With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.

To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge from a dynamic simulation model to an actual planetary gearbox. Massive amounts of different fault simulation data are collected by creating a dynamic simulation model of a planetary gearbox. A fresh deep transfer learning network model is built by fusing one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods. The network model is used to learn domain invariant features from simulated data, thereby enabling fault diagnosis on real data. The fault diagnosis experiment is verified by using the Drivetrain Diagnostics Simulator test bench. The validity of the proposed means is evaluated by comparing the diagnostic accuracy of various means on various diagnostic tasks.

Benefiting from the progress of artificial intelligence, the deep learning-based data-driven fault diagnosis procedure has gradually become a preferred and reliable option. Although this kind of end-to-end diagnosis mode can achieve satisfactory results in most conventional conditions, the existing methods may not be able to guarantee the diagnostic accuracy when noise is a prominent issue. To address the above problem, the deep rational attention network (DRANet) is developed in this article. In the DRANet, a pseudo soft threshold function is developed to further modulate the attention weights in a convolutional neural network. Besides, the threshold is automatically determined during network optimization. The attention weight modulate strategy driven by the pseudo soft threshold function accords with the idea of threshold denoising in signal processing and thus endows the deep architecture with the ability to further neglect the features with little contribution. Experimental vibration signals coupled with different types of noise are employed to evaluate the efficacy of the proposed DRANet. Results show that in comparison with other methods, the method of this work holds a more outstanding diagnostic performance, especially in the case of low SNR.

Determining the metallicity and semiconductivity of a multi-walled carbon nanotube (MWCNT) bundle plays a particularly vital role in its interconnection with the metal electrode of an integrated circuit. In this paper, an effective method is proposed to determine the electrical transport properties of an MWCNT bundle using a current–voltage characteristic curve during its electrical breakdown. We established the reliable electrical nanoscale contact between the MWCNT bundle and metal electrode using a robotic manipulation system under scanning electron microscope (SEM) vacuum conditions. The experimental results show that the current–voltage curve appears as saw-tooth-like current changes including up and down steps, which signify the conductance and breakdown of carbon shells in the MWCNT bundle, respectively. Additionally, the power law nonlinear behavior of the current–voltage curve indicates that the MWCNT bundle is semiconducting. The molecular dynamics simulation explains that the electron transport between the inner carbon shells, between the outermost carbon shells and gold metal electrode and between the outermost carbons shells of two adjacent individual three-walled carbon nanotubes (TWCNTs) is through their radial deformation. Density functional theory (DFT) calculations elucidate the electron transport mechanism between the gold surface and double-wall carbon nanotube (DWCNT) and between the inner and outermost carbon shells of DWCNT using the charge density difference, electrostatic potential and partial density of states.

To study the influence of the geometric structure of the probe coil on the electromagnetic characteristics of the eddy current probe in the process of eddy current testing, based on the principle of eddy current testing, different probe coil models were established using finite element software. These geometric structure parameters include the difference between the inner and outer radius, thickness, and equivalent radius. The magnetic field distribution around the probe is simulated and analyzed under different parameters, and the detection performance of the probe is judged in combination with the change rate of the magnetic field around the probe coil. The simulation results show that at a closer position, increasing the difference between the inner and outer radii, reducing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At a far position, reducing the difference between the inner and outer radii, increasing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At the same time, the accuracy of the simulation data is verified by comparing the theoretical values with the simulated values under different conditions. Therefore, the obtained conclusions can provide a reference and basis for the optimal design of the probe structure.

Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation (SAX) is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the SAX ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation (ESAX) is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the ESAX into usable digital feature vectors, the bag-of-words (BoW) model in natural language processing (NLP) is employed to perform the counting statistics of the fault-related words, and the Laplacian score (LS) algorithm is then utilized to rerank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments.