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The energy dissipation in the lifecycle of gearboxes is often dominated by the use phase. Energy efficiency targets can accelerate technological development. For tribological contacts, the definition of superlubricity with coefficients of friction of less than 0.01 has stimulated research to reduce friction. Similarly, this potential study on the energy efficiency of gearboxes aims to push research to reduce their energy dissipation during the use phase. Different cylindrical gear geometries, gear oils, and lubrication methods are evaluated using an energy efficiency index and mean energy efficiency. Superefficiency is allocated to the technological setup of a water-containing polyglycol, an extreme low-loss gear, and minimum quantity lubrication. Its corresponding values for the energy efficiency index and mean energy efficiency depend on the particular gearbox and the underlying operating cycle. In the future, the method used in this study can be used to introduce energy classes for gearboxes and to assign energy labels. [ABSTRACT FROM AUTHOR]

ABSTRACT Gearboxes are a crucial component of the transmission systems in many devices. Due to prolonged operation and high loads, it is inevitable that their condition will degrade over time. Therefore, intelligently dividing health state stages and conducting timely and effective health state assessments are essential for ensuring the safe and reliable operation of gearboxes. In response to the acoustic signals generated during the operation of gearboxes, a health state division and assessment method based on acoustic signals is proposed. Initially, fast Fourier transform (FFT) is utilized to convert the measured sound signal into a spectral signal that is relatively less disturbed by noise. Subsequently, the temporal convolutional autoencoder (TCAE) is proposed and constructed to encode and decode the spectrum signals at different moments, so that the trained encoder can be used to extract the deep features of the signals adaptively. After that, K‐Means clustering method was used to automatically divide the health state of the gearbox combined with the extracted deep features. Finally, the one‐dimensional convolutional neural networks (1DCNN) model is constructed and trained, and the deep features extracted by TCAE are input to identify the health state stage of the test sample, so as to realize the health state assessment of the gearbox. The experimental results show that, in the gearbox data set of three working conditions, the proposed method is closer to the health stage of manual calibration, which proves the rationality of the proposed intelligent method. The accuracy of the proposed health assessment method can reach 95%, 90%, and 90%, respectively, and the effect is obviously better than that of the more commonly used models at present, achieving effective health state assessment of the gearbox under non‐destructive testing conditions. [ABSTRACT FROM AUTHOR]

For the description of power transmission through drive links, a system of linear first-order differential equations has been applied taking into account the accelerations and rates of changes in stresses under torsional and transverse oscillations. An eighth order vector equation has been derived and solved, including algebraic elements, making it possible to calculate the fluctuations of stresses and motion velocities in any link of the system to a first approximation with the use of an engineering method. For a particular drive, the effect of the inertial properties of the load and the features of resistance torque formation exerted on the dynamic characteristics of the drive has been analyzed. [ABSTRACT FROM AUTHOR]

Due to constant loads, gear wear, and harsh working conditions, gearboxes are subject to fault occurrences. Faults in the gearbox can cause damage to the engine components, create unnecessary noise, degrade efficiency, and impact power transfer. Hence, the detection of faults at an early stage is highly necessary. In this work, an effort was made to use transfer learning to identify gear failures under five gear conditions—healthy condition, 25% defect, 50% defect, 75% defect, and 100% defect—and three load conditions—no load, T1 = 9.6, and T2 = 13.3 Nm. Vibration signals were collected for various gear and load conditions using an accelerometer mounted on the casing of the gearbox. The load was applied using an eddy current dynamometer on the output shaft of the engine. The obtained vibration signals were processed and stored as vibration radar plots. Residual network (ResNet)‐50, GoogLenet, Visual Geometry Group 16 (VGG‐16), and AlexNet were the network models used for transfer learning in this study. Hyperparameters, including learning rate, optimizer, train‐test split ratio, batch size, and epochs, were varied in order to achieve the highest classification accuracy for each pretrained network. From the results obtained, VGG‐16 pretrained network outperformed all other networks with a classification accuracy of 100%. [ABSTRACT FROM AUTHOR]

