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This paper introduces a novel multi-setup merging method and assesses its performance using simulated response data from a Finite Element (FE) model of a five-storey frame and experimental data from a cantilever beam tested in a laboratory setting.

In the research conducted at the Central Building Research Institute (CBRI) in Roorkee, India, a cantilever beam was examined in a laboratory setting. The study successfully extracted the modal properties of the multi-storey building using the merging technique. Identified frequencies and mode shapes provide valuable insights into the building's dynamic behavior, which is essential for structural analysis and assessment. The sensor layout and data merging approach allowed for the capture of relevant vibration modes despite the limited number of sensors, demonstrating the effectiveness of the methodology.

The results show that reducing the number of sensors can impact the accuracy of the mode shapes. It is recommended to use a minimum of 8 sensor locations (every two floors) for the building under study to obtain reliable benchmark results for further evaluation, periodic monitoring, and damage identification.

The results demonstrate that the developed algorithm can improve the system identification process and streamline data handling. Furthermore, the proposed method is successfully applied to analyze the modal properties of a multi-storey building.

The purpose of this paper is to realize the lightweight of connecting rod and meet the requirements of low energy consumption and vibration. Based on the structural design of the original connecting rod, the finite element analysis was conducted to reduce the weight and increase the natural frequencies, so as to reduce materials consumption and improve the energy efficiency of internal combustion engine.

The finite element analysis, structural optimization design and topology optimization of the connecting rod are applied. Efficient hybrid method is deployed: static and modal analysis; and structure re-design of the connecting rod based on topology optimization.

After the optimization of the connecting rod, the weight is reduced from 1.7907 to 1.4875 kg, with a reduction of 16.93%. The maximum equivalent stress of the optimized connecting rod is 183.97 MPa and that of the original structure is 217.18 MPa, with the reduction of 15.62%. The first, second and third natural frequencies of the optimized connecting rod are increased by 8.89%, 8.85% and 11.09%, respectively. Through the finite element analysis and based on the lightweight, the maximum equivalent stress is reduced and the low-order natural frequency is increased.

This paper presents an optimization method on the connecting rod structure. Based on the statics and modal analysis of the connecting rod and combined with the topology optimization, the size of the connecting rod is improved, and the static and dynamic characteristics of the optimized connecting rod are improved.

This study aims to improve the control accuracy and antidisturbance performance of the manipulator with the flexible link, a combined controller, which combines the novel backstepping sliding mode controller based on the extended state observer (ESO) and super-twisting sliding mode controller.

First, the dynamic of the system is constructed by Lagrange method and assumed mode method, and then the dynamic is decoupled by the singular perturbation theory to obtain the slow-varying subsystem and fast-varying subsystem. For the slow-varying subsystem, the novel backstepping sliding mode controller based on ESO is used to achieve joint tracking. For the fast-varying subsystem, the super-twisting sliding mode controller is used for vibration suppression. At the same time, to suppress chattering, the tanh function is used to replace the sign function in the reaching law.

The simulation results show that the combined control has better trajectory tracking performance, antiinterference performance and vibration suppression performance than traditional sliding mode control (SMC).

A novel backstepping sliding mode controller based on ESO is designed to guarantee the performance of the tracking trajectory. The new controller improves the converge rate. A super-twisting sliding mode controller, which can stabilize the fast-varying subsystem, is used to suppress the vibration of flexible link.

To ensure the stability of the flying wing layout unmanned aerial vehicle (UAV) during flight, this paper uses the radial basis function neural network model to analyse the stability of the aforementioned aircraft.

This paper uses a linear sliding mode control algorithm to analyse the stability of the UAV's attitude in a level flight state. In addition, a wind-resistant control algorithm based on the estimation of wind disturbance with a radial basis function neural network is proposed. Through the modelling of the flying wing layout UAV, the stability characteristics of a sample UAV are analysed based on the simulation data. The stability characteristics of the sample UAV are analysed based on the simulation data.

The simulation results indicate that the UAV with a flying wing layout has a short fuselage, no tail with a horizontal stabilising surface and the aerodynamic focus of the fuselage and the centre of gravity is nearby, which is indicative of longitudinal static instability. In addition, the absence of a drogue tail and the reliance on ailerons and a swept-back angle for stability result in a lack of stability in the transverse direction, whereas the presence of stability in the transverse direction is observed.

The analysis of the stability characteristics of the sample aircraft provides the foundation for the subsequent establishment of the control model for the flying wing layout UAV.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

In the present study, a steel lifting lug is replaced with a composite (carbon fiber-reinforced epoxy [CFRP]) lifting lug made of a carbon/epoxy composite. The purpose of this paper was to obtain a composite lifting lug with a higher level of strength that is capable of carrying loads without failure.

The vibration and static behaviors of steel and composite lifting lugs have been investigated using finite element analysis (FEA), ANSYS software. The main consideration in the design of the composite (CFRP) lifting lug was that the displacement of both steel and composite lugs was the same under the same load. Hence, by using the FEA displacement result of the steel lifting lug, the thickness of the composite lifting lug is determined using FEA.

