using one-class support vector machine for the fault diagnosis of an industrial once-through benson boiler

considering that once-through benson boiler is one of the most crucial equipment of a thermal power plant, occurrence of any fault in its different parts can lead to decrease of the performance of system, and even may cause system damage and endanger the human life. in this paper, due to the high complexity of the system's dynamic equations, we utilized data-based method for diagnosing the faults of the once-through benson boiler. in order to enhance the fault diagnose (fd) system proficiency and also due to strong interactions between measurements, we decided to utilize six one-class support vector machine (svm) algorithms to diagnose six major faults of once-through benson boiler. in the proposed structure, each one-class svm algorithm has been developed to diagnose one special fault. finally, we carry out diverse test scenarios in different states of fault occurrence to evaluate the performance of the proposed fd system against the six major faults of the once-through benson boiler under conditions of noisy measurement.