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Multi-sensor systems are very powerful in the complex environments. The cointegration theory and the vector error correction model, the statistic methods which widely applied in economic analysis, are utilized to create a fitting model for homogeneous sensors measurements. An algorithm is applied to implement the model for error correction, in whic...
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This work is the first part of a project dealing with an in-depth study of
effective techniques used in econometrics in order to make accurate forecasts
in the concrete framework of one of the major economies of the most productive
Italian area, namely the province of Verona. In particular, we develop an
approach mainly based on vector autoregressi...
Citations
... Engine sensors can fail under harsh conditions, such as high-temperature and high-pressure environments. Two primary redundancy techniques used by the engine FTC system are hardware redundancy and analytical redundancy [4,5]. Compared with hardware redundancy, analytical redundancy reduces the number of sensors measuring the same physical variable which is of great significance for simplified design and weight reduction of aeroengine [6,7]. ...
Due to the complex nature of a variable cycle engine (VCE), which has numerous control variables and working modes across a broad flight envelope, coupled with the whole engine’s degradation, the analytical redundancy method based on component-level models may not provide an accurate estimation of the sensors. Variable-weights-biases neural network (VWB Net) is proposed to construct VCE’s analytical redundancy. Unlike conventional networks whose weights and biases are fixed, VWB Net’s variable-weights and variable-biases are functions of input which greatly increase its nonlinear mapping capability by integrating input information. Variable-biases can also be used to eliminate the error between actual sensor output and estimated value quickly at the terminal node. Compared with the BP network and Dense net, VWB Net has fewer parameters, faster calculation speed, and higher accuracy. Digital simulation results of VCE parameter estimation demonstrate that VWB Net’s average relative errors are under 0.27% with calculation and parameter efficiency at least 166 times higher than that of Dense net. Hardware in the loop simulation further verifies VWB Net’s estimation accuracy and real-time calculation.
... Each dataset (i.e., a 24-element vector) includes 21 characteristic sensors for engine health data recording. With the preprocessing method, 14 sensors that are currently available onboard for many commercial turbofan engines are selected for PHM in this study [44]. Table 1 shows the description of selected sensors. ...
In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive density peaks clustering algorithm, which has the advantages of an extremely short training time and high enough precision, is employed for modelling engine fault development from the beginning of engine service, and principal component analysis is introduced to convert complex high-dimensional raw data into low-dimensional data. The model can be updated from time to time according to the accumulation of engine data to capture the occurrence and evolution process of engine faults. In order to address the problems with the commonly used data driven methods, the DP + LSTM model is employed to estimate the remaining useful life (RUL) of the engine. Finally, the proposed PHM framework is validated experimentally using NASA’s commercial modular aero-propulsion system simulation dataset, and the results indicate that the DP model has higher stability than the classical artificial neural network method in fault diagnosis, whereas the DP + LSTM model has higher accuracy in RUL estimation than other classical deep learning methods.
... Fig. 1 Aero-engine multiple-sensor distribution diagram for degradation estimation. 29 3. Multi-rate information filter algorithms ...
Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter (KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multi-sensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Square-root Cubature Information Filter (MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate information filter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment. Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system. Keywords: Aero-engine, Cubature information filter, Performance degradation, Sensor fusion, State estimation
This paper proposes the deployment of an embedded system for a fixed-wing unmanned aerial vehicle (UAV), including the implementation of an autonomous navigation algorithm for it. An Extended Kalman Filter (EKF) based Global Positioning System (GPS) / Inertial Navigation System (INS) integration with cointegrated measurements from inertial sensors has been developed in order to achieve localization on a mission. The experimental results with data from a flight test show that this approach can perform accurate state estimation.
