摘要
针对多电飞行器在复杂运行模式下故障检测的精确性与实时性问题,开发了基于贝叶斯优化ResNet-BiLSTM模型的故障检测技术。首先设计以残差网络为核心的多层次特征提取模块,实现故障特征的多层次表达,以提升故障检测的实时性;接着构建以双向长短期记忆网络为核心的多尺度特征提取模块,获取长期依赖的故障特征信息,以提升故障检测的准确性;进而提出基于贝叶斯优化的超参数寻优方法,进一步提升故障特征的学习效果;最后,设计多电飞行器故障检测实验方案,并开展不同故障模式下的性能分析。结果表明,所提方法可有效实现多种故障模式下的故障诊断,并且检测准确率和实时性均优于现有方法,有助于提升多电飞行器故障检测性能,为飞行安全提供有力的技术保障。
[Objective]Due to the increasing system complexity,multielectric aircraft will experience various malfunctions,seriously affecting their safe and stable operation.However,traditional fault-detection methods are generally based on preset judgment rules and fixed detection thresholds,which are difficult to adapt to the dynamic changes in the operation status of aircraft.In addition,traditional fault-detection methods based on statistical data and expert experience lack learning ability and are,therefore,unable to detect unknown faults.Moreover,handling problems such as large data and low analysis efficiency using traditional methods is difficult,making it impossible to achieve real-time diagnosis.Therefore,there is an urgent need to develop an efficient fault-detection technology to achieve accurate and real-time fault detection in complex operating modes for multielectric aircraft.[Methods]First,a multilevel feature extraction module with ResNet as the core unit is built to obtain multilevel feature expressions about faults,improving the real-time performance of fault diagnosis.In addition,by constructing a multiscale feature extraction module with a BiLSTM network as the core unit,long-term-dependent features during fault evolution can be obtained,thereby ensuring fault diagnosis accuracy.Second,a Bayesian optimization algorithm-based hyperparameter optimization method with fault detection accuracy as the objective function is proposed,which helps improve the learning effect of fault features and further enhance the fault detection performance.Finally,a multielectricaircraft fault-detection experiment is designed,and the detection performance under different fault modes is analyzed.[Results]In the hyperparameter optimization method,the accuracy of the fault detection model on the training set is selected as the objective function,and an acquisition function is used to maximize the objective-function solution,thereby obtaining the optimal hyperparameter combination that affects the detection performance in the mod
作者
张建良
季瑞松
ZHANG Jianliang;JI Ruisong(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第10期10-17,共8页
Experimental Technology and Management
基金
教育部新工科研究与实践项目“信息化、智能化背景下电气工程及其自动化专业的改造升级与创新实践”(E-ZDH20201612)。
