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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/

Five-level rectifiers have received widespread attention because of their excellent performance in high-voltage and high-power applications. Taking a five-level rectifier with only four-IGBT for this study, a sliding mode predictive control (SMPC) algorithm is proposed to solve the problem of poor dynamic performance and poor anti-disturbance ability under the traditional model predictive control with the PI outer loop.

First, mathematical models under the two-phase stationary coordinate system and two-phase synchronous rotating coordinate system are established. Then, the design of the outer-loop sliding mode controller is completed by establishing the sliding mode surface and design approach rate. The design of the inner-loop model predictive controller was completed by discretizing the mathematical model equations. The modulation part uses a space vector modulation technique to generate the PWM wave.

The sliding mode predictive control strategy is compared with the control strategy with a PI outer loop and a model predictive inner loop. The proposed control strategy has a faster dynamic response and stronger anti-interference ability.

For the five-level rectifier, the advantages of fast dynamic influence and parameter insensitivity of sliding mode control are used in the voltage outer loop to replace the traditional PI control, and which is integrated with the model predictive control used in the current inner loop to form a novel control strategy with a faster dynamic response and stronger immunity to disturbances. This novel strategy is called sliding mode predictive control (SMC).

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The aim of this study was to investigate the activity of 4-((4-((2-hydroxyethyl)(methyl)amino)benzylidene) amino)-1,5-dimethyl-2-phenyl-1,2-dihydro-3H-pyrazol-3-one (HEMAP), a Schiff base synthesized and characterized for the first time, to the authors’ knowledge, as a novel inhibitor against corrosion of mild steel (MS) in hydrochloric acid solution.

HEMAP was characterized by some spectroscopic methods including High-Resolution Mass Spectrometry (HRMS), Proton Nuclear Magnetic Resonance (1H NMR), Carbon-13 (C13) nuclear magnetic resonance (13C NMR) and Fourier Transform Infrared Spectroscopy (FT-IR). Then, the inhibition efficiency of HEMAP on MS in a hydrochloric acid solution was investigated by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). To explain the inhibition mechanism, the surface charge, adsorption isotherms and thermodynamic parameters of MS in the inhibitor solution were studied.

EIS tests displayed that the highest inhibition efficiency was calculated approximately as 99.5% for 5 × 10⁻² M HEMAP in 1 M HCl solution. The adsorption of HEMAP on the MS surface was found to be compatible with the Langmuir model isotherm. The thermodynamic parameter results showed that the standard free energy of adsorption of HEMAP on the MS surface was found to be more chemical than physical.

This study is important in terms of demonstrating the performance of the first synthesized HEMAP molecule as an inhibitor against the corrosion of MS in acidic media. EIS tests displayed that the highest inhibition efficiency was calculated approximately as 99.5% for 5 × 10⁻² M HEMAP in 1 M HCl solution.

The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.

The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.

A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.

The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.

To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.