Characteristics Of Vibration Signals Research Articles

Establishment of a Feeding Rate Prediction Model for Combine Harvesters

Feeding rates serve as a vital indicator for adjusting the working parameters of the combine harvester. A non-invasive diagnostic approach to predicting the feed rates of combine harvesters by collecting vibration signals of the inclined conveyor was introduced in this study. To establish a feed rate prediction model, the correlation between feeding rates and vibration signal characteristics was investigated. Vibration signal characteristics in both the time domain and frequency domain were also analyzed in detail. The RMS (root mean square) value and the total RMS value of the one-third octave extracted from the vibration signal were utilized to establish a feed rate prediction model, and field tests were conducted to verify the model performance. The experimental results indicated that the relative errors of the established model range from 3.1% to 4.9% when harvesting rice. With the developed feed rate prediction system, the control system of the combine harvester can acquire feed rate information in real time, and the working parameters can be adjusted in advance, thereby, it can be expected to greatly enhance the working performance of the combine harvesters.

Research on the ditching resistance reduction of self-excited vibrations ditching device based on MBD-DEM coupling simulation.

In plant horticulture, furrow fertilizing is a common method to promote plant nutrient absorption and to effectively avoid fertilizer waste. Considering the high resistance caused by soil compaction in southern orchards, an energy-saving ditching device was proposed. A standard ditching blade with self-excited vibration device was designed, and operated in sandy clay with a tillage depth of 30cm. To conduct self-excited vibration ditching experiments, a simulation model of the interaction between soil and the ditching mechanism was established by coupling the ADAMS and EDEM software. To begin with, the ditching device model was first set up, taking into account its motion and morphological characteristics. Then, the MBD-DEM coupling method was employed to investigate the interaction mechanism and the effect of ditching between the soil particles and the ditching blade. Afterwards, the time-domain and frequency-domain characteristics of vibration signals during the ditching process were analyzed using the fast fourier transform (FFT) method, and the energy distribution characteristics were extracted using power spectral density (PSD). The experimental results revealed that the vibrations ditching device has reciprocating displacement in the Dx direction and torsional displacements in the θy and θz directions during operation, verifying the correctness of the coupling simulation and the effectiveness of vibrations ditching resistance reduction. Also, a load vibrations ditching bench test was conducted, and the results demonstrated that the self-excited vibrations ditching device, compared with common ditching device, achieved a reduction in ditching resistance of up to 12.3%. The reasonable parameters of spring stiffness, spring damping, and spring quality in self-excited vibrations ditching device can achieve a satisfied ditching performance with relatively low torque consumption at an appropriate speed.

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling.

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

A multi-target predictive model for predicting tool wear and surface roughness

To simultaneously predict tool wear and surface roughness accurately during machining, this paper develops a multi-target predictive model that leverages a novel two-step feature-integration approach and Multi-kernel Gaussian process autoregressive regression (MK-GPAR). Compared with the traditional Gaussian process autoregressive regression (GPAR) models, the MK-GPAR demonstrates an enhanced capability in capturing detailed structures within heterogeneous data originating from various sources or disparate data forms. The weight coefficients of the kernel functions in MK-GPAR are typically assigned a value of 1 to minimize their influence on the predictive model. Furthermore, when compared to conventional multi-target predictive models like Gradient Boosting, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), the GPAR not only delivers higher-accuracy predictions for tool wear and surface roughness but also provides corresponding confidence intervals (CI). However, the CI of predictions for tool wear and surface roughness provided by MK-GPAR are inconsistent, which will seriously affect the reliability of the evaluation of the prediction results. The proposed two-step feature-integration approach is employed to enhance signal features effectively, reduce noise, and mitigate its adverse impact, thus enhancing the consistency and smoothness of the CI. The two-step feature-integration approach integrates principal component analysis (PCA) and orthogonal neighborhood preserving projections (ONPP) to merge force and vibration signal features obtained from the fixture's monitoring signals. Following the two-step feature-integration (first PCA, then ONPP) process, the MK-GPAR is utilized to build a multi-target predictive model for tool wear and surface roughness prediction. The impact of the proposed two-step feature-integration method on the prediction performance of the MK-GPAR is demonstrated revealed milling experiments. The experimental findings indicate that the proposed two-step feature-integration method significantly enhances the accuracy of predictions and the consistency of CI of the MK-GPAR. This research lays the foundation for implementing the multi-target predictive model in real industrial environments to predict tool wear and surface roughness.

An experimental study on dynamic response of cement concrete pavement under vehicle load using IoT MEMS acceleration sensor network

Understanding the dynamic response of pavements is crucial for comprehending pavement deformation and interaction mechanisms under vehicle load. There is a notable absence of monitoring systems that involve the in-situ embedding of acceleration sensors within full-scale cement concrete pavement slabs. This research introduces an IoT system designed for the monitoring of pavement vibration responses, with its deployment and installation executed within a field engineering project. By collecting and analyzing the pavement vibration responses to vehicle loads, the study investigates the time–frequency characteristics of vibration signals across various vehicle speeds, sensor depths, and lateral distances. The findings reveal that both the amplitude and main frequency of the vibration signal escalate with vehicle speed, whereas the embedding depth of the sensor impacts only the amplitude, leaving the main frequency unaffected. Through statistical analysis, it was determined that effective sensor spacing on cement concrete pavements should not exceed 120 cm to avoid significant signal attenuation, which on average reaches 50 % at a sensor spacing of 90 cm. These insights offer critical guidance for optimizing sensor layouts on cement concrete pavements and lay the foundation for future research on vibration-based damage detection and vehicle information analysis.

