Fault detection technology combining ResNet and BiLSTM for multielectric aircraft based on Bayesian optimization

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.

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 multielectric- aircraft fault-detection experiment is designed, and the detection performance under different fault modes is analyzed.

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 model. In a comparative experiment of fault detection, to verify the effectiveness of the proposed method, comparative experiments were conducted with commonly used methods, including ResNet-BiLSTM models without Bayesian optimization, CNNs, and BiLSTM. The results show that the proposed Bayesian optimization-based method can achieve effective fault-detection results in various fault modes and that compared with those of existing methods, the accuracy and real-time performance of this method are better.

The proposed method can effectively detect and classify different faults in various operation modes, which helps achieve accurate analysis of faults for multielectric aircraft and provides very powerful technical support for flight safety.