Development of an intelligent gas turbine engine health monitoring and degradation prediction system

智能燃气轮机健康监测和退化预测系统的开发

• 批准号: 508307-2016
• 负责人: Liu, Jie
• 金额: $ 2.91万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618547
• 关键词: Development; intelligent; gas; turbine; engine

Gas turbine engines (GTE) have been widely used in civilian and defense sectors for generating energy and are critical to the operation of many industrial plants, aircrafts, and heavy vehicles. Any unexpected engine failure could be costly and potentially disastrous. Monitoring and predicting the GTE's health condition is therefore critically important and necessary in industries for gaining comprehensive knowledge of the engine system, improving operation reliability, and reducing maintenance cost. Because of their tremendous importance, extensive research has been conducted over the past decades in GTE health condition monitoring and prediction as a means to provide advance warning of failure, enable forecasted maintenance, and improve system qualification. Although a variety of methods have been proposed in the literature, reliable assessment of GTE current and future health conditions still remains a challenging task in both research and industrial applications due to the inherent complexities in GTE structures and time-varying operating conditions. Partnering with an Ottawa-based company, Life Prediction Technologies (LPTi), the proposed research is aimed at developing an intelligent GTE health monitoring and degradation prediction system to address this challenge. Our ultimate goal is to develop more reliable techniques and tools for GTE health condition monitoring and prediction to help Canadian companies improve asset utilization, reduce the total cost of GTE ownership, and remain competitive in the global market.

燃气涡轮机发动机（GTE）已经广泛用于民用和国防部门以产生能量，并且对于许多工业工厂、飞机和重型车辆的操作是至关重要的。任何意外的发动机故障都可能是昂贵的，并可能是灾难性的。因此，监测和预测GTE的健康状况对于获得发动机系统的全面知识、提高运行可靠性和降低维护成本在工业中是至关重要和必要的。由于其巨大的重要性，在过去的几十年中，在GTE健康状况监测和预测方面进行了广泛的研究，作为提供故障预警、实现预测维护和提高系统质量的手段。虽然在文献中已经提出了各种方法，GTE当前和未来的健康状况的可靠评估仍然是一个具有挑战性的任务，在研究和工业应用中，由于GTE结构和随时间变化的操作条件的固有复杂性。该研究与渥太华的Life Prediction Technologies（LPTi）公司合作，旨在开发一种智能GTE健康监测和退化预测系统，以应对这一挑战。我们的最终目标是开发更可靠的技术和工具，用于GTE健康状况监测和预测，以帮助加拿大公司提高资产利用率，降低GTE拥有的总成本，并在全球市场上保持竞争力。