Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine

Abstract

One of the challenges that data-based gas path component monitoring systems are facing, is the poor performance in those operational conditions that are not considered in the training process. An approach could be retraining the model repeatedly with a large number of previous and fresh data from the scratch, which would require a lot of time and storage memory. To address this problem, a component fault diagnostic system based on the bank of online sequential extreme learning machines (OSELMs) are applied that can incrementally update with any number of new training samples. This system is adapted for a two-shaft industrial gas turbine that is used for power generation. Besides, an optimal set of measurements are selected as the input of the system using a hybrid approach based on the variable length genetic algorithm and extreme learning machine (VLGA-ELM). The performance of OSELM based diagnostic system is compared with several batch learning algorithms by analyzing the confusion matrix of all systems. The results show that the OSELM based approach reflects a promising compromise between the overall accuracy, false positive rate, F1-score and false negative rate, while having the least training time compared with the other systems.