The gearbox of an electric vehicle operates under the high load torque and axial load of electric vehicles. In particular, the bearings that support the shaft of the gearbox are subjected to several tons of axial load, and as the mileage increases, fault occurs on bearing rolling elements frequently. Such bearing fault has a serious impact on driving comfort and vehicle safety, however, bearing faults are diagnosed by human experts nowadays, and algorithm-based electric vehicle bearing fault diagnosis has not been implemented. Therefore, in this paper, a deep learning-based bearing vibration signal processing method to diagnose bearing fault in electric vehicle gearboxes is proposed. The proposed method consists of a deep neural network learning stage and an application stage of the pre-trained neural network. In the deep neural network learning stage, supervised learning is carried out based on two acceleration sensors. In the neural network application stage, signal processing of a single accelerometer signal is performed through a pre-trained neural network. In conclusion, the pre-trained neural network makes bearing fault signals stand out and can utilize these signals to extract frequency characteristics of bearing fault. [ABSTRACT FROM AUTHOR]

Gearboxes, as critical components, often operate in demanding conditions, enduring constant exposure to variable loads and speeds. In the realm of condition monitoring, the dataset primarily comprises data from normal operating conditions, with significantly fewer instances of faulty conditions, resulting in imbalanced datasets. To address the challenges posed by this data disparity, researchers have proposed various solutions aimed at enhancing the performance of classification models. One such solution involves balancing the dataset before the training phase through oversampling techniques. In this study, we utilized the Sparse Autoencoder technique for data augmentation and employed Support Vector Machine (SVM) and Random Forest (RF) for classification. We conducted four experiments to evaluate the impact of data imbalance on classifier performance: (1) using the original dataset without data augmentation, (2) employing partial data augmentation, (3) applying full data augmentation, and (4) balancing the dataset while using Kernel Principal Component Analysis (KPCA) for dimensionality reduction. Our findings revealed that both algorithms achieved accuracies exceeding 90%, even when employing the original non-augmented data. When partial data augmentation was employed both algorithms were able to achieve accuracies beyond 98%. Full data augmentation yielded slightly better results compared to partial augmentation. After reducing dimensions from 18 to 11 using KPCA, both classifiers maintained robust performance. SVM achieved an overall accuracy of 98.72%, while RF achieved 96.06% accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Light Industry Machinery is the property of Light Industry Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability. [ABSTRACT FROM AUTHOR]

In this study, the failure of a certain type of gearbox of a high-speed locomotive group with cracks is examined, and a gearbox failure assessment method that considers the coupled vibration is established and combined with mechanical signal and frequency signal to determine the basis for judging the failure of a faulty gearbox. First, according to the mechanical model of the finite element calculation, we determined the stress weak links and then the layout response stress measurement points and acceleration measurement points. We then calculated the gearbox ratio, meshing frequency, vibration frequency, mechanical response, modal response, and other frequency characteristics using the Hilbert–Huang transform (HHT) method and the fast Fourier transform (FFT) algorithm to analyze the vibration signals generated by different speeds and wheel out-of-roundness conditions. These were used to calculate the frequency of the different vibration sources of the mechanical response on the weak areas. The frequency correlations of the different vibration sources on the mechanical response in the weak areas were then analyzed, and the vibration transmission law of the gearbox case was obtained. The fault determination criterion was then determined, and the final cause of the fault was obtained. [ABSTRACT FROM AUTHOR]

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%. [ABSTRACT FROM AUTHOR]

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The possibility of using the transient response method to assess the technical condition of the hydraulic control system (HCS) of the gearbox to reduce failures of a high-power tractor is analyzed. Based on the data of regulatory and technical documentation, the relationship between the structural and diagnostic parameters of the gearbox is studied. The possibility of monitoring the technical condition of the HCS by the time of the transient process when shifting of gears is experimentally established. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Minimum entropy deconvolution (MED) is a popular algorithm in recent years, extensively applied in feature extraction and fault diagnosis of components such as gearboxes and bearings. However, in the actual computational process, the parameter settings of the MED inverse filter are highly sensitive to the extraction results. To address this issue, an optimized method was firstly proposed for extracting the fault characteristics of rolling bearings using MED. This method takes into account the energy proportion of feature frequencies under different lengths of inverse filters during the MED computation process, thereby determining the optimal parameters for the inverse filter. Additionally, the self-correlation of envelope signals is utilized to further enhance the weak fault characteristic signals of rolling bearings. By integrating self-correlated envelopes with the optimized MED method, a novel method for feature extraction and fault diagnosis of gearbox rolling bearings has been developed. Simulations and tests have verified that this method can effectively enhance the characteristic signals related to bearing faults, and the optimized MED method is significantly superior to the traditional MED and other related bearing signal processing methods. Notably, the self-correlated envelope, due to its ability to significantly enhance impulse components and its excellent denoising characteristics, shows more prominent results in the actual diagnosis of gearbox bearing faults.

