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The electro-hydraulic actuator plays a significant role in the automatic flight control system, so it is vital to predict the remaining useful life (RUL) for the electro-hydraulic actuators. Relevance vector machine (RVM) is flourishing in the field of RUL prognostics and gradually applied to the prediction of complex systems or components, but the general RVM cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse RVM model which is only based on historical data set could cause a large prediction error in the long term. To deal with these plights, an optimized incremental learning algorithm based on RVM is presented taking full advantage of the on-line updating samples to improve the precision of prognostics.

The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostic methods is vital for providing reasonable preventive maintenance schedules and preventing unexpected failures. Particle filter has been proved to be a traditional approach to deal with actuator prognostic problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process, this paper presents a hybrid framework combining support vector regression (SVR) and particle filter (PF). The SVR output prediction results are employed as the “measurements” for the subsequent PF algorithm. To accomplish the accurate prognostics for actuator fault of civil aircraft, an improved PF based on Kendall correlation coefficient is put forward to solve the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the SVR-PF hybrid approach has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches.

The air turbine starter (ATS) is an equipment employed for initiating aircraft engines, in which rolling bearings play a central role. Predicting the remaining useful life (RUL) of rolling bearings constitutes a challenging task within the domain of prognostics and health management (PHM). Improving computational efficiency to predict the RUL of bearings is a core problem addressed in this paper, and thus, this paper introduces the parallel causal convolutional hierarchical dilated temporal convolutional network (HD-TCN). HD-TCN effectively captures long-term temporal dependencies and significantly enhances computational efficiency compared to serial convolutional recurrent neural networks (RNNs). In addition, the introduction of the feature data stacking (FDS) module allows features from different hierarchical levels and dimensions to participate in the prediction task, ensuring both computational efficiency and prediction accuracy. To consolidate the extracted multidimensional features, the network incorporates the matrix multiplication dimensionality reduction transformation (MMDRT) module, which reduces the dimensionality of the data and further improves the computational efficiency as well. The introduction of the MMDRT module resulted in reductions of 43.4% in root mean square error (RMSE). The proposed method achieved reductions in RMSE by 38.3% and 46.4% compared to the transformer and long short-term memory (LSTM) models, respectively. Finally, the effectiveness of the proposed method is validated using the XJTU-SY data set and civil aircraft bearing components RUL prediction test bench bearing data. The results show that the proposed approach can predict the RUL of bearings with high accuracy. [ABSTRACT FROM AUTHOR]

Rolling bearing is the core part of rotating mechanical equipment, so developing an effective remaining useful life prognostics method and alarming the impending fault for rolling bearing are of necessity to guarantee the reliable operation of mechanical equipment and schedule maintenance. The relevance vector machine is one of the substantially used methods for remaining useful life prognostics of rolling bearing. However, the accuracy generated by relevance vector machine drops rapidly in the long-term prognostics. To remedy this existing shortcoming of relevance vector machine, a novel hybrid method combining grey model, complete ensemble empirical mode decomposition and relevance vector machine are put forward. In the hybrid prognostics framework, the grey model is applied to gain a "raw" prediction result based on a trained model and produce an original error sequence. Subsequently, a new smoother error sequence reconstructed by complete ensemble empirical mode decomposition method is used to train relevance vector machine model, by which the future prediction error applied to correct the raw prediction results of grey model is projected. Ultimately, the online learning technique is used to implement dynamic updating of the "old" hybrid model, so that the remaining useful life of rolling bearing throughout the run-to-failure data set could be accurately predicted. The experimental results demonstrate the satisfactory prognostics performance. [ABSTRACT FROM AUTHOR]

The electro-hydraulic actuator plays a significant role in the automatic flight control system, which is critical to the flight security, so it is vital to monitor the state of health and predict the remaining useful life for the electro-hydraulic actuator. Relevance vector machine is flourishing in the field of remaining useful life prediction and gradually applied to the prediction of complex systems or components. However, the general relevance vector machine cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse relevance vector machine model which is only based on historical data set could cause a great prediction error in the long term. To deal with these plights, an optimized incremental learning and on-line training algorithm based on the relevance vector machine is presented taking full advantage of the on-line updating samples to improve the precision of prediction, and in order to remedy the problem of computational complexity and improve the computational efficiency, the "sample entropy" is introduced as an effective signature of the electro-hydraulic actuator's health to effectively reduce the size of training samples. The effectiveness of the proposed on-line training approach is evaluated through an experiment of an electro-hydraulic actuator, and the results show a satisfactory prediction accuracy as well as an improvement in the computational efficiency for remaining useful life prediction. [ABSTRACT FROM AUTHOR]