Jan Ma

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach.

Electromechanical actuators are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition multifractal detrended fluctuation analysis and probabilistic neural network. First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions. Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction.

The Anscombean conception of intentional action endorses that an action's being intentional is in virtue of its internal structure, rather than any extra ingredient. Anscombe (2000) calls it the calculative order "that is there whenever actions are done with intentions," which features its teleological aspect. In his work reviving this tradition, Thompson (2008) highlights its temporal aspect. With the help of the concept "compound action," we shall see that typical intentional action attends to both aspects. The synthesis is met by shifting from the aspect of action to agency. To act intentionally, an agent needs not only the capacity for calculating from a general end to a specific means, but also the capacity for planning: coordinate stages of different actions over time hierarchically, by occupying a certain kind of slots.