Regarding the problem that the traditional near-field acoustic holography (NAH) has difficulty in reconstructing the sound field of the gearbox, this work makes full use of the cyclostationary characteristics of the radiated noise and combines the cyclostationary signal analysis with the equivalent source NAH algorithm to develop a sound imaging method for the gearbox. Firstly, the set of cyclic frequencies was estimated from the noise signal; secondly, the cyclic frequency of interest was selected, and the corresponding cyclic spectral density function was calculated; thirdly, the equivalent source NAH method was developed to reconstruct the potential sound source of the gearbox. Finally, the effectiveness of the sound imaging method was justified by the simulation and experimental data.

Tractor gearboxes have become a crucial mechanization tool in the agricultural industry in Indonesia. However, in practice, many of the hand tractor gearboxes available are still imported. Therefore, research is needed to produce hand tractor gearboxes to support domestic industry self-sufficiency. The gray cast iron hand tractor gearbox is produced using a metal casting process with sand molds. The material resulting from this metal casting process has a composition of 3.249 wt.% C, 1.757 wt.% Si, 0.6 wt.% Mn for the first batch, 3.153 wt.% C, 1.568 wt.% Si, 0.624 wt.% Mn for the second batch, and 3.105 wt.% C, 1.932 wt.% Si, 0.560 wt.% Mn for the third batch. Based on microstructure analysis, the gray cast iron consists of 5% ferrite phase, 95% pearlite, and flake graphite type A with a size of 5. Brinell hardness testing resulted in hardness values of 152, 155, and 164 BHN for the gray iron for the first, second, and third batches respectively. The gray cast iron has a yield strength of 163, 172, and 176 MPa for each batch respectively. Meanwhile, the tensile strength of the gray cast iron from each batch is 285, 311, and 326 MPa

As a valuable method for quantifying irregularity and randomness, multivariate multiscale permutation entropy (MMPE) has found widespread application in feature extraction and complexity analysis of synchronized multi-channel data. Nonetheless, MMPE fails to consider the amplitude information of the data, and its coarse-graining process possesses inherent flaws, resulting in inaccuracies in evaluating entropy values. To address these issues, a novel nonlinear dynamic characteristic evaluation index, named refined composite multivariate multiscale weighted permutation entropy (RCMMWPE), has been developed. This index aims to comprehensively rectify the shortcomings of disregarding amplitude characteristics and incomplete coarse-graining analysis in MMPE, thereby preserving crucial information present in the original time series data. Through the analysis and comparison of multi-channel synthetic signals, the efficacy and superiority of RCMMWPE in assessing the complexity of synchronized multi-channel data have been confirmed. Subsequently, an intelligent fault detection framework is introduced, leveraging RCMMWPE, multicluster feature selection (MCFS), and kernel extreme learning machine optimized by the particle swarm optimization algorithm (PSO-KELM). The proposed fault detection scheme is then applied to test gearbox fault data and extensively benchmarked against other fault detection schemes. The results demonstrate that the proposed gearbox fault detection scheme excels in accurately and consistently identifying fault categories, outperforming the comparison schemes. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This literature review aims to assess the current state of research concerning the use of acoustic emission (AE) analysis for gear condition monitoring and fault detection. There is a particular focus on wind turbine gearboxes and modeling of AE generated by gear transmissions. It identifies key challenges and opportunities for advancing AE analysis as an effective alternative to traditional vibration analysis. Methods: The review critically examines a range of experimental studies and application cases from the last decades, including those utilizing different approaches such as envelope spectrum analysis, wavelet transform, empirical mode decomposition, and machine learning. It also analyzes models developed for predicting AE based on the interaction of two simple surfaces in sliding contact. Results: Findings reveal that AE analysis has seen significant advancements but is largely restricted by its experimental nature. Although several advanced signal processing techniques have been applied, a standard procedure for AE analysis in gear transmissions is yet to be established. Moreover, existing models for predicting AE often overlook factors such as lubrication and surface roughness, affecting their applicability. Conclusion: The development of an analytical model that predicts AE signatures based on specific gear transmission parameters and potential faults is crucial. This need sets AE apart from vibration analysis, which already boasts numerous dynamic, geometric, and phenomenological models. Addressing this gap is essential to progress AE analysis as a reliable process for condition monitoring and fault detection in gear transmissions. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic models. In addition, there are few labelled fault samples in real engineering scenarios, which makes it difficult to perform accurate fault identification for mechanical equipment. Thus, to solve the problem of diagnostic model performance degradation under small sample, noisy and variable load environments, this paper proposes a Multiscale Dilated Convolution and Swin-Transformer (MSDC-Swin-T) method for small sample gearbox fault diagnosis. First, we design the Coordinate Reconstruction Attention Mechanism (CRAM), which enhances the capture of impulse information by coordinate reconstruction. In addition, a multiscale convolutional token embedding module is constructed to extract local features at different scales, and its ability for capturing important features is adaptively enhanced by CRAM. Then, Swin-Transformer is utilized for modeling global dependencies, thus mining more subtle fault features. Finally, the effectiveness and stability of the MSDC-Swin-T is proved on two gearbox datasets. The experiments show that MSDC-Swin-T has superior diagnostic performance under small sample with noise and variable load environments. The diagnostic accuracy is better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The features of the integral tractor concept are considered. The values of controlled interaxle and interwheel power distribution mechanisms are analyzed. Transmission options and simplified kinematic diagrams of interaxle and interwheel power distribution mechanisms are considered. Gear ratios in characteristic operating modes, types of their control, and the required technological bases for their design and manufacture are determined. [ABSTRACT FROM AUTHOR]

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To investigate the lubrication characteristics in high-speed train gearboxes, a two-stage herringbone gearbox with an idle gear was analyzed. The lubricant flow and distribution were shown using the moving particle semi-implicit (MPS) method. A liquid film flow model was brought in to enhance the non-slip wall boundary conditions, enabling MPS to predict the film flow characteristics. This study investigates the influence of gear rotating speed, lubricant volume, and temperature on lubricant flow, liquid film distribution, lubrication state in the meshing zone, and churning power loss. The results indicate that lubrication characteristics depend on the splashing effect of rotating gears and lubricant fluidity. Increasing gear rotating speed and lubricant temperature can improve liquid film distribution on the inner wall, increase lubricant volume, and thus enhance film thickness. The lubricant particles in the meshing zone correlate positively with the gear rotating speed and lubricant volume, correlate negatively with a temperature above 20 °C, and decrease notably at low temperatures. Churning power loss mainly comes from the output gear. As lubricant volume and gear rotating speed increase, churning torque and power loss increase. Above 20 °C, viscosity decreases, reducing power loss; low temperatures lessen lubricant fluidity, reducing churning power loss. [ABSTRACT FROM AUTHOR]

In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a MultiCriterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results. [ABSTRACT FROM AUTHOR]

This research introduces the novel "Kraken-Gear" mechanism, emphasizing the advantages of additive polymer 3D printing in high-ratio gearbox systems for lightweight robotic applications, such as surgical instruments. The innovative kinematic solution provides high torsional system stiffness, substantial gear ratios, and backlash-free transmission. Leveraging the "hot lithography" additive manufacturing method ensures precise and warp-free gearbox components. Targeting medical technology, the gearbox meets stringent requirements: backlash-free, minimal vibration, high precision, and torque, with minimized weight for ergonomic comfort and fatigue mitigation. Computational simulations assess forces and stresses, highlighting the potential of additive manufacturing for cost-effective and functionally efficient gearbox fabrication. Nevertheless, careful material selection remains imperative for optimal functionality, especially in demanding medical applications. In summary, this research underscores a promising approach to gearbox fabrication, emphasizing the critical role of material selection and simulation-based assessments for optimal performance. [ABSTRACT FROM AUTHOR]

Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS setup is initially developed, comprising a Raspberry Pi 4B+ CPU module, a NucleoF401RET6 MCU, an OLED screen, and an Adxl1002z accelerometer for acquiring vibration signals at the desired sampling frequency stored in the CPU memory. Further, an RF model is also developed to classify different types of faults based on features extracted from the acquired vibration data. The model evaluates the precision and reliability of the MEMS setup in capturing and classifying vibration signals. A detailed signal analysis is also conducted to determine the performance of the developed MEMS setup and to investigate the effect of bearing vibration signature due to gear fault and vice versa. The results indicate that bearing faults cause irregularities in the shaft's rotational speed, leading to modulation of the gear mesh frequency (gmf) of gears mounted on the affected shaft. Conversely, gear faults disrupt the shaft's rotational motion, imposing excessive loads on shaft-supported bearings. These disruptions result in distinct vibration patterns characterised by increased harmonics and side bands within the bearing frequency range. The RF model effectively identifies and classifies faults with high accuracy by leveraging its ability to prioritise the most significant vibrational features, resulting in improved predictive performance and robustness.