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

Fast adaptive time-varying window length STFT for compound track short-wave defects identification

Track short-wave defect, as a common defect of railway tracks, can cause dynamic response on both vehicle and its components and thus severely affect the stability and safety of running vehicle. The vibration signal, collected by an accelerator mounted on the axle box of vehicle, is a kind of vehicle dynamic response signal. It consists of complex periodic and impact signal components, which poses challenges for track short-wave defects identification. In this paper, the forms and features of vibration signal of vehicle dynamic response are analyzed at first. Then, based on short-time Fourier transform (STFT), a fast adaptive time-varying window length STFT (FATW-STFT) is developed for compound track short-wave defect analysis. Specifically, by quickly calculating a series of time-frequency planes with various window length, modified kurtosis, boundary function, and Rényi entropy are introduced to sift the time-frequency planes and optimize the window lengths at each time point. Unlike other STFT-based methods, FATW-STFT uses fully adaptive window length optimization to distinguish both periodic and impactive signal components, which helps to detect multiform track short-wave defects such as corrugation and impact of welded joint. Both the simulated and measured results indicate that the proposed approach can rapidly and effectively identify the compound track short-wave defects and possesses potential prospect of practical applications.

HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis

In response to the high-noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). Experimental results demonstrate that HMM can accurately diagnose and predict faults in ECS turbofan rolling bearings. The HMM method enhances diagnostic accuracy, and its effectiveness and feasibility in fault diagnosis based on different rolling bearing fault instances are elaborated. By employing the HMM model to establish precise models from decomposed dynamic data, it successfully identifies faults such as the fracture of the bearing cage under biased load conditions, although its performance in recognizing overheating faults is suboptimal.

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.

The DMF: Fault Diagnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fusion

Effective fault diagnosis for diaphragm pumps is crucial. This paper proposes a diaphragm pump fault diagnosis method based on deep learning and multi-source information fusion (DMF). The time-domain features, frequency-domain features, and modulation features are extracted from the vibration signals from eight different positions. After feature enhancement and data preprocessing, the features are input into auto encoders (AE), convolutional neural networks (CNN), and support vector machines (SVM) to obtain the diagnostic results. The results indicate that the DMF method achieves a fault diagnosis accuracy of 99.98%, which is on average 9.09% higher than using a single diagnostic model. The demodulation method is more suitable for vibration signal feature extraction of the diaphragm pump, while the CNN is more suitable for identification of diaphragm pump faults. Specifically, it outperformed the sampling point 1-DPCA-AE model by 13.98% and the sampling point 4-DPCA-SVM model by 8.98%.

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox

Abstract Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.

A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks

Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining synthetic minority oversampling technique to realize time–frequency conversion augmented oversampling. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified deep residual network, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.

An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples

Aiming at limitations in fully exploiting the temporal correlation features of the original signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training data under multiple working conditions, this paper proposes an optimized Convolutional Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) scheme for bearing fault diagnosis under multiple working conditions with limited training samples. A CNN-BiLSTM network is developed to obtain precise fault features and high detection accuracy by extracting high-dimensional and temporal correlation features of raw vibration signals. An improved particle swarm optimization (PSO) algorithm is leveraged to optimize the training hyperparameters of the CNN-BiLSTM network for further advances in fault diagnosis performance. The optimized CNN-BiLSTM network is regarded as a pre-trained model and transferred to new working conditions to achieve satisfactory fault diagnosis results based on limited training samples. Several comprehensive experiments are implemented to confirm the excellent performance of the proposed schemes, especially efficiently addressing the challenges of model training and fault diagnosis in new working conditions with scarce samples.

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.

Numerical and experimental research on vibration signal characteristics of water pipeline leakage

In the leak detection of water pipelines, interference and environmental noise directly affect the effectiveness of leak detection and the accuracy of leak point location, so it is necessary to study the real signal. Previous studies mostly rely on a single test to improve the positioning accuracy by collecting vibration signals for denoising, but denoising will also affect the real signal, and the actual detection situation is complex, so it is difficult to deal with all working conditions only through the test. With the rapid development of computer technology, the combination of numerical simulation and experiment is expected to solve this problem. Therefore, this paper takes the acceleration signal as the research object, collects and simulates the leakage signals under different pressure strengths by combining experiments with numerical simulation, and verifies the feasibility and accuracy of a numerical simulation model. The generation of leakage vibration acceleration under different pressure and different leakage hole radius is simulated. Research findings: a. the leakage signal energy has a power function relationship with the pressure and an exponential function relationship with the leakage radius; b. the characteristic frequency is the variable of the characteristics of pipeline and leakage hole, which is independent of the pressure; c. the power of each peak frequency has a step-by-step change trend with the energy of the signal; d. the power of acceleration signal concentrates to a higher frequency with the increase of pressure and the decrease of leakage radius. This study provides a basis for identifying, detecting, and extracting leakage signals in a real noise environment, helps to improve the accuracy of acoustic emission signal detection, and can provide a theoretical basis for pipeline leakage detection research.

