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Huawei Wang [1] and Jun Gao [2] and Zhiyong Liu [2]

Academic Editor: H. K. Leung

, 1, , College of Civil Aviation, , Nanjing University of Aeronautics and Astronautics, , Nanjing 210016, , China, , nuaa.edu.cn,

, 2, , Department of Management, , Shijiazhuang Mechanical Engineering College, , Shijiazhuang 050003, , China,

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Engines are the heart of an aircraft, whose failures can effect aircraft safety and lead to heavy economic losses. For aero engines, the most advanced preventive maintenance (PM) policy strategies rely on the monitoring of measurable parameters and making maintenance decisions on the level of the degradation of the system. Condition-based maintenance (CBM) has been used for aero engines, which has been proved useful in minimizing the cost of maintenance, improving operational safety, and reducing the quantity and severity of in-service failures. However, the traditional CBM only carries out maintenance tasks that focus on condition monitoring and diagnostics, which attempts to avoid unnecessary maintenance tasks when there is evidence of abnormal behaviors [1]. In recent years, CBM plus(CBM+), a development of CBM, has been put forward, which is the application and integration of appropriate process, technologies, and knowledge-based capabilities to improve reliability and maintenance effectiveness [2]. CBM+ can be viewed as cost-effective and accurate maintenance, which shows increasing importance in improving aero engines availability, reducing downtime cost, and enhancing operation reliability.

The core factors of maintenance decision can be summarized as condition monitoring, reliability evaluation, and decision optimization. Condition monitoring involves comparing online and offline data with expected values. Reliability evaluation and prediction are based on condition monitoring.

For aero engines, there is little failure data. However, additional information, including on-board sensor measurements, maintenance histories, and component data, is available. The goal of CBM+ is to achieve more accurate maintenance. In order to achieve accurate maintenance, data fusion techniques are suggested. Thus, it is beneficial to put event data and condition monitoring data together. This combined data analysis can be accomplished by building a mathematical model that properly describes the underlying mechanism of a fault or a failure.

Applying fusion...

    

 

    

  

  

  

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