Maintenance, Reliability and Condition Monitoring
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Study of a two-stage wood chip hydrolysis process and the kinetics of monosaccharide formation
Full text linkThis article studies the sulfuric acid hydrolysis of paulownia and poplar wood chips using a two-stage approach. The research analyzes the temperature-dependent kinetics of the hydrolysis process, nothing a peak yield of reducing agents at 170 °C. An increased likelihood of reducing agent decomposition is observed as temperature rises, especially at 180 °C. Using liquid chromatography, the amounts of glucose, fructose, and arabinose in the hydrolysates were measured, with the largest glucose yield obtained at 170 °C. In addition, High-performance liquid chromatography (HPLC) and Fourier-transform infrared (FTIR) spectroscopy showed the emergence of new functional groups in the hydrolyzed wood structure. Based on mathematical modeling and experimental validation, the two-stage hydrolysis method is regarded as a good technique. This method helps raise the of hydrolysates, which are employed as raw materials in biopolymer manufacture
Feature extraction of rolling bearing based on adaptive variational multi-harmonic mode extraction
Full text linkVariable multi-harmonic mode extraction (VMHME) not only has the advantages of high computational efficiency and extraction accuracy similar to variational mode extraction (VME), but also could extract the multi-harmonic components of periodic narrowband impulse signals in frequency band as wide as possible, making it very suitable for feature extraction in the event of rolling bearing failure. VMHME needs to accurately estimate the fault characteristic frequency of rolling bearing as its prior parameter, and small errors in estimating the fault characteristic frequency will cause significant deviations in the target extraction components. At present, the theoretical fault characteristic frequency of rolling bearings is commonly used as the estimated fault characteristic frequency. However, due to the installation deformation of rolling bearings and the random sliding between the rolling elements and the raceway during operation, it can cause a deviation between the actual fault characteristic frequency and the theoretical fault characteristic frequency. The most scientific and effective method is to enable VMHME to adaptively obtain the fault characteristic frequency based on the characteristics of the analyzed signal itself. Therefore, this paper introduces the envelope harmonic product spectrum (EHPS) theory into VMHME and proposes an adaptive VMHME (AVMHME) method to effectively extract the multi harmonic components of the periodic narrowband impulse signal when rolling bearings fail. Feasibility of the proposed method is verified through simulation and rolling bearing’ early weak fault experiment, and its superiority is also verified through comparative analysis
Innovative design of a gear belt transmission for technological machines
Full text linkThe article presents the types of belt transmission designs, as well as the advantages of their use in mechanical engineering. Belt drives create loads as a result of excessive vibrations due to a flexible element (belt). A new design of an innovative toothed belt drive is proposed, which contains two paired driving and driven gear pulleys with different diameters and two belts with teeth covering them, while the gear ratios of each pair of gears are equal to each other. The simulation demonstrates a 25-38 % reduction in velocity fluctuation compared to conventional drives, confirming the effectiveness of the proposed design
Determination of optimal drive parameters for high-speed linear systems
Full text linkThe problem of optimizing the drive design parameters for a high-speed linear system is solved based on minimizing the inertial torque. New analytical expressions are obtained for determining the optimal gear ratio of the intermediate transmission, taking into account the moments of inertia of rotating masses, the carriage mass, and the screw pitch. An optimization problem is proposed to determine the number of gear teeth and the screw pitch by minimizing a function that includes the relative error between the actual and calculated gear ratio, as well as the total number of teeth required to ensure the specified travel speed of a carriage. At the next calculation stage, the number of gear teeth is refined based on the nearest standard screw pitch values. The resulting parameters are evaluated using a transient dynamic analysis according to key kinematic and energy characteristics
Dynamic detection and evaluation of wheel flats in heavy-haul railway wheelsets using wayside monitoring systems
Full text linkIn recent years, heavy-haul railways have become a critical direction for freight transport in China, with wheel flats in wheelsets posing significant threats to operational safety and infrastructure integrity. Traditional detection methods (e.g., manual inspection, TPDS) suffer from low efficiency or limited accuracy in characterizing flat features. To address this, this study develops a rigid-flexible coupling dynamic model for C80 wagons with K6 bogies, uniquely integrated with field data from the Truck Operation Detection System (TODS) to bridge simulation and engineering application gaps. Focusing on wheel-rail force responses under wheel flat conditions, we establish a quantitative mapping relationship between flat length, vehicle speed, and impact force through polynomial fitting of simulation data (10-80 km/h for empty/loaded vehicles). To validate feasibility, a 56-channel wayside monitoring system (TODS) is installed on a heavy-haul railway, calibrated via hydraulic loading to ensure measurement accuracy. Field tests (80,541 vehicles monitored) confirm that TODS can infer flat length from detected impact forces, with results consistent with TPDS alarms but offering finer characterization of flat dimensions. This work provides a practical solution for real-time wheel flat detection, enhancing maintenance efficiency and safety in heavy-haul operations