Compared to the steel lifting lug, the composite (CFRP) lifting lug has much lower stresses and much higher natural frequencies. Static behavior was experienced by the composite lifting lug, showing a reduction in von Mises stress, third principal stress and XZ shear stress, respectively, by 48.4%, 34.6% and 89.8%, respectively, when compared with the steel lifting lug. A higher natural frequency of mode shape swaying in X (258.976√1,000 Hz) was experienced by the composite lifting lug when compared to the steel lifting lug (195.935√1,000 Hz). The safe strength of the design composite lifting lug has been proven by FEA results, which showed that the composite (CFRP) lifting lug has a higher factor of safety in all developed stresses than the steel lifting lug. According to von Mises stress, the factor of safety of the composite lifting lug is increased by 76% when compared to the steel lifting lug. The von Mises stress at the edge of the hole in the composite lifting lug is reduced from 23.763 MPa to 20.775 MPa when compared to the steel lifting lug.

This work presents the designed composite (CFRP) lifting lug, which will be able to carry loads with more safety than a steel one.

Due to the abundant use of granular materials in chemical industries, it is inevitable to store raw materials and products in bulk in silos. For this reason, much research has been carried out in the field of construction, operation and maintenance of silos. One of the important issues that must be investigated in silos is the behavior of their structure when the materials inside them are unloaded. Structural vibrations and the creation of normal noise usually discharge the granular of material from the silo. Both of phenomena are undesirable due to the problems they can cause to the structure and its surroundings. According to the said issues, this paper aims to investigate the vibration problem of the sulfur storage silo of the first refinery during discharge with the help of measuring experimental vibration data and simulating the silo model.

In the experimental investigation, the main cause of the vibration of the 400-ton silo in the refinery is used. The mass asymmetry phenomenon when the silo is filled is also considered. The experimental results are authenticated by software analysis too.

The results showed that the natural frequency of the ninth mode is almost equal to the natural frequency of sulfur discharge from the silos and has the largest shape change in the structure and vibration range. It is also concluded that the larger sulfur silo (400 tons) should be prioritized over the smaller sulfur silo (200 tons) in the emptying program, and the 400 tons silo should never be emptied even through the 200 tons silo is empty.

An attempt is made to investigate the issue of vibration in sulfur storage silos in the first refinery of South Pars in the form of experimental investigation and modal analysis.

The paper aims to analyze the structural integrity of an existing offshore platform located in the Northern Adriatic Sea, followed by the topside decommissioning and the re-utilization of the jacket as a wind turbine support. The structural integrity assessment against the in-place and the long-term actions is accomplished by using a reduced basis finite element method (RB-FEA) software program assessing the capability of the jacket to be used as a support for wind turbines at the end of its life cycle as oil and gas (O&G) platform.

The project starts by modeling the jacket, and subsequently, the structural analyses for the in-place loads in operative and extreme conditions are performed. Then, the fatigue analysis is carried out in order to define the cumulative damage necessary to evaluate the possibility to use the jacket as a wind turbine support.

The results show that the jacket, at the end of the service life as O&G platform, is able to withstand the loads produced by the installation of the wind turbine since the analyses are satisfied even with the conservative approach used which overestimates the thickness loss assuming a linear increasing value during the service life.

Because of the chosen approach, the study presents some limitations, especially concerning the real state of the platform which has been defined considering the thickness loss only. Additionally, a 1D model was used to perform the analyses, and hence, a 3D model could help in evaluating the critical points with higher precision.

The assessment of the structure could be improved by modeling a digital twin of the asset allowing a real-time monitoring which, however, involves a huge amount of data to be processed, so a suitable simulation technology must be used.

The RB-FEA proposed by Akselos is suitable to perform the analyses speeding up the processing of the data even in real time.

The nexus of transport and tourism is critical to the 2021 Glasgow Declaration which sets out the net zero by 2050 goal for global tourism in the context of the Paris Agreement. Numerous small and medium-sized urban destinations (SMUDs) populated under one million are constrained by a limited capacity to manage visitor flows and increasing greenhouse gas (GHG) emissions. This paper aims to develop an analytical approach for urban practitioners, based on a case study in Taiwan, to identify the low-emission pathway and strategies for tourism passenger transport.

A GHG emissions assessment and scenario analysis were enabled by historical activity data from official sources and projected scenario data from the International Energy Agency. The scenarios were established based on the avoid-shift-improve framework for low-carbon transport.

To drive tourism passenger transport to a low-emission pathway compatible with the Paris Agreement goal, three low-carbon transport strategies, i.e. “Avoid,” “Shift” and “Improve,” shall be applied all together, with a focus on “improving” the efficiency of heavy-duty vehicles and rail transport. Meanwhile, alternative tourism and integrated transport policy packages could enhance demand-side management of visitors’ mobility, enabling the “avoid” and “shift” strategies.

Unlike most studies that have focused on large cities or small tourist areas, this paper addressed our knowledge gap regarding the low-emission pathway for tourism transport in numerous SMUDs compatible with a 1.5°C world. The proposed analytical approach can help policymakers assess effective strategies toward the targeted pathway.