Gearbox is the most stressed component in an aircraft gas-turbine engine. This implies the need for finding ways to monitor its technical condition. Practice shows that the most efficient method is vibration-based diagnostics. However, this method requires the use of sophisticated measurement systems and highly skilled personnel. This paper shows that errors of gear coupling manufacture and assembly, characteristics of machine operating mode, design factors, frequency modulation of the carrier of the engine rotor speed in the stationary mode of its operation, and wear of teeth flanks determine the width of the spectral component of the gearbox output shaft speed. Using the results obtained from the developed model of the width of gear tooth spectral component, ratios for width of the spectral component of the signal of the “standard” tachometric speed sensor of the gearbox output shaft speed and the corresponding spectral component of its vibration were obtained. Models for repaired and newly manufactured gearboxes and gearboxes with tooth flank wear were proposed. This allowed the development of a number of new diagnostic indicators of a defect. Several examples of the use of these new diagnostic indicators are given for the gearbox of one of turbo-prop engines. The results obtained provide an opportunity to assess the gearbox technical condition in operation.

Faults identification plays a vital role in improving the safety and reliability of industrial machinery. Deep learning has stepped into the scene as a promising approach for detecting faults, showcasing impressive performance in this regard. However, challenges such as noise and variable working conditions often limit the effectiveness of these approaches. This study addresses these limitations by employing a combination of signal-processing methods and neural networks. Specifically, the proposed methodology incorporates maximal overlapping discrete wavelet packet decomposition (MODWPD) for raw vibratory signal, mel frequency cepstral coefficient mapping (MFCC) for time-frequency feature extraction, and a fusion of bidirectional long and short-term memory network with convolutional neural networks (CNN-BiLSTM) to capture local features and temporal dependencies in sequential data. The evaluation is conducted using two diverse experimental datasets, PHM2009 for mixed defects and Case Western Reserve University (CWRU) for bearing faults, under unexpected operating conditions. The proposed method is rigorously tested through stratified K-fold cross-validation, demonstrating superior performance compared to a leading state-of-the-art model. [ABSTRACT FROM AUTHOR]

Ensuring the safety of mechanical equipment, gearbox fault diagnosis is crucial for the stable operation of the whole system. However, existing diagnostic methods still have limitations, such as the analysis of single-scale features and insufficient recognition of global temporal dependencies. To address these issues, this article proposes a new method for gearbox fault diagnosis based on MSCNN-LSTM-CBAM-SE. The output of the CBAM-SE module is deeply integrated with the multi-scale features from MSCNN and the temporal features from LSTM, constructing a comprehensive feature representation that provides richer and more precise information for fault diagnosis. The effectiveness of this method has been validated with two sets of gearbox datasets and through ablation studies on this model. Experimental results show that the proposed model achieves excellent performance in terms of accuracy and F1 score, among other metrics. Finally, a comparison with other relevant fault diagnosis methods further verifies the advantages of the proposed model. This research offers a new solution for accurate fault diagnosis of gearboxes. [ABSTRACT FROM AUTHOR]

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes. [ABSTRACT FROM AUTHOR]

When optimizing a mechanical device, the symmetry principle provides important guidance. Minimum gearbox mass and maximum gearbox efficiency are two single objectives that need to be achieved when designing a gearbox, and they are not compatible. In order to address the multi-objective optimization (MOO) problem with the above single targets involved in building a two-stage helical gearbox with second-stage double gear sets, this work presents a novel application of the multi-criteria decision-making (MCDM) method. This study's objective is to identify the best primary design elements that will increase the gearbox efficiency while lowering the gearbox mass. To carry this out, three main design parameters were selected: the first stage's gear ratio and the first and second stages' coefficients of wheel face width (CWFW). Furthermore, a study focusing on two distinct goals was carried out: the lowest possible gearbox mass and the highest possible gearbox efficiency. Furthermore, the two stages of the MOO problem are phase 1 and phase 2, respectively. Phase 2 solves the single-objective optimization issue to minimize the difference between variable levels and the MOO problem to determine the optimal primary design factors. To solve the MOO problem, the EAMR (Evaluation by an Area-based Method of Ranking) method was also chosen. The following are important features of this study: First, a MCDM method (EAMR technique) was successfully applied to solve a MOO problem for the first time. Secondly, this work explored the power losses during idle motion to calculate the efficiency of a two-stage helical gearbox with second-stage double gear sets. This study's findings were used to identify the optimal values for three important design variables to design a two-stage helical gearbox with second-stage double gear sets. [ABSTRACT FROM AUTHOR]

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Resource and energy efficiency are of high importance in gearbox applications. To reduce friction and wear, an external lubricant supply like dip or injection lubrication is used to lubricate tribosystems in machine elements. This leads to the need for large lubricant volumes and elaborate sealing requirements. One potential method of minimizing the amount of lubricant and simplifying sealing in gearboxes is the self-lubrication of tribosystems using oil-impregnation of porous materials. Although well established in low-loaded journal bearings, self-lubrication of rolling-sliding contacts in gears is poorly understood. This study presents the self-lubrication method using oil-impregnated porous sinter material variants. For this, the tribosystem of gear contacts is transferred to model contacts, which are analyzed for friction and temperature behavior using a twin-disk tribometer. High-resolution surface images are used to record the surface changes. The test results show a significant increase in self-lubrication functionality of tribosystems by oil-impregnated porous sinter material and a tribo-performance comparable to injection-lubricated tribosystems of a sinter material with additionally solid lubricant added to the sinter material powder before sintering. Furthermore, the analyses highlight a significant influence of the surface finish, and in particular the surface porosity, on the overall tribosystem behavior through significantly improved friction and wear behavior transferable to gear applications. [ABSTRACT FROM AUTHOR]

This paper provides a novel application of the multi-criteria decision-making (MCDM) method to the multi-objective optimization problem (MOOP) of creating a two-stage helical gearbox (TSHG) with second-stage double gear sets (SDGSs). The aim of the study is to determine the optimum major design components for enhancing the gearbox efficiency while reducing the gearbox volume. In this work, three primary design parameters are chosen to accomplish this: the gear ratio of the first stage and the coefficients of the wheel face width (CWFW) of the first and second stages. Additionally, the study is conducted with two distinct objectives in mind: the lowest gearbox volume and the maximum gearbox efficiency. Moreover, phase 1 and phase 2, respectively, are the two stages of the MOOP. Phase 2 handles the MOOP to identify the ideal primary design factors as well as the single-objective optimization problem to minimize the difference between the variable levels. Additionally, the Multi-Attributive Ideal–Real Comparative Analysis (MAIRCA) approach is selected to deal with the MOOP. The results of the study are utilized to determine the ideal values for three crucial design parameters in order to create a TSHG with SDGSs. [ABSTRACT FROM AUTHOR]

Aiming at the defect that refined composite multiscale permutation entropy (RCMPE) could not fully extract fault information from vibration signals of rotating machinery, which led to unstable fault identification accuracy of rotating machinery, a fault diagnosis method for rotating machinery based on refined composite multiscale normalized amplitude aware permutation entropy (RCMNAAPE), Laplace scores (LS) and grey wolf algorithm optimization support vector machine (GWO-SVM) was proposed. Firstly, the amplitude aware permutation entropy was used to replace the permutation entropy in RCMPE, and the RCMNAAPE was proposed to extract the fault characteristics of the vibration signals of rotating machinery and generate the feature samples. Subsequently, LS was used to select fewer features from the original high-dimensional fault feature vectors that can more accurately describe the fault state, and sensitive feature samples were constructed. Finally, the low-dimensional fault feature vector was input into the support vector machine optimized by grey wolf algorithm for training and testing, and the fault identification and classification of rotating machinery samples were completed. The RCMNAAPE-LS-GWO-SVM and other fault diagnosis methods were compared and evaluated by using rolling bearing and gearbox fault data set. The results show that the RCMNAAPE-LS-GWO-SVM fault diagnosis method can effectively identify various kinds of rotating machinery faults, and its recognition accuracy is higher than other fault diagnosis methods, among which the rolling bearing fault recognition accuracy reaches 99. 33%, and the identification accuracy of gearbox fault reaches 98. 67% . However, the feature extraction efficiency of this method is not good, and the average feature extraction time is respectively 153. 02 s and 163. 98 s, which is only better than refined composite multiscale fuzzy entropy (RCMFE), but its comprehensive performance is better. [ABSTRACT FROM AUTHOR]