Large Machinery Research Articles

Research on visible light communication channel model in underground mines

Abstract In order to effectively model the Visible Light Communication (VLC) channel in underground mines, this paper delves into the challenges of the underground environment and develops a mathematical model by analyzing the propagation characteristics of visible light. The objective is to deepen our understanding of light signal behavior in this unique setting. Most existing research focuses on visible light channel modeling in indoor environments, with limited studies on underground mines. Our model takes into account the irregular surfaces of mine walls, as well as the effects of factors such as high dust concentration, extreme temperatures and pressures, light absorption by mine walls, and obstruction caused by large machinery on the propagation of visible light signals. This paper introduces refined optical propagation models that incorporate both scattering and light absorption considerations, resulting in a maximum received power of -46.12 dBm, which is significantly lower than that in indoor environments. These models enhance our ability to analyze and improve channel performance, thereby optimizing communication efficiency.

Synchronization phenomenon in a vibrating system driven by four eccentric rotors mounted in the orthogonal plane

The engineering application of vibration synchronization of multiple eccentric rotors (ERs) with the same rotational plane in far resonance is limited due to the constraints of motion characteristics and resultant force cancellation. Therefore, a dynamic model of four ERs distributed in two orthogonal planes is proposed in this paper, which is designed to increase the excitation force and drive the vibrating body to achieve the motion in a straight line. Based on the governing equation corresponding to the dynamic model firstly, the synchronous condition of four ERs and its stability condition are deduced. Then, the phase relationship of four ERs is obtained by numerical analysis, and the resultant force of four ERs in each phase difference is further studied to determine the motion trajectory of the vibrating body. Lastly, theoretical results are verified by four sets of experiments, which show that there are two stable motion states of the system. In each motion state, the resultant force of four ERs is increased compared to two ERs, and the system also moves in a straight line. Therefore, the model presented in this paper can provide a theoretical basis for designing large vibration machinery.

Optimization analysis of vibration reduction for aeroengine multistage blade-disk system

In order to reduce the vibration localization of multistage blade-disk system of aeroengine compressor, the integral model and substructure model of multistage blade-disk system are established by using substructure modal synthesis method, and the dynamic characteristics of two models are analyzed and the accuracy of the substructure model is verified by comparison. Based on the substructure model, two optimization algorithms of the random wear mass and the snake optimization are proposed. The results show that the vibration amplitudes of the two models are in good agreement in the first 200 modes, and the substructure model meets the requirement of analysis accuracy. Both optimization algorithms can effectively reduce the degree of the vibration localization of the mistuned blade-disk system. The random wear mass algorithm can quickly reduce the amplitude of the blade. The snake optimization algorithm can find the global optimal solution with enough iterations. The results show that the maximum vibration amplitude and the vibration localization factor after optimization are reduced by 5.72 % and 12.0 % respectively by using the random wear mass algorithm, and that of 9.13 % and 16.7 % respectively by using the snake optimization algorithm. The analysis method presented in this paper takes into account the analysis efficiency and analysis precision, it can be used for dynamic analysis and vibration reduction optimization of large power machinery such as aeroengine and gas turbine, and provides a basis for further improving the reliability and service life of aircraft.

Ultrasonic TFM imaging inspection of large complex rings using defect quantitative evaluation mappings

ABSTRACT Large complex rings are widely used in wind power energy, aerospace, large construction machinery. There are different scales of defects in the manufacturing process for large complex rings. Because of the complex cross-section and large thickness, it is difficult to quantitative evaluation of defects by conventional ultrasonic technology. Therefore, the ultrasonic total focusing method (TFM) defect quantitative inspection for large complex rings is proposed in this paper. First, the TFM inspection technology of large complex rings for simultaneous scanning of the end-face and side-face is designed. Then, the maximum amplitudes of defects are collected at each grid point of the imaging area by the TFM inspection of calibration specimens. The Φ1.5mm defect quantitative evaluation mappings are obtained by the interpolation fitting algorithm. Finally, the ultrasonic TFM and B-scan inspection experiments of the Φ3440mm large ring are carried out. The results show that the TFM quantitative evaluation mapping has higher accuracy compared to the conventional B-scan method. Especially for the Φ1.5mm defects, the average error of TFM quantitative evaluation is 2.92%. The proposed method can effectively identify defects of other sizes that exceed the calibration mapping. This work has industrial application value for developing ultrasonic TFM inspection of large complex components.

