Vibration Signal Denoising Research Articles

Method and Application of Spillway Radial Gate Vibration Signal Denoising on Multiverse Optimization Algorithm-Optimized Variational Mode Decomposition Combined with Wavelet Threshold Denoising

To address the noise issue in the measured vibration signals of spillway radial gate discharge, this paper utilizes the Multiverse Optimization Algorithm (MVO) to optimize the number of decomposition modes (K) and the penalty factor (α) in Variational Mode Decomposition (VMD). This approach ensures improved efficiency of VMD decomposition while maintaining accuracy. Subsequently, the obtained Intrinsic Mode Functions (IMFs) from VMD decomposition are classified based on Multi-scale Permutation Entropy (MPE). IMFs are divided into pure components and noisy components; the noisy components are processed with Wavelet Threshold Denoising (WTD), while the pure components are overlaid and reconstructed to obtain the denoised vibration signal of the gate. Comprehensive comparisons involving artificial signal simulations, gate flow-induced vibration model tests, and numerical simulations lead to the following conclusions: compared to other algorithms, the proposed combined denoising method (MVO-VMD-MPE-WTD) achieves the highest signal-to-noise ratio (SNR) in both the frequency and time domains for artificial signals, while yielding the lowest mean square error (MSE). In the gate flow-induced vibration model tests, the method significantly reduces noise in the vibration signals and effectively preserves characteristic information. The error in preserving characteristic information across model tests and numerical simulations is kept below 1%. Furthermore, compared to other optimization algorithms, the MVO demonstrates higher computational efficiency. The parameter-optimized combined denoising method proposed in this study provides insights into denoising measured vibration signals of hydraulic spillway radial gates and other drainage structures, and it opens possibilities for exploring more efficient optimization algorithms for achieving online monitoring in the future.

An Improved Rolling Bearing Fault Diagnosis Model of Long Short-Term Memory Network Based on VMD Denoised Vibration Signals

Data driven fault diagnosis methods are increasingly being used for condition monitoring of rotating machinery in the era of Industry 4.0. The effectiveness of Variational Mode Decomposition (VMD) denoised vibration signals in improving the Long Short-Term Memory (LSTM) method for the intelligent fault diagnosis of a rolling bearing is reported. The raw and VMD denoised vibration signals of rolling bearings are provided as inputs to the LSTM network for classification of bearing condition. The efficacy of the methodology to extract the fault information is assessed through datasets obtained from experiment test rig and through the open-source dataset of Case Western Reserve University (CWRU). A comparative analysis is also carried out using both VMD based decomposition and denoising techniques along with four machine learning classifiers viz. Decision Tree, k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) by using the statistical features of the VMD modes. Among the different methods evaluated, VMD denoised signals when fed to LSTM result in the maximum classification accuracy of 99.14 % with experimental dataset. For the case of CWRU dataset, VMD denoised signals as input to the SVM resulted in maximum classification accuracy of 98.16%.

Surface roughness prediction in turning processes using CEEMD-based vibration signal denoising and LSTM networks

Surface roughness prediction in turning processes using CEEMD-based vibration signal denoising and LSTM networks

Rolling Bearing Fault Diagnosis Based on MResNet-LSTM

In response to the challenges of weak fault characteristics and complex and variable working conditions of roll-ing bearings in strong noise environments, a diagnostic methodology is proposed. This method integrates a multi-scale residual neural network (MResNet) and a long short-term memory (LSTM) neural network to achieve fault diagnosis of bearings under both constant and variable rotational speed working conditions. First, complete an ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that is applied for vibration signal denoising. Secondly, a dropout layer is introduced between multi-scale residual blocks to prevent net-work overfitting, and the powerful time series information capture ability of LSTM is combined to improve di-agnostic accuracy. Finally, experimental verification is conducted using constant and variable speed bearing data sets. The results show that the proposed approach maintains strong diagnostic capabilities when the rotat-ing speed conditions change, and the signal is heavily polluted by noise.

Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model

Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.

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.

