Fault Diagnosis Approach of Marine Steam Turbine Based on Exponential-Trigonometric Optimization Algorithm

Aiming at the fault diagnosis problem of marine steam turbine, a fault diagnosis approach of marine steam turbine based on exponential-trigonometric optimization (ETO) algorithm was proposed. Firstly, the Min-Max method was used to standardize the collected steam turbine data, and the steam turbine fault feature vectors were obtained. Secondly, the standardized steam turbine fault feature vectors were divided into the training data for training probabilistic neural network(PNN) and the testing data for testing PNN. Then, the fault diagnosis model of probabilistic neural network was constructed, and the optimal parameter was obtained by using ETO. Finally, the testing data were inputted into the optimized probabilistic neural network fault diagnosis model, and the fault diagnosis result was outputted. The simulation experiment results show that the training accuracy of the probabilistic neural network fault diagnosis model optimized by ETO is increased from 86.29% to 100%, and the testing accuracy is increased from 86.67% to 92.67%, indicating that the method can effectively improve the fault diagnosis accuracy, and the method has a simple structure, few iterations, and high operating performance and execution efficiency. It can improve the fault diagnosis level of marine steam turbine.