Attention Mechanism-Based CNN-LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation
Algorithm 1
CLA model algorithm.
Input: Status data of wind turbine after processed:
Output: Fault prediction results of wind turbine.
● FC is the full connection layer;
● max is the maximum pool layer;
● is the sigmoid activation function;
● is the convolution kernel, is the size of convolution kernel, are the features from to and is the offset term;
● is the weight matrix, is the offset vector, is the hidden state of the previous time, is the input of the current time, is the memory unit of the current time;
● is the prediction result of the model and is the real label of the sample.
Training:
Initialization: Initialize all parameters in the model;
For p in n do:
(1) Deep level feature is extracted based on CNN: ;
(2) LSTM integrates the fault characteristics of each time segment into a unified sequential fault feature:
(3) The attention mechanism allocates the weights and the full connection layer generates the prediction results:,;
(4) Calculate the loss value of the model , then adjust the model parameters.
