Production engine pass-off testing is a compulsory technique adopted to ensure that each engine meets the required performance criteria before entering into service. Gas turbine performance analysis greatly supports this process and substantial economic benefits can be achieved if an effective and efficient analysis is attained. This paper presents the use of an integrated method to enable engine health assessment using real pass-off test data of production engines obtained over a year. The proposed method is based on a well-established diagnostic technique enhanced for a highly-complex problem of a three-spool turbofan engine. It makes use of a modified optimization algorithm for the evaluation of the overall engine performance in the presence of component degradation, as well as, sensor noise and bias. The developed method is validated using simulated data extracted from a representative adapted engine performance model. The results demonstrate that the method is successful for 82% of the fault scenarios considered. Next, the pass-off test data are analyzed in two stages. Initially, correlation and trend analyses are conducted using the available measurements to obtain diagnostic information from the raw data. Subsequently, the method is utilized to predict the condition of 264 production turbofan engines undergoing a compulsory pass-off test.

量产发动机通过测试是一项强制性技术，可确保每个发动机在投入使用前均满足所需的性能标准。燃气轮机性能分析极大地支持了这一过程，并且如果获得有效和高效的分析，则可以实现可观的经济效益。本文介绍了使用集成方法进行引擎健康状况评估的方法，该方法可以使用一年中获得的生产引擎的实际通过测试数据进行评估。所提出的方法基于针对三转子涡轮风扇发动机的高度复杂问题而增强的公认的诊断技术。它利用修改后的优化算法来评估在存在部件退化以及传感器噪声和偏差的情况下发动机的整体性能。使用从代表性的适应性发动机性能模型中提取的模拟数据验证了开发的方法。结果表明，该方法在考虑的故障场景中有82％是成功的。接下来，分两个阶段分析通过测试数据。最初，使用可用的度量进行相关性和趋势分析，以从原始数据中获取诊断信息。随后，该方法用于预测264台生产涡轮风扇发动机正在接受强制通过测试的状况。使用可用的测量进行相关性和趋势分析，以从原始数据中获取诊断信息。随后，该方法用于预测264台生产涡轮风扇发动机正在接受强制通过测试的状况。使用可用的测量进行相关性和趋势分析，以从原始数据中获取诊断信息。随后，该方法用于预测264台生产涡轮风扇发动机正在接受强制通过测试的状况。