Machine learning-based defect identification has emerged as a promising solution to improving the defect accuracy of the aero-engine blade. This solution adopts machine learning classifiers to classify the types of defects. These classifiers are trained to use features collected in ultrasonic echo signals. However, the current studies show the potential number of features, such as statistic values, for identifying defect reaches a number more than that offered by an ultrasonic echo signal. This necessitates multiple acquisitions of echo signal and increases manual effort, and the feature obtained from feature selection is sensitive to the characteristic of the classifier, which further increases the uncertainty of the classifier result. This paper proposes an ensemble learning technique that is only based on few features obtained from an echo signal and still achieves a high accuracy of defect identification as that in traditional machine learning, eliminating the need for multiple acquisitions of the echo signal. To this end, we apply two well-known ensemble learning classifiers and simultaneously compare three widely used machine learning models on defect identification of blades. The result shows that the proposed ensemble learning models outperform machine learning-based models with an equal number of features. In addition, the two-feature-based ensemble learning model reaches an accuracy close to that of multiple statistic features-based machine learning models, where features are obtained from multiple collections of the signal.

Ultrasonic echo estimation has played an important role in industrial non-destructive testing and analysis. The ability to estimate parameters in the ultrasonic echo model is crucial to ensure the effectiveness of practical ultrasonic testing applications. In this paper, a scheme called ABIDE for identifying both multiple noises in the echo signal and the distribution of the denoised signal is proposed for ultrasonic echo signal parameter estimation. ABIDE integrates complementary ensemble empirical mode decomposition and the synchrosqueezed wavelet transform (CEEMD-SSWT) as well as the expectation maximization (EM) algorithm. The echo signal is split into a series of IMF components and a residual with the help of CEEMD, and then these IMFs are classified into the noise-dominant part and signal-dominant part by analyzing the correlation of each IMF and the echo signal using grey relational analysis. Considering the effect of noise in the signal-dominant part, SSWT is adopted to remove the noise in the signal-dominant part. Lastly, the signal output by the SSWT algorithm is used for reconstructing a denoised signal combined with the residual from CEEMD. Considering the distribution characteristic of the denoised signal, the EM algorithm is used to estimate parameters in the ultrasonic echo model. The relative performance of the proposed scheme was evaluated on synthetic data and real-world data and then compared with the state-of-the-art methods. Simulation results on synthetic data show that ABIDE outperforms the state-of-the-art methods in parameter estimation. Physical results on real-world data show that the proposed scheme has a greater PCC value in estimating echo model parameters. This paper also shows that ABIDE requires less convergence time than competitive methods.

Balloon-borne based solar unmanned aerial vehicle (short for BS-UAV) has been researched prevalently due to the promising application area of near-space (i.e., 20–100 km above the ground) and the advantages of taking off. However, BS-UAV encounters serious fault in its taking off phase. The fault in taking off hinders the development of BS-UAV and causes great loss to human property. Thus, timely diagnosing the running state of BS-UAV in taking off phase is of great importance. Unfortunately, due to lack of fault data in the taking off phase, timely diagnosing the running state becomes a key challenge. In this paper, we propose Ponder to diagnose the running state of BS-UAV in the taking off phase. The key idea of Ponder is to take full advantage of existing data and complement fault data first and then diagnose current states. First, we compress existing data into a low-dimensional space. Then, we cluster the low-dimensional data into normal and outlier clusters. Third, we generate fault data with different aggression at different clusters. Finally, we diagnose fault state for each sampling at the taking off phase. With three datasets collected on real-world flying at different times, we show that Ponder outperforms existing diagnosing methods. In addition, we demonstrate Ponder’s effectiveness over time. We also show the comparable overhead.