Method for identifying fault of variable-pitch bearing of wind turbine generator set
A technology for wind turbines and pitch bearings, applied in mechanical bearing testing, character and pattern recognition, computer components, etc., can solve problems such as high detection costs, high requirements for operators, and difficulties in accurate fault location, achieving high accuracy rate effect
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[0055] Example: as figure 1 A method for fault identification of a pitch bearing of a wind turbine generator shown in the figure includes the following steps:
[0056] (1-1) Offline modeling, collecting training sample sets:
[0057] Collect data including normal operating condition data of wind turbines and pitch bearing fault condition data, conduct variation coefficient analysis on training set data, select variables sensitive to pitch bearing faults as input variables of hidden Markov model HMM, and train hidden Markov model HMM. Markov model;
[0058] (1-1-1) Offline modeling:
[0059] Set the monitoring data collected during the operation of the wind turbine to form two data sets X={x m,1 x m,2 …x m,n }∈R m×n and where dataset X represents the data collected during normal operation, where m is the number of normal samples, n is the number of monitored variables, and dataset X f represents the data collected when the wind turbine pitch bearing fails, where m f...
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