电子测量与仪器学报

唐智,柏林,白豪,吴过,王章旭.弱数据下多源传感融合的某试车台气路健康评估方法[J].电子测量与仪器学报,2024,38(5):10-18

弱数据下多源传感融合的某试车台气路健康评估方法

Health assessment method for gas circuit system of engine test bed based onmulti source sensor information fusion under weak data environment

DOI：

中文关键词: 发动机试车台健康评估支持高阶张量机相空间重构信息融合

英文关键词:engine test bed health evaluation support high-order tensor machine adaptive constructive phase space information fuse

基金项目:中文基金项目国家自然科学基金（52175077）项目资助

作者	单位
唐智	1.重庆邮电大学先进制造工程学院重庆400065；2.重庆大学机械传动国家重点实验室重庆400044
柏林	重庆大学机械传动国家重点实验室重庆400044
白豪	重庆川仪软件有限公司重庆401121
吴过	2.重庆大学机械传动国家重点实验室重庆400044；4.上海联影医疗科技股份有限公司上海201807
王章旭	重庆大学机械传动国家重点实验室重庆400044

Author	Institution
Tang Zhi	1.School of Advance Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 2.State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Bo Lin	State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Bai Hao	Chongqing Chuanyi Software Co., Ltd., Chongqing 401121, China
Wu Guo	2.State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China; 4.Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, China
Wang Zhangxu	State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China

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中文摘要:

航天发动机试车台作为检验发动机可靠性的关键装备，其健康状态评估对确保发动机安全运行具有重要意义。试车台气路系统具有故障模式复杂多变，多点位、多模态传感信息关联性强等特点，且存在数据积累有限、采集的健康状态样本分布不均、人工监测运行状态造成人力资源浪费以及高误警率等问题。为此，提出了基于自适应重构相空间-支持高阶张量机的健康评估模型。该方法首先通过设计E1(m)的稳定性判定准则，实现对气路系统相空间的自适应重构；其次采用张量对气路系统的多点位、多模态数据进行表征；然后基于支持高阶张量机挖掘张量样本中的多源传感关联信息与健康模式，实现对试车台气路系统的健康状态评估；最后利用中航某所发动机试车台实际试车数据，与支持向量机、决策树与朴素贝叶斯算法对比，结果表明提出方法在弱数据环境下具有良好评估能力，整体评估精度为89.7%，在极端弱数据环境，精度下降保持在8%以内。

英文摘要:

The aerospace engine test bench is a key equipment for verifying engine reliability, and its health status assessment is of great significance for ensuring the safe operation of the engine. The gas circuit system of the engine test bench has the characteristics of complex and variable fault modes, strong correlation between multi-point and multimodal sensing information, etc. Moreover, there are issues such as uneven distribution of collected health status samples, high signal noise, human resource waste caused by manual monitoring of the operating status of the gas pipeline system, and high false alarm rates. To this end, a health assessment model for test benches based on adaptive reconstruction of phase space and support for high-order tensor machines is proposed. This method first involves designing stability criterion for E1(m) to achieve adaptive phase space reconstruction of the gas path system. Secondly, tensors are used to characterize the multi-point and multimodal data of the pneumatic system. Then, a high-order tensor machine is used to mine the multi-source sensor correlation information and fault modes in tensor samples, achieving a health status assessment of the test bench pneumatic system. Finally, the proposed method is compared with the support vector machine, decision tree and plain Bayesian algorithms based on the actual test data from the engine test bench of a China National Aviation Corporation (CNAC). The results show that the proposed method has a good evaluation capability in a weak data environment, with an overall evaluation accuracy of 89.7%, and the accuracy drop is kept within 8% in an extremely weak data environment.

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