Vibration Signal Processing Research Articles

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Fault diagnosis of driving gear in battery swapping system based on auditory bionics

Rack and pinion drives (RPD) are widely used in battery swapping system (BSS) for electric heavy trucks (EHT), and due to the continuous heavy-load and high-intensity operation, along with the electric erosion, the gears in the RPD are always damaged, which causes unexpected consequences such as downtime or safety incidents. The working conditions of the RPD in BSS include uncertain noises, fluctuant and low speed, which pose steep challenges to accurate fault diagnosis. Considering the auditory resistance of interference, the low-frequency sensitivity of auditory perception, and the auditory saliency mechanism, to leverage the advantages of auditory perceptual mechanism in addressing the above challenges, as the contribution in artificial intelligence, we propose an entire vibration signal processing scheme based on auditory bionics, including some mathematical models for auditory mechanisms. For the application in engineering, the proposed scheme is employed for fault diagnosis of RPD in BSS in unique working conditions. First, adaptive resampling is used to smooth the speed fluctuation, then, Gammatone filters are employed to transform vibration signals to cochleograms, after that, based on auditory stream segregation and selective attention mechanisms, effective frequency channels and salient features are extracted from the cochleograms, besides, to improve the diagnosis accuracy, binaural features are also extracted, finally, based on (sectional) sparse representation and fusion, fault diagnosis is achieved. The effectiveness of the fault diagnosis scheme is demonstrated using a BSS prototype system.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

Observations and Considerations for Implementing Vibration Signals as an Input Technique for Mobile Devices

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users’ hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research.

New Fault Diagnosis Method for Rolling Bearings Based on Improved Residual Shrinkage Network Combined with Transfer Learning.

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance.

Concept and Application of Envelope Spectrum Analysis in the Field of Vibration Signal Processing

Concept and Application of Envelope Spectrum Analysis in the Field of Vibration Signal Processing

Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method’s average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized.

Composite failure analysis of an aero-engine inter-shaft bearing inner ring

The inter-shaft bearing is a critical component in dual rotors aero-engine. Due to the complex and severe loads caused by the interactive excitation of HP and LP rotors during operation, the inter-shaft bearing is prone to damage accumulation and failure. Focusing on a typical failure of inter-shaft bearing inner ring fragmentation and cannot be spliced together completely in high thrust-weight ratio turbofan engine, this paper conducted morphological investigation, fractographic analysis, vibration signal processing, finite element numerical simulation, and fully studied the failure mechanism of this failure.The results indicated that: there exists an uncoordinated resonance speed of the HP rotor in close proximity to the operating speed. This puts the rotor in non-synchronous procession state, accompanied by the uncoordinated angular displacement of the inner and outer rings, which resulting in a lager rolling dynamic load in bearing inner ring. This further leads to high tangential impact load at the root fillet of the anti-rotation pin, causing significant stress concentration, and eventually leads to the generation of initial cracks. These cracks persist and propagate into fatigue oblique fracture over time. After oblique fracture, the modal frequencies of the inner ring become more dispersed. Under the rolling dynamic load (traveling wave load), nodal diameter vibration occurs, causing high stress vibration fatigue damage, and ultimately leading to multiple-point simultaneous fragmentation.The investigation suggests that reducing the stress levels at the root fillet of the anti-rotation pin of the inner ring is an effective approach to prevent bearing failure. This can be achieved through two methods: one is optimizing the rotor structure to decrease the rolling dynamic load on the inner ring, the other is eliminating the anti-rotation pin and appropriately reducing the tightening torque of the compression nut and the interference between the bearing inner ring and the HP turbine rear journal.

On the experimental full scale vibrational response analysis of a large pleasure yacht

This paper presents an experimental investigation carried out on a large pleasure yacht during a sea trial using vibration signal processing methods to characterize its dynamic operational behaviour and to identify contributions of the main sources acting on the system including the onset of the propeller cavitation phenomenon.Synchronous averages were computed to isolate vibration components associated with a specific source acting on the system. Their use jointly with variance computation allowed the detection of the rise of cavitation. Spectral Kurtosis analysis suggested optimal-bandwidth of the filter for system response demodulation and so better identify the rise of high frequency energy of bursting contents induced from cavitation strictly linked to propeller rotation.Cyclic modulation spectrum was also adopted, which allows to extract the modulation features of cyclostationary signals, shows the rise of an interaction between high frequency contents and blade passage phenomenon approaching cavitating condition.The results seem to provide an interesting solution based on the vessel vibrational response to define source contribution to the targets for a better identification of the dynamic system. Moreover, the proposed diagnostic methods reveal reliable tools for real time condition-based monitoring of the marine propeller to distinguish cavitation and wake energy contribution using vibrational response measured on the hull instead of hydrophone arrays.

