Complex system health condition estimation using tree-structured simple recurrent unit networks

Modern production has stricter requirements for the reliability of complex systems; thus, it is meaningful to estimate the health of complex systems. A complex system has diverse observation features and complex internal structures, which have been difficult to study with regard to health condition estimation. To describe continuous and gradually changing time-based characteristics of a complex system's health condition, this study develops a feature selection model based on the information amount and stability. Then, a reliability tree analysis model is designed according to the selected relevant features, the reliability tree is developed using expert knowledge, and the node weight is calculated by the correlation coefficient generated during the feature selection process. Using the simple recurrent unit (SRU), which is a time series machine learning algorithm that achieves a high operating efficiency, the results of the reliability tree analysis are combined to establish a tree-structure SRU (T-SRU) model for complex system health condition estimation. Finally, NASA turbofan engine data are used for verification. Results show that the proposed T-SRU model can more accurately estimate a complex system's health condition and improve the execution efficiency of the SRU networks by approximately 46%.