Abstract
In this paper, Proper net is proposed to construct variable cycle engine’s analytical redundancy, when all control variables and environmental variables change simultaneously, also accompanied with the whole engine’s degradation. In another word, Proper net is proposed to solve a multivariable, strongly nonlinear, dynamic, and time-varying problem. In order to make the topological structure of Proper net physically explainable, Proper net’s topological structure is designed according to physical relationship between variables, by which means analytical redundancy based on Proper net achieves higher accuracy with less calculation time. Experiments were compared with performance of analytical redundancy based on Proper net, seven convolutional neural network topological structures, and five shallow learning methods. Results demonstrate that under condition of average relative error less than 1.5%, Proper net is the most accurate and the least time-consuming one, which proves not only the effectiveness of Proper net but also the feasibility of topological structures’ design method based on physical relationship.
Highlights
To guarantee the safety of aeroengines, diagnostics and faulttolerance control have been developed [1]
Analytical redundancy is usually defined as signals calculated by algorithms or methods rather than signals measured by physical sensors
For diagnostics [2], analytical redundancy could be used as redundant signals for voting whether faults occur
Summary
To guarantee the safety of aeroengines, diagnostics and faulttolerance control have been developed [1]. In practice, strong nonlinearity still makes Newton Raphson iteration or Euler iteration diverge as signals change rapidly or working points are away from the common operating line, which brings unsafe factors to model-based analytical redundancy [4]. This problem could be solved by constraining changing rate of environmental variables and control variables, but it is paradoxical to the emerging requirement—high mobility of military planes. In Gou et al.’s research, a convolutional neural network (CNN) model trained with preprocessed and labeled data sets is used to extract features of a time-frequency graph, based on which faults can be identified and isolated [10].
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