Modern Industrial Fields Research Articles

A multi-rate sensor fusion and multi-task learning network for concurrent fault diagnosis of hydraulic systems

Hydraulic systems are widely used in key modern industrial fields such as mechanical manufacturing, aerospace, and heavy machinery, and their efficient and reliable operation is crucial to ensuring production safety and efficiency. However, hydraulic systems often experience concurrent faults, such as pump failures, valve blockages, pipeline leaks, and fluid contamination, which pose significant challenges to the fault diagnosis in hydraulic systems. This paper introduces a multi-task learning network that deconstructs the challenge of concurrent fault diagnosis into specific sub-tasks, enabling the simultaneous identification and classification of multiple hydraulic components' faults. Automatic channel filtering is designed to screen out sensitive channels of each component from multi-rate sensors. A dual-flow model is used to feature extraction, which can simultaneously extract the local spatial features and global semantic information. Then, four classification models are designed to identify the extracted shared features. An uncertainty weight loss is also proposed to balance the loss of different tasks. The experimental results show that our model significantly outperforms traditional methods and other popular multi-output methods in diagnosing concurrent faults.

Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor.

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time-frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time-frequency format. Subsequently, a CNN model is developed to extract and classify these time-frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.

A lightweight and accurate recognition algorithm of pointer meter based on improved Deeplabv3+ for inspection robots

Pointer meter is widely utilized in the fields of modern industry. Nowadays, intelligent inspection robots are gradually employed in place of labors for inspection task. Pointer meter recognition is one of the most important tasks of inspection robots. This article presents a lightweight pointer meter recognition algorithm, which is suitable for deployment on inspection robots. First, pointer meter is identified by pruned YOLOv5. Afterwards, the dial and the pointer are segmented through improved Deeplabv3+ in which the JPU and depthwise separable convolutions are utilized in lieu of dilated convolutions. After perspective transform and central-line extraction of pointer, the reading of the pointer meter can be determined using the angle method. Experimental results verify the FPS of pruned YOLOv5 improves 13.18% compared to original YOLOv5 and the FPS of improved Deeplabv3+ improves 45.74% compared to original Deeplabv3+. Additionally, the reading accuracy of the algorithm is 98.40% and average fiducial error is 0.32%, which indicate good accuracy. The relative standard deviation of reading is less than 1.4%, which indicates good stability of proposed algorithm. This study proposes a lightweight and accurate pointer meter recognition algorithm based on improved Deeplabv3+, the algorithm is ported to NVIDIA Jetson TX2 NX to verify its stability and accuracy.

Experimental Analysis of the Low-Velocity Impact and CAI Properties of 3D Four-Directional Braided Composites after Hygrothermal Aging.

Three-dimensional braided composites (3D-BCs) have better specific strength and stiffness than two-dimensional planar composites (2D-PCs), so they are widely used in modern industrial fields. In this paper, two kinds of 3D four-directional braided composites (3D4d-BCs) with different braided angles (15°, denoted as H15, and 30°, denoted as H30) were subjected to hydrothermal aging treatments, low-velocity impact (LVI) tests, and compression after impact (CAI) tests under different conditions. This study systematically studied the hygroscopic behavior and the effect of hygrothermal aging on the mechanical properties of 3D4d-BC. The results show that higher temperatures and smaller weaving angles can significantly improve the moisture absorption equilibrium content. When the moisture absorption content is balanced, the energy absorption effect of 3D4d-BC is better, but the integrity and residual compression rate will be reduced. Due to the intervention of oxygen molecules, the interface properties between the matrix and the composite material will be reduced, so the compressive strength will be further reduced. In the LVI test, the peak impact load of H15 is low. In CAI tests, the failure of H15 mainly occurs on the side, and the failure form is buckling failure. The main failure direction of H30 is 45° shear failure.