Analytical modeling of cylindrical eddy current brakes and multi-objective optimization based on game theory

With the research and development of eddy current brake (ECB), today ECBs have been applied in a variety of fields including vibration control and braking of strong impact loads.The application of a new cylindrical structure eddy current brake (ECB) for strong impact braking of large machinery has been discussed recently. Its high-speed and high-kinetic-energy braking conditions require different analytical and optimization design models and methods from what has been addressed in previous studies. For subsequent more engineering-oriented research and optimization, modeling methods are needed, as well as analysis and optimization studies for braking forces and critical speeds of interest. In this work, a magnetic equivalent circuit model is established, and the influence of eddy current induced during application is taken into account by considering it as a magnetomotive force in the model. The braking force is calculated with the MEC model and an approximate electric field cross-section method. A small prototype experiment is carried out and proves the correctness of the proposed model. Using the proposed and FEM model, parameters of the ECB are analyzed. Then, aiming at design objectives of the ECB that are somewhat competitive under this special braking condition, a multi-objective optimization model is established using the Stackelberg game strategy. An FEM model is built and assessed based on optimization results. The results indicate that the multi-objective optimization model based on the Stackelberg game is effective for the design of this ECB structure.

Structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraulic motor

Cam-lobe radial-piston hydraulic motors are widely used in large heavy-duty machinery as direct-drive components of hydraulic systems. Due to their extremely high output torque and power density, the wet brake equipped with the hydraulic motors are necessary to achieve ultra-high braking torque in a compact space. However, due to a large number of internal friction pairs and small fit clearance between multiple friction pairs in wet brake, the rotation of the friction pairs in non-braking state will cause large friction torque loss and obvious temperature rise, thereby leading to the thermal failure of wet brake. How to optimize the structure of friction pairs to reduce the temperature rise of wet brake in non-braking state is a persistent challenge. Existing structure optimization design approaches of friction pairs in brakes mainly focus on the structure of each friction pair, which ignore the effect of fit clearance between the friction pairs on the temperature rise of brakes. Nevertheless, the fit clearance between friction pairs is an important factor that affect the temperature rise. For that, this study proposes a two-step structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraculic motor, which simultaneously considers the fit clearance between the friction pairs and the structure of each friction pair. In this approach, the design process could be divided into two steps: firstly, the influence of fit clearance between multiple friction pairs on the temperature rise of wet brake in non-braking state is analyzed and optimized by the temperature rise theoretical model; secondly, in order to further reduce the temperature rise of wet brake, the oil groove structure of each friction plate are introduced and optimized by fluid-solid-thermal coupling simulation method. By this approach, the low-temperature rise structure of friction pairs with the optimized fit clearance between multiple friction pairs and oil grooves of each friction plate. Finally, a series of experiments are conducted to verify the effectiveness of the design approach. The result show that the temperature rise of wet brake could be effectively reduced 12 % by optimizing the structure of wet brake, demonstrating that the structure optimization design approach could provide a theoretical guidance to design low-temperature rise wet brake for large torque hydraulic motors.

Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring

Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and high-dimensional, especially for large structure and machinery. In condition-based monitoring (CBM) of rotating machines, e.g., gas turbine, rotor fault diagnosis serves a significant role in the system reliability, safety, and efficiency, which helps reduce potential damage to the on-rotor structures and avoid catastrophic failures. For multi-channel vibration measurement, classical rotor diagnosis approaches typically involve data fusion techniques based on cross spectral analysis for identifying spectral correlation, e.g., cross power spectral density, and/or matrix analysis tool for dimensionality reduction, e.g., principal component analysis. The operation on vectors or matrices may limit their effectiveness for higher-order array or tensor data. To circumvent this limitation, the third order vibrational spectral tensor is generated by representing the multi-channel acceleration spectra as the three-way array. Through nonnegative Tucker decomposition (NTD), the spectral tensor is decomposed into multiple principal factors: the spectral factor, segmental factor, channel-wise factor, and the dense tensor core. The correlations across the characteristic spectral contents and the sensor channels are revealed by the factorization, which enables the diagnosis of rotor fault and facilitates fault localization by identifying the dominant channels of the characteristic spectral factor. The method is validated on an experimental rotor testbed where the faulty channel with crack, rub, or misalignment fault, is effectively localized via 4-channel vibration measurements, which presents a promising approach for multi-sensor fusion and fault diagnosis in rotating machinery health monitoring.

An exploration into the sleep of workers on block-calving, pasture-based dairy farms

The benefits of sufficient and high-quality sleep for people are well documented. Insufficient sleep increases the risk of accidents, injuries, and negative health implications for people. This is especially relevant for farmers, as they work with large animals and machinery. Dairy farming often requires early start times and long days, particularly over the high workload calving period in block calving, pasture-based systems. However, there is little published data quantifying the sleep quantity and quality of farmers over this period. In this study, the sleep patterns of workers (n = 33) on 10 New Zealand dairy farms was measured for 90 d over the spring calving period using a sleep measuring device (OuraTM ring, Oura Health Ltd., Oulu, Finland). Total sleep time (TST) averaged 6 h 15 min, lower than the required 7 to 9 h for optimal wellbeing and cognitive functioning. TST decreased over the calving period and was significantly correlated with both sleep start and wake times. Factors such as work start time, farm location, and role on farm influenced sleep quantity and quality; indicating adjusting these on-farm factors could positively impact TST. Further research is required to better understand sleep and its effect on dairy farmers, over both the calving period and the remaining months of the year.

Cross-domain bearing fault diagnosis using dual-path convolutional neural networks and multi-parallel graph convolutional networks

Bearing fault diagnosis is significant in ensuring large machinery and equipment's safe and stable operation. However, inconsistent operating environments can lead to data distribution differences between source and target domains. As a result, models trained solely on source-domain data may not perform well when applied to the target domain, especially when the target-domain data is unlabeled. Existing approaches focus on improving domain adaptive methods for effective transfer learning but neglect the importance of extracting comprehensive feature information. To tackle this challenge, we present a bearing fault diagnosis approach using dual-path convolutional neural networks (CNNs) and multi-parallel graph convolutional networks (GCNs), called DPC-MGCN, which can be applied to variable working conditions. To obtain complete feature information, DPC-MGCN leverages dual-path CNNs to extract local and global features from vibration signals in both the source and target domains. The attention mechanism is subsequently applied to identify crucial features, which are converted into adjacency matrices. Multi-parallel GCNs are then employed to further explore the structural information among these features. To minimize the distribution differences between the two domains, we incorporate the multi-kernel maximum mean discrepancy (MK-MMD) domain adaptation method. By applying the DPC-MGCN approach for diagnosing bearing faults under diverse working conditions and comparing it with other methods, we demonstrate its superior performance on various datasets.

Turbine bearing fault diagnosis based on embedded system

Abstract Failure of the main components of the transmission mechanism of large machinery and equipment usually uses sensors to collect real-time vibration or audio information, followed by data processing on the computer to analyze whether the equipment is operating normally. In this paper, a multi-core embedded fault diagnosis platform based on ARM and DSP is designed. It is small in size. It can independently set the signal types, ranges, and filter parameters of multiple acquisition channels, and synchronously set high-precision acquisition and storage of voltage signals output from various sensors. The DSP adopts the fast Fourier algorithm to analyze the multi-channel sampling data in real time and extracts the fault characteristics through the analysis of envelope demodulation. The operation results show that the system can meet the routine online fault diagnosis of mechanical equipment transmission mechanism, and the fault analysis and diagnosis can reach 95% accuracy within one minute, with high real-time accuracy.

Study on the Two-Step Construction Method of Super Large Cross-Section Tunnels Crossing Karst Cave Areas

To explore the solution of the two-step method applied in the rapid construction of super large cross-section tunnels passing through IV-and V-grade surrounding rock sections in karst cave areas, based on an engineering example of the Lianhuashan Tunnel, we use the numerical calculation method to analyze the stability of surrounding rock and the design parameters of the control measures for super large cross-section tunnels during the construction of the step method. The calculated results show that the working face of IV-grade surrounding rock can be stabilized by an advanced small pipe, and the stability of the supporting structure should be controlled mainly by IV-grade surrounding rock. In order to control the stability of the tunnel face, it is necessary to use an advanced large pipe shed in the surrounding V-grade rock. The reinforcement range of the advanced large pipe shed is 120° and the length is 20 m. This is the most economical design parameter of the advanced large pipe shed, ensuring the deformation control effect. For control of the stability of the supporting structure, under the condition that the working space is suitable for large machinery, the settlement of the arch of the supporting structure can be obviously reduced by shortening the step cycle footage and reducing the step length, and the peripheral convergence of the supporting structure can be obviously reduced by reducing the step height. After comprehensive analysis and considering the development of karst caves, the advanced support measures, design parameters, bench excavation design parameters, initial support measures, karst cave treatment measures, and bench construction process of IV- and V-grade surrounding rock is determined. The application verification shows that the research results have a good control effect on the stability of the surrounding rock and cave and are suitable for large-scale mechanical operations, which can significantly improve the excavation speed of the super large cross-section tunnel passing through the IV- and V-grade surrounding rock sections in the karst cave area.

Effects of the cross angle on output characteristics and tilting behaviour for 750 mL/r axial piston pumps

Large displacement axial piston pumps (>500 mL/r) are the core power components of large construction machineries' hydraulic system. The demand for higher power in large construction machinery is increasing. High pressure and large displacement are the primary areas of focus for enhancing the power of piston pumps. However, this may exacerbate issues with the pumps' output characteristics, such as flow or pressure pulsation and leakage. This paper uses a cross angle of the swash plate to optimize the pulsation and leakage for the 750 mL/r axial piston pumps. Piston and cylinder dynamics affect flow and pressure pulsations, and leakage is related to cylinder tilting behaviour. The pumps’ flow distribution, kinematics and dynamics of the piston and cylinder, output flow and pressure are analysed. A virtual prototype is built based on the rigid-flexible-hydraulic coupling model. The oil film flexible body of the valve plate pair is added. The maximum principal stress and strain of the oil film flexible body are used as indicators of tilting behaviour. Effects of the cross angle on output characteristics and tilting behaviour are investigated through the numerical solution. Results show that the cross angle of −0.5° can effectively reduce the pressure pulsation rate by 0.89% and the flow pulsation rate by 3.77% while optimizing the tilting behaviour of the cylinder. In addition, the cylinder’s tilting behaviour is verified by the wear detection on the valve plate surface.

A study of multi-acoustic black holes considering suitability of attachment positions for suppressing low-frequency vibrations on large motor

Vibrations generated from machinery in various industries, such as the automotive, aerospace, and shipbuilding industries, must be suppressed for developing low-vibration and low-noise systems. High-frequency vibrations can be easily controlled via damping treatments; however, controlling vibrations in the low-frequency range generated from large machinery is a major challenge. In general, a substantial amount of damping material is required for suppressing low-frequency vibrations. This is unsuitable when developing low-cost, lightweight, and eco-friendly systems. To overcome the limitations, effective methods are required for suppressing these low-frequency vibrations. In this study, an effective method was proposed for suppressing vibrations in the low-frequency range using multiple acoustic black holes (multi-ABHs) considering the suitability values of the attachment positions, which were determined by flexural wave propagation and interactions between the multi-ABHs and structures. The suitability values of the attachment positions are proposed as non-dimensional parameters that are calculated using the structural intensity and mode superposition methods. For validating the proposed method, the vibration suppression characteristics were analyzed using a steel plate. The analyzed cases were categorized into six groups according to the suitability values of the attachment positions of the multi-ABHs. The largest reduction in vibrations was observed when the multi-ABHs were attached to the location with the highest suitability in the six cases. Finally, the multi-ABHs were applied to a large 2-pole motor considering the suitability values of the attachment positions, and the dominant vibrations at 60, 120, and 180 Hz frequencies were suppressed.

