Multi-channel Vibration Signals Research Articles

Multichannel Vibration Signal Fusion Based on Rolling Bearings and MRST-Transformer Fault Diagnosis Model

Multichannel Vibration Signal Fusion Based on Rolling Bearings and MRST-Transformer Fault Diagnosis Model

Multitype chatter detection via multichannelinternal and external signals in robotic milling

Robot milling chatter monitoring is susceptible to interference from robot pose, feed direction, inertial excitation-dominated low-frequency flexible vibration (IELFFV), and spindle noise. Therefore, this paper proposes a multi-type chatter detection method utilizing multi-channel internal and external signals. First, the multichannel vibration signals and internal controller signals were acquired and aligned. The vibration signals were converted into an engagement coordinate system. Then, based on the milling vibration signal characterization and sensitivity analysis of vibration signals to chatter, a combination of three chatter indicators, calculated via filtered displacement, filtered acceleration, and internal signals, was utilized to detect structural mode-dominated low-frequency chatter, IELFFV, and tool mode-dominated high-frequency chatter. Finally, the effectiveness of the proposed method in detecting the multitype chatter was demonstrated via robot milling experiments. However, the process of the tool entering and exiting the workpiece could be misclassification as TMHFC, which would be addressed in subsequent research.

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

Monitoring and Early Warning for Hydroelectric Generating Sets based on Hotelling’s T2 and LSTM Model

A method of monitoring and early warning for hydroelectric generating sets based on Hotelling’s T-squared statistics(T2) and Long Short-Term Memory(LSTM) network is proposed. Multi-channel vibration and swing signals can be fused and predicted based on the given model. The monitoring and alerting function can also be implemented according to a threshold value of T2. First, the vibration and swing signals of multi-channels hydroelectric generating sets are obtained and fused based on Principal Component Analysis(PCA) to reduce the amount of data. Second, Hotelling’s T2 statistics under normal running state is calculated and taken as a warning threshold. Third, a LSTM model is established to predict future values of T2, and early warning for a hydroelectric generating set can be realized by use of the obtained warning threshold. The vibration and swing signals from 16 channels in a set are used to validate the effectiveness of the method. Finally, there is a more than 90% reduction in the amount of data and the efficiency is significantly improved. LSTM has a high accuracy in T2 prediction and realize the early warning for abnormal status.

Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate

This paper investigates a demanding and meaningful task of intelligent fault diagnosis, in which multi-sensors signals are fused for semi-supervised analysis with few labeled fault data. Exploring effective strategies to solve this task from an industrial or academic perspective remains a challenging and resource-consuming task. To this issue, this study develops a new low-rank tensor-based semi-supervised classifier, called graph embedded low-rank tensor learning machine (GE-LRTLM), which can effectively alleviate the foregoing difficulties and increase the diagnosis precision in engineering applications. First, multi-sensor and multi-channel vibration signals are converted into the pixel matrices that are stacked as the multi-sensor information fusion feature tensors, making the coupling relationship between multi-sensor signals remained and achieving a reasonable fusion of multi-source features. Additionally, an advanced tensor decomposition method, tensor nuclear norm (TNN), is introduced in GE-LRTLM model to obtain the low-rank structure information of each feature tensor, implementing the extraction of the most important feature and patterns from the tensor data while ensuring that the tensor data structure remains unchanged. Ultimately, the manifold regularization and tensor-based graph construction method are introduced to obtain potential label information from unlabeled samples, achieving better description of the geometry similarity and distribution of tensor data. Numerous semi-supervised experiments are conducted across multiple datasets, and the results demonstrate that the proposed method can achieve a classification accuracy of 97% even when the number of labeled samples is extremely limited. Simultaneously, it also verifies that the combination of the constructed labeled and unlabeled multi-sensor information fusion tensor samples can promote the improvement of model accuracy.

Cross-correlation-guided operational modal identification of bridge considering asynchronous signals

The accurate identification of modal parameters is crucial for bridge health monitoring. In practical scenarios, there is often an asynchronization in the multi-channel vibration signals obtained from wireless sensors or offline measurements without temporal data. This asynchronization can lead to significant bias in the mode shapes when using the classical operational modal identification method. To address this issue, a new cross-correlation (CC) coefficient-based time lag detection is proposed. This technique aims to determine whether the multi-channel signals are synchronous and to prepare the reliably synchronous signals for modal identification. Because this detection technique specifically focuses on detecting the time lag between single degree-of-freedom (DOF) asynchronous signals, a multivariate variational mode decomposition (VMD) technique is developed to handle the multiple DOF signals of a bridge. To ensure that the decomposed components are not distorted or mixed, the penalty parameter in the multivariate VMD is optimized by minimizing the proposed similarity index. The proposed time lag detection process is validated using numerical and actual bridge examples. The results demonstrate that the new CC coefficient-based method is more effective than the classical CC coefficient or phase-based method, regardless of the employed mode or the amount of time lag present.

Rolling bearing fault diagnosis based on information fusion and parallel lightweight convolutional network

With the development of technologies such as Internet of Things and big data, the realization of fusion and cross analysis of multi-sensor signals provides the possibility for comprehensive condition monitoring and intelligent fault diagnosis of rolling bearings. Considering the bottleneck that deep neural networks with complex parameters and a single convolution kernel style may cause high computational effort and information loss, an intelligent fault diagnosis method based on information fusion and parallel lightweight convolutional network is proposed. Firstly, a normalized pulse energy kurtosis weighted rule is constructed to enable the fusion of multi-channel vibration signals. What’s more, a novel lightweight convolutional neural network is designed to achieve feature extraction and classification. Finally, a novel nonlinear piecewise activation function is introduced to further improve nonlinear learning ability. The effectiveness and superiority are verified by bearing data sets with different speeds and loads. Compared with other models, our diagnostic framework has better effect and provides a new idea for intelligent fault diagnosis.

Combine Harvester Bearing Fault-Diagnosis Method Based on SDAE-RCmvMSE.

In the fault monitoring of rolling bearings, there is always loud noise, leading to poor signal stationariness. How to accurately and efficiently identify the fault type of rolling bearings is a challenge. Based on multivariate multiscale sample entropy (mvMSE), this paper introduces the refined composite mvMSE (RCmvMSE) into the fault extraction of the rolling bearing. A rolling bearing fault-diagnosis method based on stacked auto encoder and RCmvMSE (SDAE-RCmvMSE) is proposed. In the actual environment, the fault-diagnosis method use the multichannel vibration signals of the bearing as the input of stacked denoising autoencoders (SDAEs) to filter the noise of the vibration signals. The features of denoise signals are extracted by RCmvMSE and the rolling bearing operation-state diagnosis is completed with a support-vector machine (SVM) model. The results show that in the original test data, the accuracy rates of SDAE-RCmvMSE, RCmvMSE, and commonplace features of vibration signals combined with SVM (CFVS-SVM) methods are 99.5%, 100%, and 96% respectively. In the data with noise, the accuracy rates of RCmvMSE and CFVS-SVM are 97.75% and 93.08%, respectively, but the accuracy of SDAE-RCmvMSE is still 100%.

A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network

With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time classification of multichannel, multidimensional sensor signals is increasing. In view of the need for intelligent methods for fault diagnosis methods of mechanical equipment, the generalization ability of fault diagnosis models also needs to be further strengthened. In this context, in order to make fault diagnosis intelligent and efficient, a bearing fault diagnosis method based on spectrum map information fusion and convolutional neural network (CNN) is proposed. First, short-time Fourier transform (STFT) is used to analyze the multichannel vibration signal of the rolling bearing and obtain the frequency domain information of the signal over a period of time. Second, the information fusion is converted into two-dimensional (2D) images, which are input into CNN for training, and the bearing fault identification model is obtained. Next, the frequency domain information of each signal is converted into a 2D spectrum map, which is used as a CNN training dataset to train a bearing fault identification model. Finally, the diagnostic model is validated using the existing datasets. The results show that the accuracy of fault diagnosis using the proposed bearing is greater than 99.4% and can even reach 100%. The proposed method considerably reduces the workload of the diagnosis process, with strong robustness and generalization ability.

