A Method for Optimizing the Threshold Strength and Times of Preventive Maintenance of Shipborne Aircraft Sensor System

Abstract

The invention relates to a method for optimizing the threshold strength and times of preventive maintenance of a shipboard aircraft sensor system. The carrier-based aircraft sensor system first generates the probability density function of the remaining degradation of the preventive maintenance. According to the probability of preventive maintenance and replacement after the preventive maintenance and the replacement probability of scrapping, the life cycle and total maintenance cost of the carrier-based aircraft sensor system are calculated, and the long-term The expected cost rate is optimized to obtain the optimal preventive maintenance threshold strength and the limit of preventive maintenance times. The advantage of this invention is that the life cycle prediction model of the carrier-based aircraft sensor system is described by a nonlinear degradation process. At the same time, the optimization model takes into account the influence of two main variables, the threshold strength of preventive maintenance and the limit of preventive maintenance times, on the long-term maintenance cost rate, and the optimal The long-term maintenance cost rate is lower, and at the same time, it provides a reference for parameter setting during maintenance and overhaul. The present invention verifies the reliability and effectiveness of the proposed method through simulation experiments.

Description

(1) Technical field [0001] The present invention is a method for preventing maintenance and intermediate strength and number of times optimized by the carrier machine sensor system. (2) Background [0002] The main combat weapon of the aircraft carrier is a carrier. Since the number of carrier machines mounted on the aircraft carrier is limited, in order to ensure the availability requirements of the available carrier, it is necessary to ensure higher carrier-loading machine availability. The availability of the carrier is mainly dependent on the life cycle of each sensor, so there is an important significance for research on the life cycle factors of the sensor. The prediction and health management techniques can make full use of the state monitoring information of the system, estimate the reliability of the system in the life cycle and reducing the risk of system failure events through maintenance activities. Accurate predict the remaining life is the core and foundation, and i...

AI Technical Summary

Problems solved by technology

These methods do not consider the impact of preventive maintenance on remaining life

LeiY.G., 2016 proposed a new method for predicting the remaining life of machinery based on a stochastic process model. While considering multiple sources of variation in the stochastic degradation process of the machine, a new stochastic process model was constructed, and then the Kalman particle was used to The filtering algorithm estimates the state of the system. This method only considers the impact of preventive maintenance on the remaining life, but does not consider its impact on maintenance costs.

There are also some studies based on linear degradation models to study the impact of preventive maintenance on remaining life and maintenance decisions, but the degradation models in reality often have nonlinear characteristics, and these methods need to be combined with nonlinear degradation models for improved research

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