Unbalanced Feature Identification of Rotor System Based on Fused Cross‐Correlation Fast Fourier Transform

Rotor system unbalance is one of the most important factors that affects the operating accuracy and stability in aerospace engineering. The extraction and identification of unbalanced features is the premise for the dynamic balance of rotor system. In order to accurately identify the unbalanced vibration feature, based on the fused cross‐correlation fast Fourier transform (FC‐CFFT) method, an unbalanced feature extraction method of the rotor system is proposed. The convolution window weighs the data of the vibration sequence and carries out delay processing. Through spectrum calculation, the phase information of the vibration signal is obtained. And the amplitude information of the vibration feature can be acquired through cross‐correlation calculation. The unbalanced features of the rotor system vibration signals are identified under different working conditions through simulation and experiment. The results show that this method is more accurate than FFT, the cross‐power method, and the sine‐approximation method in extracting different unbalanced vibration features of the rotor system. The extraction accuracy of unbalanced feature phase and amplitude reaches 98.61% and 97.27%, respectively. After the unbalanced feature is extracted, the dynamic balance experiment can be carried out. The unbalanced feature is used for dynamic balance compensation. In this way, the average amplitude decreased by 76.35%. The high accuracy of the FC‐CFFT method for extracting the unbalanced feature has been verified. This research provides a theoretical foundation for the vibration signal processing and dynamic balance control of the rotor system.

Dijagnostika kvarova u ležajevima kotrljajućih elemenata poboljšana AI - sveobuhvatan pristup analizi vibracija

This research presents a comprehensive approach for bearing fault diagnosis using artificial intelligence (AI), particularly through the application of artificial neural networks (ANNs). By integrating these networks into vibration analysis, the approach aims to meet the critical need for prompt fault detection. The methodology comprises three key steps: vibration signal acquisition, feature extraction, and fault classification. Experiments were conducted to acquire vibration signals for the test bearings on a machinery fault simulator. Six time-domain features were extracted using MATLAB, creating a comprehensive dataset for training the ANN models with three algorithms: Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGBP), and Bayesian regularization backpropagation (BRBP). The BRBP algorithm achieved the highest correct classification rate (97.2%), followed by LMBP (90%) and SCGBP (83.6%). To evaluate their efficacy in bearing fault classification, these three networks were simulated, revealing that BRBP could predict all four classes of bearings with zero errors.

A life prediction method based on MDFF and DITCN-ABiGRU mixed network model

A single network model exhibits limitations in the life prediction of rotating machinery for the various fault types and uncertain fault occurrence. Therefore, a network prediction model combining multi-domain feature fusion (MDFF) and distributed TCN-Attention-BiGRU (DITCN-ABiGRU) is proposed to enable a more accurate life prediction of rotating machinery. Firstly, the features of vibration signals collected from multiple sensors are extracted in the time, frequency, and time-frequency domains. Subsequently, dimensionality reduction optimization is conducted on these multi-domain features to eliminate useless information features. The temporal convolutional network (TCN) model is constructed to capture the critical information reflecting the fault characteristics of rotating machinery through the attention mechanism, and the dependencies of the whole training process are captured by the BiGRU network. Finally, precise prediction of the lifespan of rotating machinery is achieved by constructing a health indicator curve (HI). The proposed methods are verified through the life prediction of rolling bearings from the IEEE PHM Challenge 2012 dataset and ball screw pairs from a designed experiment. The experimental results show that the proposed MDFF and DITCN-ABiGRU model achieves a better score and lower error than the convolutional neural network (CNN) and GRU models.

Development of technology and methodology for monitoring the technical condition of metalcutting machines

This work was carried out with the aim of developing the basic provisions of an automated vibration monitoring system for metal-cutting equipment. The paper presents the theoretical foundations of vibration monitoring technology, which uses regular measurements of vibration parameters, their frequency analysis, and mathematical modeling of degradation processes in machines. Vibration monitoring technology allows to determine the time points necessary for repair and maintenance of equipment based on its actual condition. Applications of vibration monitoring are reducing losses associated with equipment failures and reducing maintenance and repair costs, as well as increasing the safety of work. The most relevant is the implementation of a vibration monitoring system when diagnosing the technical condition of bearings and gears (metal-cutting machines, hydraulic systems). As a result of the research, diagnostic models of changes in the technical condition of metal-cutting machines and vibration characteristics of typical defects were obtained. A method has been developed for assessing the dynamic quality of metalcutting machines using a complex quality indicator determined by measuring and analyzing the spectral characteristics of vibration signals.