An innovative approach to vibration signal denoising and fault diagnosis using attention-enriched joint learning

Vibration signals play a crucial role in mechanical fault diagnosis. However, they are susceptible to various noise disturbances, presenting challenges for reliable fault detection. We propose an end-to-end Cross-task Attention Joint Learning (CTA-JL) model that concurrently denoises and diagnoses faults in noisy signals. This model utilizes a multi-task encoder, composed of task-shared and task-specific feature encoding units, along with a feature information exchange unit with a Cross-task Attention (CTA) mechanism, fostering information exchange across different tasks. By collectively executing diagnosis and denoising tasks and sharing valuable task information, the model enhances prediction accuracy and denoising performance. Under three noise conditions of SNR = −9 dB, −6 dB, and −3 dB, the prediction accuracy of CTA-JL on the rolling bearing datasets reached 91.38%, 97.95%, and 99.69%, respectively. Meanwhile, the result on elevator guide system datasets reached 87.31%, 95.58%, and 99.64%

Elevator vibration signal denoising by deep residual U-Net

Vibration signals from elevators contain critical information pertinent to condition monitoring and fault diagnosis. However, the presence of noise in real-world data acquisition environments invariably contaminates these signals, thus compromising the effectiveness of condition monitoring and fault diagnosis. This study proposes a novel denoising method based on deep residual U-Net to mitigate noise in the vertical vibration signals of elevator cabins. The proposed network is a convolutional neural network with skip connection and multi-scale convolution structure, which can automatically learn the potential mapping between noisy and clean signals. The robustness and effectiveness are verified through experiments using real-world vibration signals compared with three conventional denoising methods in both linear and non-linear systematic indicators. Moreover, the proposed method exhibits higher accuracy and promising prospects in practical applications when applied to elevator travel distance monitoring.

Hob vibration signal denoising and effective features enhancing using improved complete ensemble empirical mode decomposition with adaptive noise and fuzzy rough sets

The acquired hob vibration signals are inevitably contaminated by noise in the industrial environment, which changes the vibration signal frequency distribution and reduces the accuracy of feature extraction and hob wear identification. To solve this problem, a novel hob vibration signal denoising and effective feature enhancement method, CEEMDAN-FRS, is proposed based on the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy rough sets (FRS). First, the effective frequency distributions of gear hobbing, particularly the modulation sidebands, were obtained as prior knowledge by analyzing the hob vibration response mechanism. Then, the two key parameters of CEEMDAN (i.e., noise standard deviation and ensemble size) were adaptively determined based on signal characteristics to achieve improved decomposition compared with the use of fixed values. The evaluation and selection of intrinsic mode functions (IMFs) based on a single feature such as kurtosis or root mean square, only focus on the partial characteristics, leading to an identification bias. Thus, 11 features with different sensitivities of IMFs are weighted based on FRS, and fused as a unified feature to conduct a comprehensive IMF evaluation. Finally, a reweighted IMF reconstruction strategy is proposed. The comparisons of the proposed method and related approaches to hob vibration signals show that the proposed method achieves better performance in terms of effective feature enhancement, noise removal, and signal-to-noise ratio improvement

Leakage identification for mineral air supply pipeline system based on joint noise reduction and ELM

This work presents a leakage identification framework for the coal mine air supply pipeline system based on joint noise reduction and extreme learning machine (ELM). First, leakage vibration signals from a pipeline system are sampled. Then, vibration signals are decomposed by parameter-optimized variational mode decomposition (POVMD), where the mode number is determined via the energy difference principle, and the separation point of low- and high-frequency modes are identified by mutual information entropy. Next, the wavelet denoising method based on an improved threshold function is implemented on high-frequency modes. Then denoised vibration signals are reconstructed by denoised high-frequency modes and low-frequency modes. Subsequently, multi-scale entropy values are extracted and fed into ELM for training and testing. In the laboratory environment, the ELM model with 132 hidden nodes and the Sigmoid function shows the highest testing accuracy (94.17%), indicating that the proposed method is more practical and effective than other methods.

An Integrated Method Based on Convolutional Neural Networks and Data Fusion for Assembled Structure State Recognition

This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition.