Fault diagnosis method for wind turbine rolling bearings based on adaptive deep learning
Full text linkIn response to the problem of difficulty in extracting fault features of rolling bearings in wind turbine transmission systems under complex working conditions, which limits the accuracy of fault diagnosis. This article proposes an Adaptive Deep Learning based Rolling Bearing Fault Diagnosis Method (ADLM). Introducing dynamic convolution into Convolutional Neural Networks (CNNs) can adaptively capture data features; At the same time, the fishing optimization algorithm (CFOA) was used to optimize the hyperparameters of the bidirectional long short-term memory network (BiLSTM), and the CFOA-BiLSTM network was constructed to fully leverage its advantages in time series analysis. The specific implementation steps are as follows: first, preprocess the collected vibration signals and divide the processed dataset into a training set and a testing set; Then, parallel adaptive convolutional neural networks (ACNN) are used to process the training set and extract spatial domain local features from the vibration signal; Then, the features extracted from the two branches are weighted and fused through a dynamic weight adjustment mechanism, and the fused features are input into the CFOA-BiLSTM network to further capture the time-dependent features of the signal; Finally, the extracted features are input into the classifier to complete model training, and the model performance is evaluated using a test set. Experimental verification shows that on the dataset of Southeast University, the diagnostic accuracy of the ADLM model reached 98.52 %, demonstrating good reliability, robustness, and superiority in the diagnosis of rolling bearing faults
Identification and analysis of pavement structure features based on vibration behavior parameters
Full text linkTo clarify the correlation between the service performance of asphalt pavement structures and their vibration behavior parameters, this study focuses on asphalt pavement structures as the primary research subject. A quarter-vehicle two-degree-of-freedom model of a standard vehicle was selected as the simplified vehicle dynamics model, while a semi-rigid asphalt pavement was adopted as the simplified pavement model. Based on the elastic layered system theory, a three-dimensional finite element model of the asphalt pavement was constructed by using the software of Abaqus. The effects of modulus variations in asphalt pavement structural layers on modal frequencies were analyzed. The impacts of coupled working conditions, such as structural layer cracking positions and interlayer failure, on the modal frequencies of asphalt pavement were investigated. Additionally, the attenuation process of dynamic responses in asphalt pavement structures under transient impact loads was examined. Building on this, the dynamic response behaviors of asphalt pavement structures under working conditions including structural layer cracking and interlayer failure were studied. The results demonstrate that as the vertical depth of the asphalt pavement structure increases, the modulus attenuation of structural layers significantly affects the overall modal frequencies and vibrational effects. When internal cracking and interlayer failure coexist in the asphalt pavement structure, the vibration acceleration characteristics under load align more closely with those of interlayer failure, while the vibration displacement exhibits greater magnitudes
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Full text linkIn recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations
Feature data analysis of dance movements by motion capture
Full text linkMotion capture technology has been applied in more and more fields, but the research in the field of dance is relatively rare. In order to combine motion capture technology with dance research, better understand the characteristics of dance movements, and provide support for their digital analysis, this paper mainly studied the application of a motion capture technology called Kinect in the analysis of dance movement feature data. The skeleton data of different dance movements was first collected based on Kinect v2, and then the collected data was analyzed using a spatio-temporal graph convolutional network (ST-GCN). On the basis of the original ST-GCN, the multi-branch structure was adopted to realize co-occurrence feature learning, and the bone length feature and direction feature were introduced to further enrich the feature data. Experiments were carried out on the NTU RGB+D and dance datasets. It was found that the improved ST-GCN had better performance than other current motion classification approaches on the NTU RGB+D. The top-1 accuracy for cross-subject (CS) and cross-view (CV) was 92.4 % and 96.7 %, respectively, and the average accuracy of different dance movements for the dance dataset was 96.035. The findings confirm the effectiveness of the proposed approach in the analysis of dance movement feature data, and it can be applied in the actual research of dance movements
Analysis of the natural characteristics of fiber-reinforced cantilever beams using 8-node solid elements
Full text linkA combined theoretical and experimental approach is employed to investigate the dynamic characteristics of fiber-reinforced cantilever beams. An 8-node element method establishes the theoretical model of the cantilever beam, allowing for the determination of its dynamic properties. A relevant experimental platform is constructed to test the fiber-reinforced cantilever beams, thereby validating the accuracy of the theoretical model. The results indicate that the theoretical model accurately predicts the dynamic characteristics of fiber-reinforced cantilever beams. Finally, based on the established theoretical model, the effects of cantilever beam length, width, and elastic modulus on the dynamic characteristics of the cantilever beam are discussed