Study on Vibration Damping and Buffering Mechanism of Soles With Layered Gradient Lattice Structure

The damping performance of shoes is becoming increasingly important. In general, lattice materials have excellent mechanical properties, such as high specific strength and high specific rigidity, which are widely used in various fields of modern industry. In this paper, four different lattice structures are used in soles and analyzed for shock absorption. Firstly, hierarchical gradient structures are designed based on the four lattice structures, each with six series of gradients, respectively. Secondly, the modal test of the ordinary sole was carried out, and the finite element analysis results are compared and verified. Finally, the vibration reduction effect of each sole is evaluated by the acceleration vibration level difference, power flow, and the vibration energy flux ratio. The results show that the sole with an X lattice structure and Series 6 gradient has the best vibration damping performance. This paper provides a method and basis for the design and optimization of shoes in the future.

Micro-sized droplet formation by interaction between dielectric barrier discharge and liquid

Liquid atomization technology is one of the applications in various fields of modern industry because it improves reactivity, diffusion, and permeability of liquids. However, existing atomization technologies are severely limited by the physical and chemical properties of the solution or the object to be treated, and there is a growing need to develop atomization technologies that solve these problems. We have developed a device that atomizes liquids to the nanoscale based on the interaction with a dielectric barrier discharge, which enables the atomization of various types of solutions, including water-based and oil-based solutions. Herein, we report the results of visualizing the dynamics of liquid atomization using a high-speed camera. The device atomizes solutions in three modes: instability of the solution jet; physical fragmentation of the solution droplets by the impact of the plasma streamer; and collapse of the droplet surface and generation of a smoke-like mist during the streamer ejection from the solution droplet. The combined and repeated action of these three modes on the produced microdroplets is expected to result in nano-sized mists of the solution.

Cooperative Exploration Model of Coal–Lithium Deposit: A Case Study of the Haerwusu Coal–Lithium Deposit in the Jungar Coalfield, Inner Mongolia, Northern China

Lithium (Li) is an important strategic metal mineral resource, irreplaceable in the fields of modern industry, new energy technology, nuclear fusion, and energy storage devices. Li is an important supplement to traditional strategic metal mineral resources and has become an important avenue of mineral resource exploration. Therefore, there is an urgent need to establish a cooperative exploration model of coal and Li deposits to lay a theoretical foundation from the perspective of technical optimization and economic rationality. This study is based on the distribution characteristics of the Haerwusu coal–Li deposit, and the effectiveness of the response to exploration techniques, the economical and effective exploration techniques, the reasonable exploration engineering design, and resource estimation parameters is investigated. Therefore, the cooperative exploration model of the coal–Li deposit is established. The high-Li areas in the surface of the Haerwusu Li deposit is distributed near the B1 anticline or in the middle area between the X1 syncline and the B1 anticline, and the vertical distribution of Li content is irregular. The exploration techniques, exploration engineering design, and resource estimation are reviewed and optimized. According to the geological, geochemical, and geophysical conditions, a reasonable cooperative exploration model for coal–Li deposits is established from the two aspects of the coordination of multi-mineral exploration and the coordination of various exploration technologies. The determination of the coal–Li deposit cooperative exploration model has important practical significance for improving the resource security system.

HCDR-based motion planning for mobile manipulators with the continuous trajectory task

Mobile manipulators have been widely used in modern industrial fields due to their advantages of large operating workspace and high motion dexterity. For the application, the path planning has been a core foundation for its motion control. However, as the environmental complexity and performance requirements increase, existing methods are limited by low execution efficiency, and lack of comprehensive consideration of motion performances. A motion planning technique is proposed for mobile manipulators with continuous trajectory task. In this technique, a hierarchical constraints dimension reduction (HCDR) strategy is presented based on the task decomposition and performance requirements analysis of each stage. HCDR strategy aims to improve the execution efficiency of the algorithm through local operations of performance constraints and the local simplification of the distance calculation at different stages of the task. Models of inverse kinematics and performance indexes are established to provide basis for constraint conditions, including distance models with different accuracy, manipulability index model and trajectory tracing error model. Two optimization strategies are presented to solve the motion planning problem considering the overall performance of the system. And simulation results show that the three-level strategy has the higher convergence efficiency than the bi-level strategy, as well as can guarantee the motion performance of the system.

