Single-channel Vibration Signal Research Articles

Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions

Abstract Bearings, as the core component for power transmission, are crucial in ensuring the safe and reliable operation of equipment. However, the fault information contained in a single-channel vibration signal is inherently limited. Additionally, under time-varying speed conditions, features are prone to drift, and the cross-domain diagnostic performance of most traditional domain adaptation (DA) models may drop dramatically. To solve the above problems and enhance the ability of DA models in extracting domain invariant features, this paper introduces a Multi-channel data fusion and Attention-guided Multi-feature Fusion-driven Center-aligned Network (MAMC). Initially, a multi-channel time-frequency information fusion strategy based on wavelet transform is constructed to achieve a comprehensive fusion of multi-channel data, thereby obtaining richer fault feature representations. Subsequently, a multi-branch feature fusion network, integrated with an attention mechanism, is devised to capture significant features across various dimensions and scales, resulting in more comprehensive and representative fault features. Finally, a novel Center-Aligned Domain Adaptation method (CADA) is proposed based on domain adversarial methods and center loss. By minimizing the distance between deep domain invariant features and trainable common class centers, the issue of domain shift between data is effectively alleviated, and the cross-domain diagnostic performance of DA models under the time-varying speed conditions is improved. The experimental results indicate that the MAMC method exhibits superior performance on both bearing datasets and is a promising approach for cross-domain intelligent fault diagnosis.

Operating modal parameter identification of railway vehicle bogie based on k-value optimization variational mode decomposition and second-order blind identification method

Due to the variable operating conditions and strong background noise, the accuracy of modal parameter identification of the railway vehicle bogie is low. This paper proposes a method for identifying operational modal parameters by combining the K-value optimization variational mode decomposition (KVMD) and second-order blind identification (SOBI) method (KVMD–SOBI method). Firstly, the variational mode decomposition (VMD) algorithm is used to adaptively decompose a single-channel vibration signal into intrinsic mode function (IMF) components with different scale characteristics. The feature IMF components are selected through the K-value optimization method based on energy ratio and stability graph. Then, the SOBI algorithm is used to separate the matrix composed of the feature IMF components. And the mode shape and single-mode signal are obtained. Finally, the natural frequency and damping ratio of each order mode are extracted by the single-mode parameter recognition method. Through simulation and experimental verification, it is shown that the proposed method has good noise robustness and can effectively identify the first three modal parameters of railway vehicle bogies in variable-speed operation. At the same time, the feature IMF components extracted by KVMD algorithm eliminate the sensitivity of sensor installation positions and operating conditions to detection results. Therefore, this method is an excellent operational mode identification method based on a single-channel signal with good noise and working condition robustness.

Fault diagnosis for rotor based on multi-sensor information and progressive strategies

Fault diagnosis is an effective tool to ensure safe operation of machinery and avoid serious accidents. As most currently used fault diagnosis methods usually employ mapping relationship established by training samples and their labels to achieve classification of testing samples, it is difficult for them to achieve fault diagnosis under the condition of incomplete training sample types. In addition, previous studies usually focus on feature extraction of single-channel vibration signal, which cannot get complete fault feature information. To solve the above problems, a progressive fault diagnosis method is investigated in this paper. First, the preliminary fault detection for the rotor is performed by studying reconstruction error of a sparse auto-encoder. Second, if a fault exists in the rotor, the outlier detection is implemented by the support vector data description method. Finally, if there are no outlier samples, the well -trained support vector machine is used to confirm the type of fault samples and complete the diagnosis. The performance of the proposed method was verified using the data obtained from a rotor laboratory bench. The types of rotor states investigated include normal, contact-rubbing, unbalance and misalignment. The experimental results verify the effectiveness and superiority of the proposed method in reducing the incidents of fault omission and fault misunderstanding.

Multivariate complex modulation model decomposition and its application to gear fault diagnosis

The single-channel vibration signal of gears contains limited information, and it is easily affected by the transmission path. Therefore, multi-channel signals should be used for fault diagnosis because multi-channel gear signals usually contain richer and more comprehensive information about the equipment status. But the existing single-channel signal processing methods are not applicable to multi-channel signals, and each of the existing multi-channel signal processing methods has its own limitations. Therefore, this study proposes a new multi-channel signal processing method called multivariate complex modulation model decomposition (MCMMD), by which multi-channel signals can be decomposed accurately and adaptively at the same time. The core of the method is to iteratively update the model parameters by combining all channel signals to acquire the pattern alignment property. The decomposition performance is analyzed first. Then MCMMD is applied to simulation and experimental multi-channel gear fault signals. For comparison, ensemble empirical mode decomposition (EEMD), multivariate empirical mode decomposition (MEMD), multivariate variational mode decomposition (MVMD), multivariate local characteristic-scale decomposition (MLCD), and completely adaptive projection MLCD (CAPMLCD) are presented as well. The results show that the decomposition accuracy and robustness of MCMMD are better than those of the other methods compared. Therefore, MCMMD is an accurate and effective multi-channel signal processing method.

Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP

In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster–Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results.

A novel multi-channel weighted convolutional neural network for fault identification of high-voltage circuit breakers

To solve the problem that single-channel vibration signal input cannot fully express fault feature information and early diagnosis of high-voltage circuit breaker (HVCB) faults, a multi-channel weighted convolutional neural network (MCW-CNN) is proposed and applied to HVCB vibration signal characteristic learning and fault diagnosis. Firstly, wavelet transform is used to extract the time-frequency features of the vibration signal. The vibration signal is converted into multi-channel image input, so as to give full play to the excellent performance of CNN in image feature extraction. Secondly, according to the difference in image frequency and bandwidth of each channel, the dynamic receptive field is used in the convolution layer to extract image features. Aiming at the difference in the intensity of shock features carried by each channel image, a multi-channel fusion method based on kurtosis weighting is proposed to enhance the channel fault features with strong shock features. Finally, the effectiveness of the proposed method is verified by fault diagnosis test of HVCB. Through the fault simulation experiment, the results verify that the MCW-CNN can effectively extract the fault features of the vibration signal, and the recognition accuracy is significantly higher than that of the traditional method.

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

Deep learning based on vibration signal image representation has proven to be effective for the intelligent fault diagnosis of bearings. However, previous studies have focused primarily on dealing with single-channel vibration signal processing, which cannot guarantee the integrity of fault feature information. To obtain more abundant fault feature information, this paper proposes a multivariate vibration data image representation method, named the multivariate symmetrized dot pattern (M-SDP), by combining multivariate variational mode decomposition (MVMD) with symmetrized dot pattern (SDP). In M-SDP, the vibration signals of multiple sensors are simultaneously decomposed by MVMD to obtain the dominant subcomponents with physical meanings. Subsequently, the dominant subcomponents are mapped to different angles of the SDP image to generate the M-SDP image. Finally, the parameters of M-SDP are automatically determined based on the normalized cross-correlation coefficient (NCC) to maximize the difference between different bearing states. Moreover, to improve the diagnosis accuracy and model generalization performance, this paper introduces the local-to-global (LG) attention block and locally enhanced positional encoding (LePE) mechanism into a Swin Transformer to propose the LEG Transformer method. Then, a novel intelligent bearing fault diagnosis method based on M-SDP and the LEG Transformer is developed. The proposed method is validated with two experimental datasets and compared with some other methods. The experimental results indicate that the M-SDP method has improved diagnostic accuracy and stability compared with the original SDP, and the proposed LEG Transformer outperforms the typical Swin Transformer in recognition rate and convergence speed.

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis.

A new method of multi-sensor signal analysis for fault diagnosis of centrifugal pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) is proposed. The single-channel vibration signal is analyzed by Continuous Wavelet Transform (CWT) to construct the time–frequency representation. The multiple time–frequency data are used to construct the three-dimension data matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to obtain the six features, which are the time domain signal (mode 3) and frequency domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen features from three direction vibration signals are used to test the data processing capability of the algorithm models by the comparison among the CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The results show that the multi-channel three-level data decomposition with PARAFAC has better performance than WPT. The improved particle swarm optimization (IPSO) has a great improvement in the complexity of the optimization structure and running time compared to the conventional particle swarm optimization (PSO.) It verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid algorithm. Further, it is characteristic of its robust and reliable superiority to process the multiple sources of big data in continuous condition monitoring in the large-scale mechanical system.

Rotating Machinery Fault Diagnosis Using HHO-RSSD and RCGmvMAAPE

Rotating machinery has played an enormous role in industrial production, and its stable operation is related to whether production can proceed smoothly. At present, multichannel entropy-based methods are usually be adopted to analyze multichannel vibration signals. However, the collected signal may only have one channel in the actual situation. At this time, analyzing only a single channel signal cannot effectively utilize the advantages of multivariate analysis. For this reason, this paper presents a novel multivariate analysis approach and applies it to the fault diagnosis of machinery. Firstly, the parameter-optimized resonance sparse decomposition (RSSD) algorithm is adopted to decompose the single-channel vibration signal into high and low resonance components. Then, the two components are regarded as dual-channel vibration signals and input into the refined composite generalized multivariate multiscale amplitude aware permutation entropy (RCGmvMAAPE) method to gain fault features. Eventually, the features are input to the deep belief network (DBN) classifier to perform fault judgment. The experiments of rotating machinery are carried to verify the effectiveness of the developed approach. The results display that the proposed fault diagnosis method can achieve the classification accuracy of 100% and 98% when only a single-channel vibration signal is used, which is better than the fault diagnosis method based on a multichannel vibration signal and enjoys strong stability.

A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery.

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

Fault Diagnosis of a Reciprocating Compressor Air Valve Based on Deep Learning

With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.