Research on Path Tracking of Unmanned Spray Based on Dual Control Strategy

The high clearance spray is a type of large and efficient agricultural machinery used for plant protection, and path tracking control is the key to ensure the efficient and safe operation of spray. Sliding mode control and other methods are commonly used abroad to track vehicles, while fuzzy control, neural networks and other methods are commonly used at home. However, domestic and foreign research on autonomous agricultural machinery is mainly focused on tractors and other machinery, while research on self-propelled spray in high clearance is less abundant. This paper takes the path tracking algorithm in the integrated navigation system of spray as the main research goal, studies the path tracking control algorithm for straight lines and turning curves that can realize the automatic driving of spray by establishing the path tracking algorithm for unmanned spray based on dual control strategies, designs the path tracking controller, including the preview model theoretical path tracking controller and variable domain fuzzy controller, and determines the preview model through the design of the preview model theoretical path tracking controller. The lateral and longitudinal errors of the model algorithm are analyzed, and the driving characteristics under the complex spray road surface are analyzed. The design of the variable domain fuzzy predictor theory path tracking controller is proposed, and the design of the road model selection controller is calculated and analyzed in detail, including the determination of the road roughness coefficient and the selection of the range of the difference between the average value of the excitation before and after sampling, which improves the performance of the spray path tracking algorithm. The experiment shows that the proposed path tracking control algorithm can meet the path tracking requirements of unmanned spray in the current road environment, and provide a reliable solution for the automatic control of high clearance spray.

Monovalent metal ion binding promotes the first transesterification reaction in the spliceosome

Cleavage and formation of phosphodiester bonds in nucleic acids is accomplished by large cellular machineries composed of both protein and RNA. Long thought to rely on a two-metal-ion mechanism for catalysis, structure comparisons revealed many contain highly spatially conserved second-shell monovalent cations, whose precise function remains elusive. A recent high-resolution structure of the spliceosome, essential for pre-mRNA splicing in eukaryotes, revealed a potassium ion in the active site. Here, we employ biased quantum mechanics/ molecular mechanics molecular dynamics to elucidate the function of this monovalent ion in splicing. We discover that the K+ ion regulates the kinetics and thermodynamics of the first splicing step by rigidifying the active site and stabilizing the substrate in the pre- and post-catalytic state via formation of key hydrogen bonds. Our work supports a direct role for the K+ ion during catalysis and provides a mechanistic hypothesis likely shared by other nucleic acid processing enzymes.

Analysis and design research of digital electronic clocks

A digital electronic clock stands as a pinnacle of modern timing technology, built on digital circuits that offer precise hour, minute, and second tracking and display capabilities. The evolution of integrated circuits, combined with the ubiquitous application of quartz crystal oscillators, has propelled the digital electronic clock into myriad sectors including science, transportation, and finance. These clocks, appreciated for their precision, clarity, stability, and other attributes, vastly differ from their mechanical predecessors. Absent of mechanical transmission devices, they promise longevity and exact timekeeping. Such digitization serves as the foundational technological support for crafting large machinery and precision tools. Delving into the intricacies of digital electronic clocks and broadening their utilization holds substantial real-world relevance. This discourse employs a top-down hierarchical circuit design approach, segmenting the comprehensive circuit into distinct modules. This method not only streamlines its configuration, rendering it user-friendly and legible, but also ensures that each segmented circuit operates as an autonomous entity. This modularity facilitates smoother simulations and circuit modifications, enhancing adaptability and efficiency.

