The transient features caused by local fault are of vital importance for bearing fault diagnosis in intelligent industry. Due to the uncertainty of fault forms and non-stationarity of operating conditions, fault feature distribution influenced by physical dynamic response of actual defect is always complex and irregular. Motivated by this, a convolution sparse self-learning (CSSL) is proposed to accomplish an adaptive feature enhancement. Firstly, the desired morphological structures is promoted by a two-dimensional manifold sensing. From a randomly selected fragment, time-frequency manifold learning is then applied to mine the latent structures. Therewith, this kernel is used to operate a shift-invariant sparse analysis on raw time-frequency image. Combined this rebuilt image with raw phase, enhanced signal is synthesized. Finally, the desired transient morphology can be automatically mined. Practical defective bearing data are analyzed to illustrate its effectiveness. A comparison further illustrates that the proposed CSSL is superior in morphological transient features enhancement.

Lamb-wave-based structural health monitoring (SHM) has attracted extensive attention in recent years. This article aims to realize the functions of data communication, defect detection, and energy transmission through piezoelectric transducers. In this work, the ${S}_{{0}}$ mode at 500 kHz and the ${A}_{{0}}$ mode at 150 kHz are selected as the carrier waves and the optimized excitation frequencies are determined through analytical investigation and frequency sweeping experiments. The ${S}_{{0}}$ mode is used for data communication and defect inspection due to the high excitation frequency and low dispersion properties. A single piezoelectric sensing element acts as the transmitter and a nine-element piezoelectric transducer array (PTA) is the receiver. Their roles exchange in terms of energy transmission based on the ${A}_{{0}}$ mode. Simultaneous data communication and energy transmission are achieved based on the frequency division multiplexing (FDM) strategy. After performing a matched filter on the received signals, the digital data information can be recovered under the interference of the energy transmission signal. The synthetic aperture imaging technology (SAFT) is adopted for accurately locating defects on aluminum plates. In terms of energy transmission, a constructive interference performance is achieved by the transducer array with a transmitted power of 3.81 mW. This system has great potential for health monitoring of the enclosed structure by eliminating the cumbersome wires for powering and communication.

Sensor nodes have been widely used for mechanical equipment condition monitoring, especially for enclosed metal structures. In these cases, it is important to achieve communication between the sensor nodes inside the enclosed metal structures and the outside receiver node. Conventional wired communication using cables will affect the structural integrity, and wireless communication using electromagnetic waves will be shielded by metal structures, so neither is desirable. Motivated by these issues, this article proposes a Lamb-waves-based sparse distributed penetrating communication (SDPC) system using the propagation characteristics of Lamb waves considered harmful in previous studies to accomplish wireless simultaneous data communication. Combining phase-shift keying and pulse position modulation mechanisms, a phase-position modulation (P-PM) technology is proposed to improve the communication rate. To overcome the intersymbol interference problem caused by the propagation characteristics of Lamb waves, a phase-position sparse reconstruction strategy is proposed for demodulation, which is more convenient and accurate compared with the conventional cross-correlation method. It should be noted that the maximum bit rate without bit error reaches 830.48 kbps via P-PM parameter optimization, which is 8.3 times higher than that of on – off keying modulation approach under the same experimental conditions. Finally, distributed communication experiments and simulations further verify the high accuracy, efficiency, and strong robustness of the proposed SDPC system, which can meet the communication requirements of enclosed metal structures.

Weak fault signal caused by defects in the raceway and roller is significant for bearing fault diagnosis, and the noise makes those weak fault signal hard to recognize. Sparse learning, an adaptive learning method, has great potential in weak fault signal detection under strong background noise. However, mode construction and sparse coefficient solution are the two principle problems for sparse learning. Focusing on these issues, this study proposes a new shift-invariant sparse filtering (SISF) method for weak signal denoising. It extracts modes directly from the sparse mapping process rather than sparse results, and locates the impulses in the fault signal by a convolution sparse method, so as to achieve the purpose of strengthening the weak fault feature. Primarily, the latent modes can be adaptively mined by sparse filtering from short-sequence signal segments in a self-learning way. Futher, phase space reconstruction combined with singular value decomposition is employed to enhance the latent modes. With the input of the entire signal and the mined modes provided by optimal mode searching, a convolution sparse way is applied to find the position of the modes in the signal. Finally, notice that the values at different locations represent the magnitude of the defect impulse, so removing the smaller values will achieve the effect of noise reduction. Through experiments and comparative results, it verifies that the SISF can better denoise and enhance the weak fault characteristics and is helpful for the accurate diagnosis of bearing faults.

In practical industrial environments, rotating machinery typically operates under normal conditions. As a result, the signals collected are primarily normal signals. This imbalance in the sample data diminishes the effectiveness of fault diagnosis. To address this issue, this paper produces a novel semi-supervised fault diagnosis approach based on a Siamese neural network combined with a generative adversarial network (SNNGAN) to enhance classification accuracy. Firstly, vibration signals collected are subjected to continuous wavelet transformation to obtain time–frequency representations, which are utilized for pre-training convolutional encoders in the generator and discriminator. Subsequently, a cosine similarity algorithm is employed to ensure the quality of generated samples. For generated data, set a similarity threshold. Those surpassing the threshold are assigned their corresponding labels and added to the original sample set. Otherwise, those falling below the threshold are transformed back into vibration vectors through an inverse transform and then serve as input to create new samples. Finally, fault diagnosis experiments are conducted on the newly balanced data set. In four imbalanced data experiments, the results demonstrate that SNNGAN outperforms other methods in average accuracy, G-mean, and F1 score, with average accuracy values of 0.919, 0.948, 0.927, and 0.953 for the respective datasets. Therefore, SNNGAN exhibits outstanding fault diagnosis performance under conditions of data imbalance.