Intelligent fault diagnosis of rotating machinery using composite multivariate-based multi-scale symbolic dynamic entropy with multi-source monitoring data

Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of modern industrial systems, which generally requires collaborative fault diagnosis by features extracted from multiple sensors since the multi-channel vibration signals carry a wealth of fault information. However, there is a remaining obstacle for fault diagnosis of multi-source monitoring data: integration of multisensory data. Hence, a novel framework is proposed for fault diagnosis of multi-source monitoring data. First, composite multivariate multi-scale symbolic dynamic entropy is proposed to extract fault features. Second, Laplacian score is introduced to select the distinguishing features with better clustering ability. Finally, the selected features are fed into a logistic regression classifier so that various faults of machinery are diagnosed. The simulation and two case studies using gearbox and pump data are performed to validate and demonstrate the superiority of the proposed method.

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.

Variational Embedding Multiscale Diversity Entropy for Fault Diagnosis of Large-Scale Machinery

The large-scale machinery generally requires multiple accelerometers for condition monitoring. The multichannel vibration signals carry a wealth of fault information. Synchronous fault feature extraction using multichannel signals will significantly improve the diagnostic performance. The multivariate entropy method is able to synchronously extract the fault features from multiple sensors, but how to recognize the single fault occurring on different channels remains unexplored. In this article, we propose a novel feature extraction method called variational embedding multiscale diversity entropy, which constructs the phase space with different structures. The proposed variational phase space construction strategy will generate a different probability distribution for each channel, which results in a better separability for multichannel feature extraction. Combined with the random forest classifier, a novel fault diagnosis scheme is developed for condition monitoring of the large-scale machinery. One simulated signal and two experimental data are designed to validate the effectiveness of the proposed strategy. The results demonstrate that the proposed method has the best multichannel feature extraction ability compared with three existing methods: multivariate multiscale sample entropy, multivariate multiscale fuzzy entropy, and multivariate multiscale permutation entropy.

Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals

Fault detection and diagnosis in time series data are becoming mainstream in most industrial applications since the increase of monitoring sensors in machinery. Traditional methods generally require pre-processing techniques before training; however, this task becomes very time-consuming with multiple sensors. Recently, deep learning methods have shown great results on time series data. This paper proposes a multi-head 1D Convolution Neural Network (1D CNN) to detect and diagnose six different types of faults in an electric motor using two accelerometers measuring in two different directions. This architecture was chosen due to each head can deal with each sensor individually, increasing feature extraction. The proposed method is verified through a series of experiments with seven different induced faults and operation conditions. The results show that the proposed architecture is very accurate for multi-sensor fault detection using vibration time series. Since the experiments are based on real electric motors and faults, these results are promising in real applications.

Maximum margin Riemannian manifold-based hyperdisk for fault diagnosis of roller bearing with multi-channel fusion covariance matrix

For rotating machinery, the sudden failure of roller bearing would lead to the downtime of the whole system and even catastrophic accidents. Therefore, multiple accelerometers are usually arranged to comprehensively evaluate the health of roller bearing, enhancing the stability and reliability of monitoring results. This paper proposes a novel fault diagnosis framework by utilizing a multi-channel fusion covariance matrix (MFCM) and Riemannian manifold-based hyperdisk. First, 22 statistical features are acquired from each channel data. Then, MFCM is calculated as the fault feature representation of roller bearing to achieve multi-channel feature fusion, where the element of MFCM represents the correlation information between different channels. Finally, since MFCM is a symmetric positive definite (SPD) matrix, lying on a Riemannian manifold, we design a maximum margin Riemannian manifold-based hyperdisk (MMRMHD) classifier to conduct fault classification, where Log-Euclidean metric (LEM) is introduced to calibrate the distribution of MFCMs. Moreover, to further improve the classification ability of nonlinear SPD data, we map MFCMs into a high-dimensional Hilbert space with the LEM-based kernel function and construct a novel kernelized MMRMHD model. The experimental results on two bearing datasets with multi-channel vibration signals demonstrate the effectiveness and superiority of the proposed fault diagnosis framework.