Dynamic material flow analysis of antimony resources in China

Antimony is recognized as a crucial material due to its extensive application in modern industrial fields. China is the largest antimony supplier and consumer in the world. However, few studies focus on antimony flows and stocks in China from a whole life cycle perspective. This study investigates the features of antimony flows and stocks in China from 2011 to 2020 by conducting dynamic material flow analysis. The results show that China’s antimony production had drastically decreased during 2011-2020, indicating a high supply risk to the antimony industry chain. Also, the corresponding environmental pollution from the antimony life cycle is serious and should be mitigated. In addition, the majority of secondary antimony production came from end-of-life lead-acid batteries and bearing gears. To ensure sustainable antimony supply and maintain clean antimony production, it is necessary to prepare appropriate antimony management policies so that different stakeholders can work together to improve the sustainable management of antimony resource in China.

Functionalized ionic liquids based on DODGA with enhanced Eu(III)/U(VI) separation efficiency in highly acidic environment

Lanthanides/actinides separation is of importance in many modern industrial engineering fields, e.g. the circulation of the nuclear fuel and rare earth. N,N-dioctyl diglycol amic acid (briefly as DODGA) is known as an effective extractant for extraction of rare earth metal. In this work, two extractants, [N1888][DODGA] and [N4444][DODGA], were developed by using quaternary ammonium as cations and DODGA as anion. The resultant ionic liquids were used to separate europium from europium-uranium binary component system. The influence of several factors (nitric acid concentration, contact time, temperature, metal ions, extractant concentration, salting agent concentration and stripping) were analyzed. The coordination chemistry of Eu(III) with [N1888][DODGA] and [N4444][DODGA] were investigated by the mole-ratio method. The experimental results confirmed that the separation factors (SF) of Eu(III)/U(VI) were reached 55.44 and 60.35 for [N1888][DODGA] and [N4444][DODGA]. The stoichiometry of the complex of Eu(III) and [N1888][DODGA] or [N4444][DODGA] were determined as 1:3. Excellent extraction of the target metals and prominent separation factors using these ionic liquids showed that they were viable to separate lanthanides from actinides.

Visualizing Catalytic Dynamics Processes via Synchrotron Radiation Multitechniques.

The importance of catalysts today as workhorses in most modern industrial fields cannot be downplayed. As a result, rational design and engineering of targeted catalysts have emerged as key objectives and are dependent on in-depth understanding of complex catalytic dynamics. Synchrotron radiation (SR) light sources with rich advanced experimental methods are being recognized as a comprehensive characterization platform, which can draw a full picture on such multiparameter-involved catalysis under actual working conditions. Herein, the recent progress of catalytic dynamics process studied by the means of various SR techniques is summarized. In particular, SR-based spectroscopic, scattering, and imaging investigations on true catalysts are first introduced with the potential of in situ and operando characterizations. Apparently, the limitations from single SR technique naturally prompt a simple combination of SR techniques to better understand the whole catalysis process. Moreover, the discrepancies among various online testing facilities and batches of samples, along with random/systematic errors introduced by traditional intermittent/asynchronous measurement make it imperative to develop more prolific systems, complementary of multiple SR techniques for deep probing of dynamic catalytic processes. It is believed that the booming new light sources can further enrich the current multiple SR techniques, and thus may realize the true visualization on future catalytic dynamic processes.

Modeling of Output Spectra and Numerical Simulation of Thulium-doped Broadband Fiber Light Sources at 1400-1520nm, 1800-2000nm

Lasers have a great influence in industrial processing and biomedical fields. Thulium-doped fiber lasers have good prospects for communication transmission and polymer welding. In this paper, the research method is as follows: in the pump light at 0. 79um, the values of Thulium-doped fiber, including its core radius, spontaneous radiation rate, cross-sectional absorption area, etc., are preset in advance. Then, by changing the fiber length, pumping power, and doping concentration of the fiber, the curve between the output spectrum of ASE light and the wavelength of ASE light is demonstrated. Finally, the peak power of the Thulium-doped broadband light source fiber is about 1. 6× w at L=3m, N=4× / , Pp=0. 2w. The output spectrum of thulium-doped fiber is mainly concentrated around the 2000nm wavelength band, and examples of thulium-doped fiber lasers for modern medical and industrial fields are presented. The thulium-doped fiber is the main direction of future research and the outlook for the future.

