the incipient cavitation occurs to the hydraulic turbine set;
• if yes, an incipient cavitation warning is given;
• otherwise, no incipient cavitation warning is given.