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This paper aims to propose an effective dynamic calibration and compensation method to solve the problem that the statically calibrated force sensor would produce large dynamic errors when measuring dynamic signals.

The dynamic characteristics of the force sensor are analyzed by modal analysis and negative step dynamic force calibration test, and the dynamic mathematical model of the force sensor is identified based on a generalized least squares method with a special whitening filter. Then, a compensation unit is constructed to compensate the dynamic characteristics of the force measurement system, and the compensation effect is verified based on the step and knock excitation signals.

The dynamic characteristics of the force sensor obtained by modal analysis and dynamic calibration test are consistent, and the time and frequency domain characteristics of the identified dynamic mathematical model agree well with the actual measurement results. After dynamic compensation, the dynamic characteristics of the force sensor in the frequency domain are obviously improved, and the effective operating frequency band is widened from 500 Hz to 1,560 Hz. In addition, in the time domain, the rise time of the step response signal is reduced from 0.29 ms to 0.17 ms, and the overshoot decreases from 26.6% to 9.8%.

An effective dynamic calibration and compensation method is proposed in this paper, which can be used to improve the dynamic performance of the strain-gauge-type force sensor and reduce the dynamic measurement error of the force measurement system.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

According to relevant research, non-uniform speed has a significant impact on the vehicle-track systems. Up to now, research work on it is still very limited. In this paper, the PEM is adopted to further transform it into a deterministic process to solve the vehicle’s problem of running at a non-uniform speed.

The multi-body vehicle model has 10 degrees of freedom and the track is regarded as a finite long beam supported by lumped sleepers and ballast blocks. They are connected via linear Hertz springs. The vertical track irregularity is a Gaussian stationary process in the space domain. It is transformed into a uniformly modulated nonstationary random process in the time domain with respect to the non-uniform vehicle speed. By solving the equation of motion of the coupled vehicle-track system with the pseudo-excitation method, the pseudo-response and consequently the power spectral density and the standard deviation of the structural response can be obtained.

Two kinds of vehicle braking programs are taken in the numerical example and some beneficial conclusions are drawn.

The pseudo-excitation method (PEM) was used to perform the random vibration analysis of a coupled non-uniform speed vehicle-track system. Transforming the track irregularity into a uniformly modulated nonstationary random process in time domain with respect to the non-uniform vehicle speed was undertaken. The pseudo-response of the coupled system is solved by applying the Newmark algorithm with constant space integral steps. The random vibration transfer mechanism of the coupled system is fully discussed.

This article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.

Firstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.

By comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.

This article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this study is to investigate the effects of nanoadditive lubricants on the vibration and noise characteristics of helical gears compared with conventional lubricants. The experiment aims to analyze whether nanoadditive lubricants can effectively reduce gear vibration and noise under different speeds and loads. It also analyzes the sensitivity of the vibration reduction to load and speed changes. In addition, it compares the axial and radial vibration reduction effects. The goal is to explore the application of nanolubricants for vibration damping and noise reduction in gear transmissions. The results provide a basis for further research on nanolubricant effects under high-speed conditions.

Helical gears of 20CrMnTi were lubricated with conventional oil and nanoadditive oils. An open helical gearbox with spray lubrication was tested under different speeds (200–500 rpm) and loads (20–100 N·m). Gear noise was measured by a sound level meter. Axial and radial vibrations were detected using an M+P VibRunner system and fast Fourier transform analysis. Vibration spectrums under conventional and nanolubrication were compared. Gear tooth surfaces were observed after testing. The experiment aimed to analyze the noise and vibration reduction effects of nanoadditive lubricants on helical gears and the sensitivity to load and speed.

The key findings are that nanoadditive lubricants significantly reduce the axial and radial vibrations of helical gears under low-speed conditions compared with conventional lubricants, with a more pronounced effect on axial vibrations. The vibration reduction is more sensitive to rotational speed than load. At the same load and speed, nanolubrication reduces noise by 2%–5% versus conventional lubrication. Nanoparticles change the friction from sliding to rolling and compensate for meshing errors, leading to smoother vibrations. The nanolubricants alter the gear tooth surfaces and optimize the microtopography. The results provide a basis for exploring nanolubricant effects under high speeds.

The originality and value of this work is the experimental analysis of the effects of nanoadditive lubricants on the vibration and noise characteristics of hard tooth surface helical gears, which has rarely been studied before. The comparative results under different speeds and loads provide new insights into the vibration damping capabilities of nanolubricants in gear transmissions. The findings reveal the higher sensitivity to rotational speed versus load and the differences in axial and radial vibration reduction. The exploration of nanolubricant effects on gear tribological performance and surface interactions provides a valuable reference for further research, especially under higher speed conditions closer to real applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0220/

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.