An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

An accurate estimation of the remaining useful life (RUL) not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance (CBM). For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work, an adaptive-order particle filter (AOPF) prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump’s degradation. With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method.     

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.     

Impact of the thermal effect on the load-carrying capacity of a slipper pair for an aviation axial-piston pump

A thermal hydraulic model based on the lumped parameter method is presented to analyze the load-carrying capacity of a slipper pair in an aviation axial-piston pump under specified operating conditions. Both theoretical and experimental results are presented to demonstrate the validity of the thermal hydraulic model. The results illustrate that the squeezing force and thermal wedge bearing force are the main factors that affect the film thickness and load-carrying capacity. At high oil temperature and high load pressure, the film thickness decreases with increasing clamping force due to a combined action of the squeezing bearing force and the thermal wedge bearing force, but the load-carrying capacity will increase. An increase of the film thickness is proven to be beneficial under high shaft rotational speed but especially dangerous as it strongly increases the ripple amplitude of the film thickness, which leads to decreasing the load-carrying capacity. The structural parameters of the slipper can be optimized to achieve desired performance, such as the slipper radius ratio and orifice length diameter ratio. To satisfy the requirement of the load-carrying capacity, the slipper radius ratio should be selected from 1.4 to 1.8, and the orifice length diameter ratio should be selected from 4 to 5.