A sparse multivariate time series model-based fault detection method for gearboxes under variable speed condition

Gearboxes often operate under variable speed condition which makes the collected vibration signal, a widely employed type of condition monitoring data, becomes non-stationary. This paper proposes a sparse linear parameter varying vector auto-regression (LPV-VAR) model-based method for fault detection of gearboxes under variable speed condition. The proposed sparse LPV-VAR model is a multivariate time-variant time series model that can represent multichannel non-stationary baseline vibration signals from a gearbox. Fault detection is based on the residuals of the sparse LPV-VAR model. The proposed sparse LPV-VAR model inherits the strengths of the sparse time series modeling and utilization of multichannel vibration signals, where the former has shown to have higher modeling accuracy than conventional non-sparse time series models, and the latter enables the removal of the correlated random noise between channels. Both simulation and experimental studies have been conducted to validate the fault detection performance of the proposed method. Results have shown that the sparse LPV-VAR model has higher modeling accuracy than the reported sparse single-variate LPV-AR and conventional non-sparse LPV-VAR models. Subsequently, the sparse LPV-VAR model-based fault detection method achieves a higher fault detection rate than using the other two models.

An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

Multivariate hierarchical multiscale fluctuation dispersion entropy: Applications to fault diagnosis of rotating machinery

With a view to completing the fault diagnosis of rotating machinery efficiently and accurately, this paper presents a novel fault diagnosis model that combines multivariate hierarchical multiscale fluctuation dispersion entropy (MHMFDE), multi-cluster feature selection (MCFS) and gray wolf optimization-based kernel extreme learning machine(GWO-KELM). Firstly, MHMFDE is presented to capture the high-dimensional fault features hidden in the attained multichannel vibration signals. Integrating the multiscale entropy-based method and the hierarchical entropy-based method that are currently popular in the domain of fault identification, MHMFDE can simultaneously extract affluent fault features from multivariate vibration signals in-depth as well as overcoming the problem of information loss in the existing single-channel data analysis methods. Afterward, MCFS is used to pick sensitive features from the attained raw fault features to form the sensitive feature vectors, thereby reducing the impact of redundant features. Finally, GWO-KELM is adopted to quantitatively analyze the diagnostic effect. Three examples reveal that the presented approach enjoys excellent performance in the fault diagnosis domain. Especially for the identification of compound faults of rotating machinery, the performance of the presented method is significantly superior to that of existing methods.

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.

Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy

As a main source of power, diesel engines are widely used in large mechanical systems. Fire failure is a kind of common fault condition, which seriously affects the power and economy of the diesel engine. Previously, scholars mostly used single-channel signal to diagnose the misfire fault of the diesel engine. However, the single-channel signal has limitations in reflecting the information of fault. A novel fault diagnosis method based on MEMD and dispersion entropy is proposed in this paper. Firstly, the multichannel vibration signal of the diesel engine cylinder head is decomposed by multivariate empirical mode decomposition (MEMD), which obtains the IMF component groups with the same frequency in the same order. Then, the IMF component with a large correlation coefficient with the original signal in each group is selected to reconstruct new signal, and dispersion entropy (DE) of the reconstructed signal is calculated as a fault feature vector. Finally, the fault feature vector is input into the support vector machine (SVM) for misfire fault classification. Compared with the other three methods, the results show that the diagnosis method proposed in this paper can effectively extract the fault features and accurately identify the fault type, which is superior to the comparison method.

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.