Abstract:As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine's different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality.
Traditional reliability allocation methods are based on the assumption that the subsystems of a system are independence in order to simplify the problem. However, this assumption could deviate from the engineering practice. To achieve the reliability requirement of the system, the subsystems must be allocated high reliability based on traditional reliability allocation approaches which neglect the dependence between subsystems. To solve this problem, an improved reliability allocation method is developed in this paper. Firstly, the failure dependence between subsystems of mechanical systems is characterized using Copula functions. Secondly, a reliability prediction model considering failure dependence is formulated based on Copula function, Furthermore, the improved reliability allocation method according to relative failure rate is proposed. Finally a numerical case is presented to illustrate the proposed approach. The optimal allocation result shows that the system can achieve reliability requirement without high reliability demand to some subsystems, which could reduce the unnecessary cost.
A three-dimensional micromechanical finite element cutting model with the thermo-mechanical coupling was developed for carbon fiber reinforced polymer composites in the paper. The finite element modeling considers the three phases of a composite, in which the interphase between the fiber and matrix can realize heat transfer and allow debonding to represent the failure of composites. The model predictions of the machining responses, such as cutting temperature and subsurface damage, at different fiber orientations were compared with various experimental data for model validation. It is indicated that the three phase micromechanical model is capable of precisely predicting cutting temperature and the damage induced by the cutting tool. It was found that cutting temperature and subsurface damage strongly depend on the fiber orientation. Subsurface damage is easily occurs in a fiber orientation range of 90°–135°, while the largest depth of the thermal damage occurs at 90°. In addition, the effect of machining parameters on the cutting temperature was investigated based on the cutting model. It was showed that the cutting speed should be reasonably selected to control the cutting temperature. The temperature decrease with increase the rake angle, while increase with increase depth of cut and radius of cutting edge.
Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.
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