Research progress and prospect of friction stir welding of copper and copper alloys

Copper and copper alloys are widely used in various industries due to the excellent properties, and they are indispensable materials in modern industrial fields. At present, friction stir welding technology is the most promising welding method for copper and copper alloys, and there have been a series of studies on copper and copper alloys FSW field. This paper summarizes the research achievements in this field in the past 30 years, and aims to provide a comprehensive and systematic summary for researchers in this field. In addition, combined with the authors team’s research and experience in this field and existing relevant references, we point out the development direction of this field in the next stage.

Electrodeposition-based fabrication of graphene/copper composites with excellent overall properties

Graphene reinforced copper composites (Gr/Cu) have considerable potential applications in many fields of modern industry. However, the present preparation methods are complicated, expensive, and time-consuming, which restricts the large-scale application. Therefore, it is vital to find an industrial method to prepare Gr/Cu composites with overall properties. In this work, Gr/Cu composites were prepared by combining electrodeposition of Gr/Cu powders, thermal reduction, and sintering (denoted as the EP-TS method). The composite shows outstanding overall properties when the current density is 62.5 mA/cm2 and the graphene oxide (GO) concentration in the electrolyte is 0.1 g/L. The addition of graphene promotes the nucleation process and refines the grains resulting in improved mechanical properties. During the process of electrodepositing and thermal reduction, the copper matrix is purified and most of the oxygen-containing functional groups of GO are removed. And the GO structure is partially repaired resulting in improved electrical properties. Compared with the one-step electrodeposition method, the graphene in the composites has fewer oxygen-containing functional groups which guarantee the thermal stability of the Gr/Cu composite. Our method provides a promising prospect for rapid mass production of graphene reinforced copper matrix composites with excellent overall properties, which has good compatibility with current copper industry technology.

Screen-Printable Silver Paste Material for Semitransparent and Flexible Metal-Semiconductor-Metal Photodetectors with Liquid-Phase Procedure.

Photodetectors are widely applied in modern industrial fields because they convert light energy into electrical signals. We propose a printable silver (Ag) paste electrode for a highly flexible metal–semiconductor–metal (MSM) broadband visible light photodetector as a wearable and portable device. Single-crystal and surface-textured silicon substrates with thicknesses of 37.21 μm were fabricated using a wet etching process. Surface texturization on flexible Si substrates enhances the light-trapping effect and minimizes reflectance from the incident light, and the average reflectance is reduced by 16.3% with pyramid-like structures. In this study, semitransparent, conductive Ag paste electrodes were manufactured using a screen-printing with liquid-phase process to form a flexible MSM broadband visible light photodetector. The transmittance of the homemade Ag paste solution fell between 34.83% and 36.98% in the wavelength range of visible light, from 400 nm to 800 nm. The highest visible light photosensitivity was 1.75 × 104 at 19.5 W/m2. The photocurrents of the flexible MSM broadband visible light photodetector were slightly changed under concave and convex conditions, displaying stable and durable bending properties.

High-Resolution and Large-Sensing-Range Liquid-Level Sensor Based on Optical Frequency Domain Reflectometry and No-Core Fiber.

Liquid-level sensors are required in modern industrial and medical fields. Optical liquid-level sensors can solve the safety problems of traditional electrical sensors, which have attracted extensive attention in both academia and industry. We propose a distributed liquid-level sensor based on optical frequency domain reflectometry and with no-core fiber. The sensing mechanism uses optical frequency domain reflectometry to capture the strong reflection of the evanescent field of the no-core fiber at the liquid–air interface. The experimental results show that the proposed method can achieve a high resolution of 0.1 mm, stability of ±15 μm, a relatively large measurement range of 175 mm, and a high signal-to-noise ratio of 30 dB. The sensing length can be extended to 1.25 m with a weakened signal-to-noise ratio of 10 dB. The proposed method has broad development prospects in the field of intelligent industry and extreme environments.

