Characteristics Of Vibration Signals Research Articles

Lithological identification based on high-frequency vibration signal analysis

Lithology changes affect drilling efficiency and safety during drilling. At present, lithology is usually identified by analyzing logging data in engineering applications. There is a certain lag due to the limitation of logging instrument installation location. This paper proposes a new rock formation identification method, which bases on high-frequency measurement sensors to record the vibration of drilling tools, and extracts the time and frequency-domain features of data. Then neural network is used to establish the lithology recognition model, so as to identify the rock formation change by using vibration signal. The method has been verified by field experiment. A lithology identification model is established by using the features of vibration signal. And average recognition accuracy of the model is 89.57%. The model accurately identifies the Soil layer, Sandstone, Strongly weathered siltstone and Medium-weathered siltstone. The identification results are in good agreement with the geological information.

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.

Research on 750kV Reactor Vibration Based on Electromagnetic-mechanical Field Coupling Finite Element Model

The vibration signal characteristics of a 750kV shunt reactor with high voltage level and high vibration amplitude is one of the important indexes to evaluate the operating condition of the reactor. Since the high level of safe operation with Ultra-high voltage power equipment, it is challenging to collect long-term and extensive vibration signal data under safe operating conditions. This paper establishes the electromagnetic-mechanical field coupling finite element model (FEM) of a 750kV shunt reactor under a COMSOL open-source simulation environment. The resonance effect of magnetostriction of the iron core and the Maxwell forces between the iron core discs on the reactor structure is analyzed. The vibration distribution and typical frequency characteristics of each part of the 750kV shunt reactor are analyzed to provide a reference for the research of vibration fault diagnosis of Ultra-high shunt reactor.

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

In recent studies, Transformer collaborated with convolution neural network (CNN) have made certain progress in the field of intelligent fault diagnosis by leveraging their respective advantages of global and local feature extraction. However, the multihead self-attention block used by Transformer and cross-channel convolution mechanism existing in CNN would make the collaborative models overly complex, leading to higher hardware requirements and limited industrial application scenarios. Therefore, this paper proposes a lightweight fault diagnosis framework called LiConvFormer to address the aforementioned challenges. First, a separable multiscale convolution block is designed to extract multilocal receptive field features of vibration signals and greatly reduce the learning parameters and computations. Second, a broadcast self-attention block is developed to capture critical fine-grained features within the signal’s global scope, while avoiding cumbersome operations such as matrix multiplication and multidimensional exponentiation. Experimental results on three mechanical systems show that the proposed framework can accommodate advantages of lightweight and robustness compared to the recent Transformer and CNN-based fault diagnosis methods; moreover, the superiority of the above two blocks is also verified. The code library is available at: https://github.com/yanshen0210/LiConvFormer-a-lightweight-fault-diagnosis-framework.

Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots

For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature information of the vibration and images are fused in the feature layer. Then, the improved weighted K-nearest neighbor (WKNN) algorithm is used to achieve the terrain classification during the travel process of tracked robots. Finally, the experimental results verify that the proposed method improves the terrain classification accuracy of the tracked robot and provides a basis for improving the stable autonomous driving of tracked vehicles.

Fine-Gained Recurrence Graph: Graphical Modeling of Vibration Signal for Fault Diagnosis of Wind Turbine

Benefiting from the recent successes of convolutional neural networks (CNNs), many studies have modeled the vibration signal of energy system into a two-dimensional (2D) input graph to amplify and highlight fault features. However, most works seldomly consider the system dynamic characteristics, which may affect the knowledge discovery and diagnosis quality. To address this issue, this paper proposes a novel graphical modeling approach, termed as fine-gained recurrence graph (FRG), to capture dynamic characteristics of vibration signals from the view of nonlinear dynamics. FRG focuses on modeling the temporal correlations and tendencies between state vectors in phase space and then visualizes the characteristics to represent intrinsic dynamic changes of the vibration signals into 2D graphs. The information representation capability of the proposed FRG is verified using different dynamic signals. Based on this finding, an ensemble fault diagnosis model is proposed fusing FRG with deep CNNs. Meanwhile, transfer learning is accompanied to deal with the training difficulties of deep CNNs. Finally, case studies on experimental data and real wind turbine data illustrate its effectiveness and feasibility. Comparisons with four state-of-the-art approaches have confirmed the preferable information representation and diagnosis performance of the proposed approach.

Periodic-Filtering Method for Low-SNR Vibration Radar Signal

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR.

Application of VMD and dynamic wavelet noise reduction techniques in rolling bearing fault diagnosis

This study proposes a denoising technique based on VMD and dynamic wavelet to address the difficulty of extracting rolling bearing fault vibration signal features in a noisy background. Firstly, the vibration signal is decomposed using the VMD method and the extracted IMFs are filtered. Then, dynamic wavelet are used to denoise the confused IMFs, and the IMFs are reconstructed to achieve joint denoising. Finally, a deep learning method is applied to adaptively extract the features of the noise-reduced signals for fault diagnosis. The effectiveness of the method is confirmed through experimental verification, demonstrating its ability to remove noise information from the signal and improve the presentation of fault characteristics, leading to improved accuracy in fault diagnosis.

Research on Liquid Flow Rate Detection of Mixed Fluid Based on Vibration Signal Characteristic Analysis of Gas Liquid Two-Phase Flow

The online detection of liquid flow rate in gas-liquid two-phase flow has become an important factor in ensuring the safe operation of gas wells. In this paper, the real-time measurement of liquid holdup in gas-liquid two-phase flow is carried out by analyzing the characteristics of vibration signals excited by gas-liquid two-phase flow impacting on the pipe wall. Firstly, an acceleration sensing detection and processing system is constructed to obtain the vibration signals excited by gas-liquid two-phase flow impacting on the pipe wall. Then, the pure airflow vibration signals at different flow velocities and the gas-liquid two-phase flow excitation vibration signals at different liquid flow rates are tested, respectively, and the time-frequency characteristics analysis based on STFT is implemented. The practice shows the following: firstly, the frequency band of 6.5−15 Hz is identified as the characteristic frequency band of liquid flow rate in gas-liquid two-phase flow. Secondly, the liquid holdup is positively correlated with the vibration energy in its characteristic frequency band. Thirdly, a mathematical model of the relationship between liquid flow rate and vibration energy is constructed. Finally, the measurement error of liquid holdup is within 10%. This research method has laid a good foundation for the subsequent detection of characteristic parameters of each phase in gas-solid-liquid complex multiphase flow fluids, and it has certain application and promotion value.

Construction of intelligent multi-construction management platform for bridges based on BIM technology

ABSTRACT In order to improve the efficiency of bridge intelligent multi-construction management, this paper combines BIM technology to build an intelligent multi-construction management platform for bridges, and studies the realization of vibration signal processing, feature extraction, fault diagnosis and wireless communication functions. Moreover, this paper completes the extraction of vibration signal features from three aspects: time domain, frequency domain and time–frequency domain, and manages the bridge construction process according to the extracted features. In addition, this paper establishes the integration between the bridge template design optimization module and the BIM model through the API interface of the Revit platform, realizes the extraction of design information from the BIM model of the bridge and combines template attribute information and safety design specifications to process it. From the simulation evaluation results, it can be seen that the bridge intelligent multi-construction management platform based on BIM technology proposed in this paper can effectively improve the management efficiency of bridge intelligent construction.

Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO‐LSSVM

AbstractFaults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

Research on feature extraction method of spindle vibration detection of weak signals for rapier loom fault diagnosis in strong noise background

Fault diagnosis of rapier loom is an inevitable requirement to meet the demand of intelligent manufacturing. Facing the strong noise interference caused by complex working environment, accurate and reliable vibration signal detection of blade loom spindle is the key to realize the rapier loom fault diagnosis. This paper proposes a method to extract the spindle vibration signal of the rapier loom by Adaptive Piecewise Hybrid Stochastic Resonance (APHSR) after the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Firstly, ICEEMDAN is used to pre-process the weak vibration signal containing noise, decompose the signal into multiple IMF components and display the high and low frequency signal characteristics of the original signal. Then, the energy density method and the correlation coefficient method are used to remove high and low noise, respectively, to filter the optimal IMF components, and then the signal containing valid information is reconstructed. Finally, the reconstructed signal is input to APHSR for noise-assisted enhancement after scale transformation to restore the faint vibration signal feature frequencies and achieve effective feature extraction. Through the simulation experiment and the engineering fault experiment analysis, comparing ICEEMDAN-APHSR with CEEMDAN-SR, ICEEMDAN-SR, CEEMDAN-APHSR methods. The difference between the spectrum amplitude, the spectrum amplitude and the maximum noise and the maximum signal to noise ratio (SNR) of the fault feature frequency of the rapier loom spindle bearing increased by 3.3668 dB,1.7205 dB,2.3952 dB, respectively. The results show that ICEEMDAN-APHSR method can accurately extract the fault feature frequency of the spindle bearing of rapier loom, and effectively solves the problem of extracting the weak vibration signal feature of rapier loom in the background of strong noise. This method is beneficial to the future research of rapier loom fault diagnosis, and is of great significance to promote the maintenance of loom equipment and production safety and quality.

Noise effects analysis on subspace-based damage detection with neural networks

Structural health monitoring (SHM) is nowadays a significant and topical issue in the civil engineering field, whose first-level task is the damage detection job. Recent advances in artificial intelligence (AI) combined with non-destructive assessments based on output-only vibration signals represent the frontier of SHM. The present research study has handled an AI-based multi-class damage classification task on a numerical benchmark beam problem with three different procedures. The main purpose was to investigate the noise effects of the analyzed damage detection procedures simulating real-world accelerometer sensors. The first damage identification method leverages simple statistical features of vibration signals, whereas the second method additionally considers a subspace-based damage indicator. The third method relies on a dataset composed of a specific set of damage indicators. Two deep learning (DL) techniques have been adopted to compare and verify the results, i.e. a multi-layer perceptron (MLP) and a one-dimensional convolutional network (1D-CNN). Three groups of different reasonable signal-to-noise ratio (SNR) levels were set up to simulate micro electro-mechanical system (MEMS) accelerometer sensors in real-world conditions to evaluate noise effects. In a nutshell, the results demonstrated that the DL models based on stochastic subspace identification (SSI) methods have good noise immunity performance for damage detection purposes.

Designing and Manufacturing of Industrial Robots with Dual-Angle Sensors Taking into Account Vibration Signal Fusion

Industrial robots are an important way to realize the development of industrial industry intelligence and automation, an important driving force to accelerate the construction of Industry 4.0, and an important basic force to improve the professional level of industrial industry. The development of industrial robot technology has enhanced the requirements of its detection quality and the demand for data comprehensiveness. Most of the previous robots obtain the corresponding data through a single sensor system, which has a large error and a high limitation of the range of data obtained. The detection method of industrial robots has the problems of high cost and high professionalism. Therefore, this paper constructs a dual-angle sensor industrial robot detection system based on the fusion of multisource vibration signals and achieves the fusion of multisensor signals through Kalman filtering to provide reliable analysis data for the dual-angle sensor detection system. The experimental results show that Kalman filtering can effectively remove noise while preserving the original vibration signal characteristics and structure and improve the reliability and validity of the vibration signal. In addition, the dual-angle sensor detection system can realize the position detection of industrial robots according to the command standard and obtain the corresponding residual and standard values through system data analysis. The overall optimization of the system algorithm enhances the accuracy and reliability of the measurement.

Diversity maximization-based transfer diagnosis approach of rotating machinery

The existing transfer diagnosis methods based on entropy minimization are easy to lead to trivial solution. To solve this problem, a deep diversity maximization-based adversarial transfer diagnosis approach for rotating machinery is presented in this paper. Firstly, the deep convolution neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The diversity maximization strategy is taken to balance the entropy minimization, so as to avoid trivial local minimum. The categories predicted by nontrivial domain adaptation method are more diverse. Moreover, the entropy is conducted to evaluate the uncertainty of the predicted result of the classifier. Using this deterministic strategy based on entropy to adjust the domain discriminator. The experimental study demonstrates the effectiveness of the developed method.

Transformer Vibration Signal Feature Extraction Based on Compressed Sensing and Wavelet Packet

Transformers are important equipment in the power system. Aiming at the collection, transmission, storage, and processing of massive high-dimensional vibration data in the process of transformer vibration online monitoring, this paper proposes a transformer vibration signal feature extraction method based on compressed sensing and wavelet packets. Firstly, the PartHadamard measurement matrix is used to compress the transformer vibration signal, and then the characteristics of the transformer vibration signal are extracted based on the wavelet packet decomposition and normalized wavelet information entropy. The proposed method completes feature extraction on the measured vibration data of the transformer, and three classification algorithms are applied to carry out simulation experiments. The results show that the proposed feature extraction method can effectively extract the vibration characteristics reflecting the operating state of the transformer while greatly reducing the dimension of vibration data, which provides a reference for the online monitoring of transformer vibration.

Study and application on the early damage signal characteristics of ultra-low-speed and heavy-load rolling bearings of large amusement machinery

Ultra-low-speed and heavy-load bearings are widely used in large amusement machinery. Their breakdown results in great economic losses and possibly even personal injuries. Using their damage signal characteristics to quickly identify early damage is an effective means of preventing accidents. In this study, to determine the early damage signal characteristics of the ultra-low-speed and heavy-load slewing mechanism, a typical ultra-low-speed and heavy-load bearing rotation experimental platform and signal testing system are designed and built, so as to simulate the operation of the rolling bearings of large amusement machinery with ultra-low speed and heavy load. Next, by constructing different bearing damage sizes and operating conditions, the vibration signal characteristics of different degrees of crack damage are analysed, along with the signal variation law of the same damage under different operating conditions. The effectiveness and practicability of this method are verified through the application analysis of a slewing bearing of a typical piece of large amusement machinery. The results of this study provide a reference for quickly identifying early damage in similar equipment and determining the damage state.

Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning.

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.

A multi-stream multi-scale lightweight SwinMLP network with an adaptive channel-spatial soft threshold for online fault diagnosis of power transformers

Fault diagnosis of power equipment is extremely crucial to the stability of power grid systems. However, complex operating environments, high costs and limitations of single-modal signals are the biggest bottlenecks. To this end,a multi-tream, multi-scale lightweight Swin multilayer perceptron (MLP) network (MLSNet) with an adaptive channel-spatial soft threshold is proposed in this paper. First, a Res2net-based feature-enhanced method is used to learn the correlated features of vibration and voltage multi-modal signals. Second, a novel MLSNet is designed to combine the benefits of Swin transformers with an MLP with a lightweight convolutional neural network and employs a staged model to extract various scale features. Third, an adaptive deep fusion approach employing a channel-spatial soft threshold module is used to integrate and recalibrate staged information at different scales. The overall accuracy of the proposed method can reach 98.73% in various experiments, potentially making it an effective method for online fault diagnosis of power transformers.