Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability
By P. J. García Nieto, E. G. Gonzalo, Fernando Sánchez Lasheras, Francisco Javier de Cos Juez. Reliability Engineering & System Safety, 2015
297 citations recorded by the source corpus.
Abstract The present paper describes a hybrid PSO–SVM-based model for the prediction of the remaining useful life of aircraft engines. The proposed hybrid model combines support vector machines (SVMs), which have been successfully adopted for regression problems, with the particle swarm optimization (PSO) technique. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not been yet widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured parameters (input variables) for aircraft engines with success. A coefficient of determination equal to 0.9034 was obtained when this hybrid PSO–RBF–SVM-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. One of the main advantages of this predictive model is that it does not require information about the previous operation states of the engine. Finally, the main conclusions of this study are exposed.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ce3d8395dda05801d18527db81cf33c22101f14b
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Citations in the corpus
- Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, background): “In addition to these different domains of application, researchers have explored different ML models, for example, support vector machine/regression [67], Gaussian Process Regression (GPR) or kriging [69], and Deep Learning for structural reliability problems, RUL prediction, fire hazard simulation,”
- Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, unknown): “Afterextracting someinformativefeatures,theconventionalmachinelearning algorithms,suchasartificialneuralnetwork[12],[13],ex-tremelearningmachine(ELM)[14],SVM[15],[16],neural networks[17],andrandomforest(RF)[18],canbeemployed topredicttheRUL.”
- Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “[78] applied an SVM in a nonlinear model to predict the remaining life of aircraft engines.”