Ethereum Blockchain Wallets

Abstract: Blockchain technology is an evolving technology which is revolutionizing the IT industry by providing better security and efficiency. This technology can help to solve different kinds of problems in the industrial sphere, such as trust, transparency, security and reliability of data processing. I theory, the use of Blockchain technology shows great and positive results. Ethereum is the most widely used blockchain platform because of its unlimited block size. Many sophisticated problems with smart contracts can be solved with Ethereum and the removal of any third party organization which may interfere in transactions and it is easier to implement compared to other blockchain technologies. In this paper the benefits and drawbacks of wallet based on Ethereum blockchain are analyzed. Many already implemented wallets on Blockchain technology were studied, as well as affected success or problems factors during the implementations. This paper aims to analyze conveniences and difficulties related to the Blockchain integration to a wallet and implementation in the different fields of modern industry

Quantitative sizing of compound location defects based on PECT-EMAT hybrid testing methods

Metal structures are widely used in modern industrial fields, in which various compound location defects are inevitably produced from manufacture to service. Facing such complex conditions, a single electromagnetic nondestructive testing method usually cannot meet all needs. For metal structure with surface crack and bottom thinning defect, a novel PECT-EMAT hybrid testing method is proposed in this study. The relevant simulation and experimental method are developed to realize the compound location defects detection. The proposed method employs only one set of equipment to realize the compound location defects detection, which greatly reduces the complexity of the experimental system and improves the detection efficiency. In addition, the correlation between the extracted signal features and the defect parameters is established through simulation, and further verified by experiment. Furthermore, in order to estimate the damage level of the defects, the inversion calculation procedure of the hybrid NDT method are developed based on genetic algorithm and neural network algorithm, respectively. The reconstruction of the compound location defects using the above two inverse algorithm are carried out using simulation data and experimental data. Finally, the effects of the two inverse methods are compared and the reasons for their advantages and disadvantages are analyzed.

Optimizing mechanical and electrical properties of Cu-coated Cu–Zr–Al bulk metallic glass composites by adjusting glassy powder size

In the rapidly developing modern industrial fields, the materials used are often required to have simultaneously high strength and high electrical conductivity. However, these two properties often conflict and cannot be met at the same time. In the present study, we developed the Cu/Cu–Zr–Al bulk metallic glass (BMG) composites which exhibited simultaneously high strength and high electrical conductivity, as well as large plasticity. The BMG composites were fabricated by using spark plasma sintering crystalline Cu-coated Cu–Zr–Al metallic glass composite powders. The effect of particle size of the Cu–Zr–Al metallic glass powders on mechanical properties and electrical properties of the BMG composites was investigated systematically. Microstructure of contact interfaces between crystalline Cu and Cu–Zr–Al metallic glass was also characterized and discussed. This study is expected to provide an effective technical approach for the development of new high strength and high conductivity materials.

Method to enhance deep learning fault diagnosis by generating adversarial samples

Modern industrial fields utilize complex mechanical equipment and machinery, which are closely linked, and equipment faults are difficult to express. Therefore, fault diagnosis is important to ensure the safety of complex mechanical equipment in modern industries. Deep learning has achieved excellent results with recent fault diagnosis methods. At present, three common deep learning models (MLP, CNN, and RNN models) can achieve diagnosis rates close to 100% with original fault diagnosis data and a signal-to-noise ratio above 10 dB. However, we found that the diagnostic rate of these three models was completely incorrect when an adversarial sample with a signal-to-noise ratio noise greater than 10 dB was added to the original sample. We propose a GAN-based adversarial signal generative adversarial network (AdvSGAN) in this paper. We conduct experiments on the CWRU dataset and conclude that we can easily obtain adversarial noise and generate training samples through AdvSGAN. With the addition of adversarial data training, the diagnostic rate of the model on these adversarial samples increased from less than 5% to 98.69%, 97.38% and 96.94%. Hence, this method increases the reliability of our deep learning model.