A hybrid model of LSTM neural networks with a thermodynamic model for condition-based maintenance of compressor fouling

Abstract

Compressor fouling is one of the critical gas path faults of gas turbines, and the fouling process is significantly influenced by the quality of the inlet air coming from the air intake system with filters. The maintenance strategies for compressor fouling mainly consist of online/offline washing and replacement of filters, where optimizing the washing cycles and the replacement of filters is essential for the economy and safety of gas turbine operation as of the trade-off between the performance improvement and the corresponding costs. By considering the filtration effects of the air intake system, the gas path analysis of the gas turbine is carried out to tackle the coupled fouling process, and a hybrid framework is presented to predict the washing cycle (remaining useful life prediction for washing) and detect filter leakage (diagnosis for filter) via integrating the thermodynamic model and long short-term memory (LSTM) neural networks. The proposed scheme is applied in a field dataset and the results show that: (i) a deterioration index based on the thermodynamic model can be used to evaluate the compressor fouling degree, which is independent of ambient conditions and control factors. (ii) A prediction model based on the LSTM-Hankel method demonstrates good performance in long-time washing cycle prediction. (iii) Air filter leakage will significantly increase the degradation rate of compressor efficiency, which can be identified by the diagnosis model to predict the new washing cycle.