A Novel Fault Diagnosis Method for Diesel Engine Based on MVMD and Band Energy

Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods.

Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain

Mechanical fault diagnosis under varying speed conditions has gradually become an important issue in rotating machinery monitoring, especially the research on compound fault diagnosis is still rare. Order analysis is one of the most effective and widely used methods to eliminate the effects of speed fluctuations; however, it is difficult to extract compound fault features directly. Moreover, there are often many restrictions on the number and locations of sensors to be installed in practice, which also brings great challenges to compound fault diagnosis under varying speed conditions. In this article, a novel compound fault detection method with virtual multichannel signals in the angle domain is proposed to solve these problems for rolling bearing monitoring under varying speed conditions. First, we aim to use only a vibration acceleration sensor and a noncontact rotational speed sensor to adapt to limitations on sensor installation in practical applications. However, this may result in an underdetermined problem when trying to detect a compound fault with only a single-channel vibration signal. Then we make it up through constructing virtual multichannel signals based on variational mode decomposition theory, before which the nonstationary vibration signal is converted from the time domain into a stationary signal in the angle domain with computed order tracking to eliminate speed fluctuations. Finally, these virtual signals can be easily taken as input to a normal independent component analysis, and different faults can be detected in the order spectrum of each independent component. Case studies on rolling bearing experiments verified the advantages of the proposed method in compound fault detection under varying speed conditions.

A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

A feature extraction method combined with variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed to improve the fault feature extraction of vibration signal of rotary machinery. Firstly, VMD is used to decompose the single-channel vibration signal. Secondly, calculate the correlation coefficient between each component and the original signal. Finally, a new multidimensional observation signal is formed with high correlation components, and the fault signals will be extracted from the new observation signal by KICA. Compared with some typical fault feature extraction methods, the better performance of the proposed method is demonstrated by two experiments which are faulty rolling bearing experiment and a comprehensive experiment with faulty rolling bearing and faulty gear. Furthermore, an experiment of faulty rotary shaft verifies the effectiveness of this method. The results demonstrate that the proposed method is efficient for fault feature extraction of single-channel vibration signal of rotary machinery.

A fault feature extraction method for rotating shaft with multiple weak faults based on underdetermined blind source signal

In order to improve the fault feature extraction of a rotating shaft, an efficient method of underdetermined blind source signal with weak faults based on Hankel matrix-based singular value decomposition (SVD) and blind source separation (BSS) is proposed. First, a Hankel matrix is constructed for single-channel vibration signal, and then SVD is used to estimate the number of fault signals. Finally, the fault features can be extracted by BSS. Compared with wavelets, variational mode decomposition and ensemble empirical mode decomposition + BSS, the better performance of the proposed method is demonstrated by an analysis of the simulated signal with the misalignment fault and imbalance fault mixed. Furthermore, an experiment verifies the effectiveness of this method. The result demonstrates that the proposed method is efficient for feature extraction of a single-channel vibration signal of a rotating shaft with multiple weak faults.

Research of multi-concurrent fault diagnosis of rotating machinery based on VMD and KICA

In order to improve the multi-concurrent fault diagnosis of rotating machinery, a feature extraction method based on variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed. First, use VMD to improve the dimension of single-channel vibration signal. Then, calculate the correlation coefficient between the signal of each dimension and the original signal. Finally, high correlation signals are used to form a new observation signal and the fault signals will be extracted by KICA. Compared with ensemble empirical mode decomposition (EEMD) + fast independent component analysis (FastICA), the better performance of the proposed method is demonstrated by an analysis of rolling bearing with the fault of inner ring and outer ring mixed. Furthermore, an experiment with the fault of outer ring of rolling bearing and gear breaking mixed verifies the effectiveness of this method. The result demonstrates that the proposed method is efficient for fault diagnosis of single-channel vibration signal of rotating machinery with multi-concurrent faults.

Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery

A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.

Nonnegative Matrix Factorization of time frequency representation of vibration signal for local damage detection – comparison of algorithms

Local damage detection in rotating machine elements is very important problem widely researched in the literature. One of the most common approaches is the vibration signal analysis. Since time domain processing is often insufficient, other representations are frequently favored. One of the most common one is time-frequency representation hence authors propose to separate internal processes occurring in the vibration signal by spectrogram matrix factorization. In order to achieve this, it is proposed to use the approach of Nonnegative Matrix Factorization (NMF). In this paper three NMF algorithms are tested using real and simulated data describing single-channel vibration signal acquired on damaged rolling bearing operating in drive pulley in belt conveyor driving station. Results are compared with filtration using Spectral Kurtosis, which is currently recognized as classical method for impulsive information extraction, to verify the validity of presented methodology.

Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

To solve the problem of multi-fault blind source separation (BSS) in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS) based on the improved tensor-based singular spectrum analysis (TSSA) is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC) weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition—singular value decomposition—Bayesian information criterion) method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals.

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.