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

The noise from other sources is inevitably mixed in the vibration information of CNC machine tools obtained using the sensors. In this work, a de-noising method based on joint analysis is proposed. The variational mode decomposition (VMD), correlation analysis (CA), and wavelet threshold (WT) denoising are used to denoise the original signal. First, VMD decomposes noisy signals into multiple intrinsic mode functions (IMFs). The penalty factor and decomposition level of VMD parameters are selected by the optimization algorithm by combining the whale optimization algorithm (WOA) and tabu search (TS). The minimum permutation entropy of IMF is used as the fitness function of the proposed fusion algorithm. Then, the IMF is divided into three categories by using the cross-correlation number. They include the pure components, signals containing noise, and complete noise components. Then, the WT method is used to further denoise the signals, and signal reconstruction is performed with the pure component to obtain the denoised signal. This joint analysis denoising method is named TS-WOA-VMD-CA-WT. The simulation results show that the fusion optimization algorithm proposed in this work has better performance as compared to the single optimization algorithm. It performs effectively when applied to the actual machine tool vibration signal denoising. Therefore, the proposed TS-WOA-VMD-CA-WT method is superior to other existing denoising techniques and has good generality, which is expected to be popularized and applied more widely.

Influence of noise on wear fault diagnosis based on recurrence plot

Recurrence plot (RP) and recurrence quantification analysis (RQA) have been widely applied in fault diagnosis of tribo-systems. The influence of noise on the RP, RQA quantifiers and RP-based wear fault diagnosis was studied by analyzing theoretical signals and vibration signals. With the decrease of signal-to-noise ratio (SNR), the vertical and diagonal structures of RP of noisy Lorenz and Rössler signals are discretized into isolated points, and RQA quantifiers decrease gradually. This effect is due to the interference of noise on the phase trajectory of original signals. Noise makes some phase points escape the hypersphere, which leads to the disappearance of recurrence relationship. Recurrence plot-convolutional neural network (RP-CNN) can diagnose wear forms with 99.2% accuracy by analyzing the denoised vibration signals, which is 2.2% higher than that without denoising. The results will help to improve the reliability and accuracy of recurrence analysis applied in fault diagnosis of tribo-systems.

A Combination of Dilated Self-Attention Capsule Networks and Bidirectional Long- and Short-Term Memory Networks for Vibration Signal Denoising

As scalar neurons of traditional neural networks promote dimension reduction caused by pooling, it is a difficult task to extract the high-dimensional spatial features and long-term correlation of pure signals from the noisy vibration signal. To address the above issues, a vibration signal denoising method based on the combination of a dilated self-attention capsule network and bidirectional long short memory network (DACapsNet–BiLSTM) is proposed to extract high-dimensional spatial features and learn long-term correlations between two adjacent time steps. An improved self-attention module with spatial feature extraction ability was constructed based on the random distribution of noise, which is embedded into the capsule network for the extracted spatial features and denoising. The dilated convolution is integrated into the improved capsule network to expand the receptive field to obtain the spatial features of the vibration signal. The output of the capsule network was used as the input of the bidirectional long-term and short-term memory network to obtain the timing characteristics of the vibration signal. Numerical experiments demonstrated that DACapsNet–BiLSTM performs better than other signal denoising methods, in terms of signal-to-noise ratio, mean square error, and mean absolute error metrics.

Deep transfer learning-based vehicle classification by asphalt pavement vibration

Deep transfer learning (TL) has great potential for a wide range of applications in civil engineering. This work aims to propose a deep transfer learning-based method for vehicle classification by asphalt pavement vibration. This work first used the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. The vibration signals were then represented in two different ways, including the time-domain graph and the time-frequency graph. Finally, two deep transfer learning-based methods, namely Method Ⅰ (Time-domain & TL) and Method Ⅱ (Time-frequency & TL), were applied for vehicle classification according to the two different representations of vibration signals. The results show that the CNN model had a satisfactory performance in both methods with the accuracy of Method Ⅰ exceeding 0.94 and Method Ⅱ exceeding 0.95. The CNN model in Method Ⅱ performed better in the accuracy metrics with considering label imbalance, but worse in the accuracy metrics without considering label imbalance than that in Method Ⅰ. The differences between these two methods have been investigated and discussed in detail in terms of input types, accuracy metrics, and application prospects. The CNN model with deep transfer learning could be an effective, accurate, and reliable technique for vehicle classification based on asphalt pavement vibration.

