Sufficient data is necessary for intelligent fault diagnostic approaches. However, in practice, it is often the case that only limited fault data is available due to various reasons, making it a challenge to accurately identify the health condition of bearings. To deal with the limited fault data issue, data augmentation strategies, such as generative adversarial network (GAN), are widely utilized. However, GAN has the disadvantages of being difficult to train and restricted ability to generate new data when the fault sample size is limited. Specifically, GAN requires a long training time and abundant training data to make the distribution of generated data closer to the distribution of actual data. This article presents a novel data augmentation approach with compressed sensing for fault diagnosis of bearings to better address the issue of limited fault data. The generated data by compressed sensing is diverse. In addition, the generated data is highly similar to the original data in frequency domain, thus retaining the main feature information of the original data. Furthermore, data augmentation achieved through compressed sensing requires less training data and has lower computational complexity. For bearing fault diagnosis under limited failure data, the limited fault data is first augmented based on compressed sensing, allowing for high fidelity reconstruction and high diversity data generation. Then, the augmented data is utilized to train a deep convolutional neural network to automatically learn and extract features for fault identification. The effectiveness of the presented approach is verified using two bearing datasets.

Rotating machinery usually runs at time-varying speeds, which often brings failures, especially compound faults. However, currently, there is still a lack of satisfactory coupled fault diagnosis methods at variable speeds. Several methods have been developed, which try to eliminate rotational speed influences by converting nonstationary fault characteristic frequencies into stationary ones. But it brings new problems of computational efficiency and accuracy, and requires an auxiliary tachometer to measure the rotational speed. Time-frequency analysis can solve the problems, and extract time-frequency curves for fault diagnosis through time-frequency representation (TFR). However, for signals containing multiple faults at variable speeds, uninterested components will be generated when extracting multiple time-frequency curve (MTFC), which makes it difficult for compound fault diagnosis. To solve these problems, a novel MTFC classification method for tacho-less and resampling-less compound bearing fault detection under time-varying speed conditions is proposed in this paper. Here the TFR characteristics of a multi-fault vibration signal is analyzed, and a compound fault diagnosis strategy based on MTFC classification is developed. Firstly, MTFC is extracted by a local peak search method. Then, according to the relationship between instantaneous shaft rotational frequency, instantaneous fault characteristics frequency and their respective harmonics, two classification criteria for curves are proposed and MTFC is classified into interested curves and uninterested curves. Finally, the average ratios between interested curves are matched to theoretical fault characteristic coefficients to determine the fault type. Case studies on rolling bearing experiments verified the effectiveness and superiority of the proposed method.

The rapid development of additive manufacturing (AM) has facilitated the creation of bionic lightweight, energy-absorbing structures, enabling the implementation of more sophisticated internal structural designs. For protective structures, the utilization of artificially controlled deformation patterns can effectively reduce uncertainties arising from random structural damage and enhance deformation stability. This paper proposed a bionic corrugated lightweight honeycomb structure with controllable deformation. The force on the onset state of deformation of the overall structure was investigated, and the possibility of controlled deformation in the homogeneous structure was compared with that in the corrugated structure. The corrugated structures exhibited a second load-bearing capacity wave peak, with the load-bearing capacity reaching 60.7% to 117.29% of the first load-bearing peak. The damage morphology of the corrugated structure still maintained relative integrity. In terms of energy absorption capacity, the corrugated lightweight structure has a much stronger energy absorption capacity than the homogeneous structure due to the second peak of the load carrying capacity. The findings of this study suggested that the combination of geometric customization and longitudinal corrugation through additive manufacturing offers a promising approach for the development of high-performance energy-absorbing structures.

As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB.