Development of intelligent system monitoring and fault detection techniques for spacecraft is of a great interest in the space engineering. In this paper, we propose a “data-driven” anomaly detection framework for spacecraft telemetry data using dimensionality reduction and clustering techniques. In this framework, we first apply dimensionality reduction or/and clustering algorithms to a normal training data set, so that we obtain statistical models representing the normal behavior of spacecraft. After the training, we monitor test data sets and detect anomaly if any, by using the obtained models. This framework is so comprehensive that a variety of clustering, dimensionality reduction and their hybrid algorithms can be used with it. In the experiment, we tested several algorithms on the past artificial satellite data, and found that a hybrid method called VQPCA is more suitable for modeling high-dimensional and multi-modal telemetry than others.
To improve test capability at intermittent blow-down supersonic wind tunnels, diffuser performance is important to keep the starting pressure ratio as low as possible. A parametric study on the tunnel geometry for the JAXA 1m × 1m supersonic wind tunnel (JSWT) was conducted by numerical simulation. It was found that the diffuser part with a shock train has large effects on the starting pressure ratio. It was also indicated that oblique shock waves generated from the model support strut and the second throat ramp contributed the efficient pressure recovery. To improve the diffuser performance, we propose a design methodology considering these shock phenomena and designed a modified geometry for the JSWT. Although the cross-sectional area distribution was only changed from the original one, its higher performance was confirmed numerically. This design concept was also validated experimentally with a 10% scale model tunnel at a Mach number of 3.0.
This study established a fiber-optic-based impact identification technique for carbon fiber reinforced plastic (CFRP) foam-core sandwich structures. Multiplexed fiber Bragg grating (FBG) sensors were embedded between the CFRP facesheet and the foam core, and measured dynamic strain values were utilized for predicting impact location and load history. In general, low-velocity impact loading, inducing barely visible impact damage (BVID) in practical application, has long impact duration. Thus a straightforward identification technique was proposed based on an assumption that the deformation of the impacted structure can be considered quasi-static. This study began by evaluating measurement accuracy of multipoint dynamic strain sensing system consisting of the multiplexed FBG sensors and an arrayed waveguide grating (AWG) filter. Impact identification test was then conducted using a CFRP foam core sandwich panel, confirming that impact location and force history can be predicted with satisfactory accuracy by using the proposed fiber-optic-based technique.
A microwave power beaming system was developed to realize wireless power supply to a Micro Aerial Vehicle. This system consists of transmitting, tracking, and receiving systems. In the transmitting system, a 5.8GHz microwave beam was irradiated from an active phased array antenna. Transmitting power was 4W and the beam divergence angle was 9deg. In the tracking system, a 2.45GHz pilot signal was detected by a two-dimensional tracking antenna and the position was deduced though the software retro-directive function. The maximum tracking error was 1.97deg in the azimuth direction and 1.79deg in the radial direction. In the receiving system, a light-weight flexible patch rectenna was developed using felt pad as substrate. The maximum rectenna efficiency of 45.3% was obtained with a 100Ω road at 63mW input power. By integrating these systems, auto-tracking wireless power supply was demonstrated to a MAV model circling at the altitude of 1,500mm. As a result, a motor was kept rotated. Received power was 24.3mW at maximum and 17.6mW on average and the total transmission efficiency was estimated at 0.60%.