Image-assisted 2D partition denoising method for distributed optical fiber vibration measurements

The traditional one-dimensional denoising method, commonly applied in vibration signal processing, would encounter issues as the calculation complexity, difficulty in maintaining spatial consistency and inability to address noise distributed spatially when measurements are from multiple points. Distributed optical fiber vibration sensing technology produces measurements densely distributed on both time and spatial axes, accompanied by inevitable noise from demodulation error, uneven packaging of sensing components and environmental interference, in which condition proposing new two-dimensional denoising method becomes particularly important. Focusing on the above spatiotemporal denoising target, this paper proposes a new image-assisted 2D partition denoising method by introducing the morphological methods, improving the traditional empirical mode decomposition, and raising the partition denoising strategy. The experimental and simulated distributed vibration measurements of a cantilever beam were carried out to verify the proposed method from the perspective of modal analysis implemented by the DATA-driven stochastic subspace identification. The analysis results indicate that the proposed method could handle the spatiotemporal noise simultaneously and is in favor of improving the continuity of mode identification results especially.

Processing and analysis of friction vibration signal of diamond-like carbon film microtextured dry gas seal rings

PurposeThis paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.Design/methodology/approachThe friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.FindingsThe end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.Originality/valueThe results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Bearings are key components in mechanical equipment, which are widely used in various fields such as automobiles and airplanes. Aiming at the analysis of vibration signal processing under the variable speed condition of bearings, this paper proposes a new bearing fault diagnosis method, which firstly resamples the vibration signals in the angular domain, and then converts the resampled signals into images by the relative position matrix method, and finally uses the transfer learning to automatically extract the features and classify them. To verify the effectiveness of the method, it is tested on the Case Western Reserve University bearing fault dataset and University of Ottawa bearing fault dataset respectively. Compared with other time series to image methods (Recurrence Plot, etc) and other pre-trained models (GoogLeNet, etc), the proposed method has some advantages in terms of accuracy, image generation time, training time, and testing time. The accuracy of the proposed method in this paper reaches more than 90%, which suggests its potential effectiveness in the classification of bearing faults under variable speed working conditions.

Проблемы диагностики современных тепловозных двигателей

Objective: to consider the issue of diagnostics of modern diesel engines, in particular vibration diagnostics. Consider modern strategies and methods based on vibroacoustic signals that allow you to track and diagnose diesel engine malfunctions, as well as an assessment of the working process in the engine. To determine which means of measuring vibration parameters and methods of processing vibration signals can be considered the most reliable and informative. Propose to make adjustments to the currently used methods of processing vibration signals. Methods: comparison of the effectiveness of vibration measurement tools and mathematical methods of vibration signal processing. Results: the necessity of choosing mathematical methods of vibration signal processing is indicated, both for individual components of a diesel locomotive engine and for evaluating the workflow. Modern mathematical methods, applied to vibration diagnostics, require updating, due to an increase in computing power, as well as due to the development of powerful signal processing methods. To increase the reliability of the comparison results, it is necessary to take into account the large variability of the components and processes of the diesel engine. The need for additional study of modern mathematical methods of vibration signal processing is revealed. Practical importance: the necessity of introducing more modern methods of vibration signal processing is shown, which will optimize the durability of the components’ design using long operating cycles, reduce maintenance costs, track the service life of the internal combustion engine during the operation of the locomotive, improve the monitoring and diagnostics systems of the locomotive engine. The presented methods of assessing the use of certain methods of vibration diagnostics for diesel locomotives can be recommended for practical use.

MSGAFN: Multi-scale graph attention fusion network for machine fault diagnosis

Fault diagnosis is essential in various fields, such as industrial manufacturing and engineering maintenance. Graph neural networks that take graph data as input can explore the relationships between data, which has strong feature expression capabilities. Currently, traditional fault diagnostic methods based on graph neural networks (GNN) are challenging to capture local and global feature information of data effectively. Most GNN models fail to consider the inherent differences between adjacent nodes, and they perform poorly in processing vibration signals in real-world industrial scenarios that commonly have strong noise. To resolve these concerns, this paper proposes a method for fault diagnosis based on a multi-scale graph attention fusion network (MSGAFN). In MSGAFN, data samples are constructed as clan graphs with multiple information scales to effectively represent local and global information of the graph structure data. Additionally, MSGAFN designed a new multi-scale feature fusion layer (MSFFL) to automatically learn the weights of adjacent nodes to represent their importance to the central node and reflect the differences between different adjacent nodes. This method is fully validated on bearing and gear datasets. The experimental results demonstrate that the proposed method exhibits excellent performance under conditions of imbalanced datasets and strong noise, providing a promising approach for bearings and gears fault diagnosis in real-world industrial scenarios with strong noise.

Fault Diagnosis of Linear Guide Rails Based on SSTG Combined with CA-DenseNet

Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production. Thus, this paper proposes a new method of conducting the fault diagnosis of linear guide rails. First, synchrosqueezing transform (SST) combined with Gaussian high-pass filter, termed as SSTG, is proposed to process vibration signals of linear guide rails and obtain time-frequency images, thus helping realize fault feature visual enhancement. Next, the coordinate attention (CA) mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework, thus realizing accurate fault classification. Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%. The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.

Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a “black-box” deep neural network. An approach to systematically design neural networks is by unrolling algorithms, such as learned iterative soft-thresholding (LISTA). In this paper, the multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived by embedding a designed multi-layer sparse coder to the convolutional extension of LISTA. Then the multi-layer convolutional dictionary learning (ML-CDL) network for mechanical vibration signal denoising is proposed by unrolling ML-CLISTA. By combining ML-CDL network with a classifier, the proposed denoising method is applied to the explainable rolling bearing fault diagnosis. The experiments on two bearing datasets show the superiority of the ML-CDL network over other typical denoising methods.

A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention

Identification of motor vibration signals is one of the important tasks in motor fault diagnosis and predictive maintenance, and wavelet time–frequency diagram is a commonly used signal analysis method to extract the frequency and time characteristics of signals. In this paper, a method based on local sensitive hashing (LSH)-Swin transformer network is proposed for identifying the wavelet time–frequency diagrams of motor vibration signals to analyze the fault types. The traditional Swin transformer model converges slowly due to the smoothing of the attention distribution when dealing with data with sparse features, while the method proposed in this paper reduces the smoothing of the computed attention and enables the network to learn the key features better by introducing locally-sensitive hash attention in the network model, dividing the sequences in the input attention into multiple hash buckets, calculating the attention weights of only some of the vectors with a high degree of hash similarity, and by sampling discrete samples with the use of the Gumbel Softmax. The experimental results show that the method proposed in this paper has better recognition accuracy and higher computational efficiency compared with the traditional network when processing wavelet time–frequency maps of motor vibration signals, and its validation accuracy reaches 99.7%, the number of parameters also has a decrease of about 13%, and the training network to reach converged epochs is also faster. The method in this paper can provide an effective solution for the analysis and processing of motor vibration signals, and has certain application value in practical engineering.

Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model.

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the Gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.

Fault diagnosis using signal processing and deep learning-based image pattern recognition

Abstract The vibration signal is a typical non-stationary signal, making it challenging to use traditional time-frequency analysis techniques for fault diagnosis. Therefore, this work investigates the processing of vibration signals and proposes a deep learning method based on processed signals for the fault diagnosis of ball bearings. In this work, the fault diagnosis is formulated as an image classification problem and solved with deep learning networks. The intrinsic mode functions (IMFs), converted from the vibration signals in the time domain, are then transformed into symmetrized dot pattern (SDP) images. In order to increase classification accuracy, the SDP parameters in this study are chosen by optimizing image similarity. The feasibility and accuracy of the proposed approach are examined experimentally.

Identification of Spalling Fault Size of Ball Bearing Based on Modified Energy Value

Abstract The size of bearing outer ring spalling failures has a significant impact on the vibration and service life of rotating machinery. It is necessary to judge the size of the outer ring fault size. Most of the vibration analyses identify the bearing fault size only in terms of the shock interval. The decreasing impact of the shock on the vibration signal will be related to the identification accuracy of the shock interval. This study aims to identify some feasible vibration signal processing methods for the identification of outer ring spalling sizes of ball bearings based on a modified energy value. The method involves the influence of impact forces on the measured vibration characteristics. According to the simulation analysis, the mapping relationship between the vibration signals with different fault sizes and the modified energy value is obtained. Then, the size of the spalling failure size of the ball-bearing outer ring is determined. Compared to existing methods, the proposed method is less affected by impact forces. Simulation and experiment results have verified the accuracy of this fault size identification.