Research on the Shock Environment Characteristics of a Marine Diesel Engine Based on a Large Floating Shock Platform

To conduct a precise shock assessment of marine diesel engines, a 200 t floating shock platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the shock response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical shock environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that shock waves were identified as the primary external force, and it was found that the influence of bubble pulsation can be disregarded when assessing a floating shock platform. This paper proposes the use of normal-distribution-based shock factor confidence intervals, which can accurately predict multidirectional shock factors and offer improved shock safety compared to the traditional method of unidirectional shock factor interpolation. The results and methods obtained in this study can provide valuable guidance and assistance for predicting the shock environment of large shipboard machinery on significant floating shock platforms.

Nuclear envelope budding: Getting large macromolecular complexes out of the nucleus.

Transport of macromolecules from the nucleus to the cytoplasm is essential for nearly all cellular and developmental events, and when mis-regulated, is associated with diseases, tumor formation/growth, and cancer progression. Nuclear Envelope (NE)-budding is a newly appreciated nuclear export pathway for large macromolecular machineries, including those assembled to allow co-regulation of functionally related components, that bypasses canonical nuclear export through nuclear pores. In this pathway, large macromolecular complexes are enveloped by the inner nuclear membrane, transverse the perinuclear space, and then exit through the outer nuclear membrane to release its contents into the cytoplasm. NE-budding is a conserved process and shares many features with nuclear egress mechanisms used by herpesviruses. Despite its biological importance and clinical relevance, little is yet known about the regulatory and structural machineries that allow NE-budding to occur in any system. Here we summarize what is currently known or proposed for this intriguing nuclear export process.

Inflammasome pathway in kidney transplantation.

Kidney transplantation is the best available renal replacement therapy for patients with end-stage kidney disease and is associated with better quality of life and patient survival compared with dialysis. However, despite the significant technical and pharmaceutical advances in this field, kidney transplant recipients are still characterized by reduced long-term graft survival. In fact, almost half of the patients lose their allograft after 15-20 years. Most of the conditions leading to graft loss are triggered by the activation of a large immune-inflammatory machinery. In this context, several inflammatory markers have been identified, and the deregulation of the inflammasome (NLRP3, NLRP1, NLRC4, AIM2), a multiprotein complex activated by either whole pathogens (including fungi, bacteria, and viruses) or host-derived molecules, seems to play a pivotal pathogenetic role. However, the biological mechanisms leading to inflammasome activation in patients developing post-transplant complications (including, ischemia-reperfusion injury, rejections, infections) are still largely unrecognized, and only a few research reports, reviewed in this manuscript, have addressed the association between abnormal activation of this pathway and the onset/development of major clinical effects. Finally, the regulation of the inflammasome machinery could represent in future a valuable therapeutic target in kidney transplantation.

Metaheuristic optimisation of Gaussian process regression model hyperparameters: Insights from FEREBUS

FEREBUS is a Gaussian process regression (GPR) engine embedded in the large machinery of FFLUX, a novel machine learnt force field developed from scratch through several well-documented proof-of-concept studies. This package relies on the exploration and exploitation capabilities of metaheuristic algorithms (MAs) to carry out the global optimisation of GPR model hyperparameters (θ). However, because MAs employ different search mechanisms to scrutinise the hyperparameter space, their performance on a specific optimisation task can vary a lot from one technique to another. Herein, we report a series of carefully designed experiments aimed at evaluating the ability of ten metaheuristic algorithms to locate the optimal set of θ values. Selected optimisation techniques belong to four popular families of MAs, namely particle swarm optimisation (4), grey wolf optimisation (2), bat (2) and firefly (2) algorithms. Our calculations suggest that grey wolf optimisers (GWOs) achieve the best results on average. Furthermore, the RMSE(θ) cost function is confirmed to be an excellent guide for the selection of atomic GPR models. This work also briefly introduces an enhanced grey wolf optimiser called GWO-RUHL (Random Update of the Hierarchy Ladder), which accounts for the (so far omitted) natural desire of non-leader wolves to occupy high-ranked leadership positions in the pack. We demonstrate that GWO-RUHL achieves better results than the standard GWO in terms of both convergence speed and quality of solutions.