Geotechnical engineering blasting: a new modal aliasing cancellation methodology of vibration signal de-noising

Geotechnical engineering blasting: a new modal aliasing cancellation methodology of vibration signal de-noising

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

As a high-precision gravity measuring device, a marine atomic gravimeter is highly sensitive to vibration signals. Accurate measurement and analysis of vibration signal is the primary condition to realize vibration compensation and vibration suppression. Denoising plays a crucial role in the processing of these vibration signals. The vibration signals of a marine gravimeter contain numerous nonlinear and nonstationary components. In this paper, a vibration signal denoising method of marine atomic gravimeter based on improved variational mode decomposition (VMD) was put forward to effectively suppress the noise. An improved genetic particle swarm optimization (GPSO) was first adopted for the parametric optimization of VMD by taking minimum permutation entropy (PE) as fitness function and adaptively determining the optimal parameters of VMD. PE was then utilized to calculate the proportion of noise-containing components in the intrinsic mode function (IMF) components obtained by VMD. The components were classified into noise and signal components by searching for the mutation points of two adjacent IMF permutation entropies. On this basis, noise components were denoised by Savitzky-Golay (SG) filter. In the end, the denoised components were reconstructed with the signal components to generate denoised vibration signals. To verify the effectiveness, the proposed method was applied in denoising, simulated and measured vibration signals of a marine atomic gravimeter, and compared with Daubechies (db) wavelet, Symlets (sym) wavelet, and empirical mode decomposition (EMD). The results showed that the proposed method could effectively remove the noise from nonlinear vibration signals and retain the authentic and useful information, so that it was able to provide the supporting data for gravity compensation of marine atomic gravimeter.

Improved Empirical Modal Decomposition Coupled with Interwoven Fourier Decomposition for Building Vibration Signal Denoising

The precise detection of building vibration signals is a crucial problem for the identification of building vibration sources and characteristics. However, the building vibration signal is usually accompanied by complex high-frequency noise. The present study proposed a novel building vibration signal denoising method based on improved empirical modal decomposition coupled with interwoven Fourier decomposition (IEMD-IWFD). The noise-embed building vibration signal is first decomposed by the IEMD-IWFD. Then, the intrinsic mode function (IMF) components with useful information are extracted from the original building vibration signal using the energy criterion of the autocorrelation function. After that, the building vibration signal is formed by reconstructing the IMF component using the Hilbert transform. Based on the comparison of similarity coefficient and mean square error between the reconstructed signal from IEMD-IWFDM and EMD and target signal, it is indicated that the IEMD-IWFDM exhibits a better denoising performance for the simulated building vibration signal induced by trains.

A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis

In this paper, a novel signal denoising method based on Stationary Wavelet Transform and Particle Swarm Optimization Algorithm (SWT-PSO) is proposed. The threshold values for wavelet denoising are selected using the meta-heuristic algorithm of particle swarm optimization such that the denoised signal has maximum weighted kurtosis. In many faulty rotating machinery elements such as rolling element bearings and gears, impact-type vibration signals are produced. Such impact type vibration signals have a high value of kurtosis. The proposed SWT-PSO scheme can be used for the enhancement of fault signature in rotating machinery by increasing the kurtosis of such signals. The proposed algorithm has been applied on simulated as well as experimental defective rolling element bearing signal. Vibration signals from rolling element bearing with inner race and outer race defects are acquired by accelerometers on a Machinery Fault Simulator (MFS) setup. Then the proposed methodology is applied successfully to denoise the signals at various noise levels. Further, Spectrum envelope of the denoised vibration signal is used to obtain the characteristic defect frequencies. The developed methodology is finally compared with widely used Minimum Entropy Deconvolution (MED) algorithm. It has been demonstrated that the proposed algorithm has better capability of extracting the impulsive characteristic as well as preserving the signal geometrical structure than compared to MED.

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.