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Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.