Abstract
Accurate health evolution trend forecasting of aero-engine is essential for operation reliability and maintenance costs of aeronautical equipment. In this study, an intelligent deep learning method, systematically blending the dispersion entropy-based multi-scale series aggregation scheme and long short term memory (LSTM) neural network, is proposed for forecasting the health evolution trend of aero-engine. Firstly, a comprehensive measurement of health levels, namely, integrated health state index (IHSI), is developed with high-dimensional dataset. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is exploited to decompose the IHSI sequence into several multi-scale series to further capture the internal characteristics of original sequence. Subsequently, multi-scale series aggregation assisted with dispersion entropy analysis theory is conducted for obtaining the aggregated sub-series (ASS). Finally, the ASS are served as the inputs of LSTM network to complete the health evolution trend forecasting of aero-engine. To demonstrate the effectiveness of the proposed method, six approaches are present for the comparisons of forecasting performance. The experimental results indicate that the proposed method can effectively measure the health evolution process of aero-engine and further obtain more accurate trend forecasting results.
Highlights
With the improvement of systemic integration degree and automation level, complete and reasonable health state monitoring has an important significance in ensuring stable operation of equipment and contributes to achieve the condition-based maintenance (CBM) [1]–[3]
It has been demonstrated that empirical mode decomposition (EMD) shows excellent performance in analyzing the non-stationary signal, there are some inherent limitations that make some restriction on the application of EMD, such as mode mixing problem and end point effect
DESCRIPTIONS OF EXPERIMENTAL DATA AND ERROR CRITERIA To demonstrate the effectiveness of the proposed method, a classical dataset derived from the problem of prognostics and health management in 2008 is used and the corresponding results can be further analyzed and evaluated
Summary
With the improvement of systemic integration degree and automation level, complete and reasonable health state monitoring has an important significance in ensuring stable operation of equipment and contributes to achieve the condition-based maintenance (CBM) [1]–[3]. Jiang et al.: Intelligent Deep Learning Method for Forecasting the Health Evolution Trend of Aero-Engine a health state measurement is the primary problem to be solved Aiming at this problem, some researches on building a suitable indicator for the effective description of health levels have been conducted. If only consider the condition at present, the information contained in the condition at an earlier time would be ignored Aiming at this problem, a new deep learning model, i.e., recurrent neural network (RNN), is developed and constructs the associations between hidden layer nodes, where the memory of recent states can be stored [21]. Based on the analysis above, an intelligent deep learning method, systematically blending the dispersion entropy (DE)-based multi-scale series aggregation scheme and LSTM neural network, is proposed in this paper to forecast the health evolution trend of aero-engine.
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