Bearing Compound Fault Diagnosis Research Articles

Domain reinforcement feature adaptation methodology with correlation alignment for compound fault diagnosis of rolling bearing

In the long-term operation process, rolling bearings often have multiple single faults or cascade faults, which are coupled with each other to form the compound fault. The complex coupling components of compound fault lead to difficultly building a correlation between compound fault feature and fault class. Moreover, for the transfer learning based on domain adaptation, compound fault feature of source domain is pretty different from that of target domain, thus knowledge of source domain is hard to be transferred into the cross-domain unsupervised learning process of target domain. To solve above mentioned problems well, this paper proposes a domain reinforcement feature adaptation methodology with correlation alignment (CA-DRFA) to complete the cross-domain compound fault diagnosis of bearings. Specifically, a deep reinforcement learning model is improved by being combined with the domain adversarial training way, and meanwhile the correlation alignment metrics is adopted to enhance the generalization performance in feature alignment. In addition, a laboratory experiment and an engineering application are performed to demonstrate that CA-DRFA outperforms other popular cross-domain fault diagnosis methods in cross cutting condition and speed transfer tasks. Overall, CA-DRFA is able to implement cross-domain compound fault diagnosis with high accuracy, and effectively reduce the cost of labeling samples.

Bearing Compound Fault Diagnosis Using Energy-Constrained Swarm Decomposition and Adaptive Spectral Amplitude Modulation

Bearing Compound Fault Diagnosis Using Energy-Constrained Swarm Decomposition and Adaptive Spectral Amplitude Modulation

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

Research on Compound Fault Diagnosis of Bearings Using an Improved DRSN-GRU Dual-Channel Model

Research on Compound Fault Diagnosis of Bearings Using an Improved DRSN-GRU Dual-Channel Model

A Lightweight Bearing Compound Fault Diagnosis Method With Gram Angle Field and Ghost-ResNet Model

A Lightweight Bearing Compound Fault Diagnosis Method With Gram Angle Field and Ghost-ResNet Model

Adaptive Feature-Oriented Dictionary Learning and Sparse Classification Framework for Bearing Compound Fault Diagnosis

Adaptive Feature-Oriented Dictionary Learning and Sparse Classification Framework for Bearing Compound Fault Diagnosis

Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data

The effective fault feature extraction is the core of rolling bearing fault diagnosis. However, rolling bearings usually operate in normal state and fault duration is very short, which will cause imbalance in fault diagnosis data, thus leading to difficulty in fault feature extraction and low diagnosis accuracy. Meanwhile, mutual interference between multiple fault responses will also lead to poor diagnosis performance. To solve these issues, a novel compound fault diagnosis method with imbalanced data based on frequency-domain Gramian angular field (FGAF) and convolutional neural networks optimized by instance normalization and efficient channel attention (IECNN) is proposed. Firstly, FGAF is adopted to map frequency-domain features of fault signals to the polar coordinate to obtain 2D FGAF feature spectrum. Secondly, an instance normalization module is established to reduce internal covariant shift caused by data distribution discrepancy and improve generalization ability. An efficient channel attention module is constructed to further excavate fault features and improve anti-interference ability. Finally, experiments are conducted under imbalanced dataset and imbalance intensified dataset, and the average accuracy of 99.91% and 99.92% were obtained, respectively, which shows the proposed method has better resistance to data imbalance.

A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis.

This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability.

A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.

Periodic component pursuit-based kurtosis deconvolution and its application in roller bearing compound fault diagnosis

As an effective roller bearing fault diagnosis method, Adaptive Periodic Mode Decomposition (APMD) method has excellent capability of repeated transient extraction. In APMD, the Maximum Likelihood Estimation (MLE) method is used to calculate the projection energy, and the period corresponding to the maximum projection energy is taken as the main period of the present signal. When the noise is large, the noise energy will greatly interfere with the projection energy, thereby affecting the accuracy of the period estimation. To solve this problem, a Periodic Component Pursuit-based Kurtosis Deconvolution (PCPKD) method is proposed, which uses Maximum Reweighted Kurtosis Deconvolution (MRKD) to de-noise the signals that need to estimate the main periods, so as to reduce the influence of noise energy on the periodic estimation to the greatest extent. The proposed method is applied to roller bearing compound fault diagnosis, and the experimental results show that PCPKD has a good ability of signal period enhancement and can effectively segment multi frequency components.

Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings

Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.

A zero-shot fault semantics learning model for compound fault diagnosis

Compound fault diagnosis of bearings has always been a challenge, due to the occurrence of various faults with randomness and complexity. Existing deep learning-based methods require numerous compound fault samples for training. However, sufficient training samples for every type of compound fault are usually unavailable in realistic industrial scenarios. In this paper, we propose a zero-shot fault semantics learning model trained on single fault samples to identify unknown compound faults. First, we propose a convolutional autoencoder-based fault semantics construction method to generate the fault semantics for single and compound faults. Second, a CNN-based feature extractor is designed to extract fault features from time–frequency domain vibration signals. Then we design an autoencoder-based fault semantics embedding module to embed the compound fault semantic vectors into the fault feature space as the category centroids. Finally, by the similarity measurement between the compound fault features and the category centroids, the model is able to identify unknown compound faults. Extensive comparative results demonstrate that our method outperforms a series of state-of-the-art models, with the accuracy of 78.40% for three categories of compound faults in the case of 2000 single-fault training samples per category.

An Improved Sparse Representation Based on Local Orthogonal Matching Pursuit for Bearing Compound Fault Diagnosis

Convolutional sparse representation based on local orthogonal matching pursuit (SR-LocOMP) can recover wheelset-bearing fault impulses without being affected by random slippage and plays an important role in wheelset-bearing fault diagnosis. However, the performance of SR-LocOMP at a low signal-to-noise ratio (SNR) is not satisfied. In addition, when the analyzed signals contain compound faults, the fault feature extraction ability of SR-LocOMP will be greatly affected. To further improve the performance of SR-LocOMP, this article proposes an improved SR-LocOMP algorithm, named improved SR-LocOMP. The proposed method introduces scale-space representation (SSR) for frequency band segmentation and then performs SR-LocOMP for signals in each segmented frequency band. Reconstructed signals corresponding to each segmented frequency band are obtained separately. Envelope spectral <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L}2/{L}1$ </tex-math></inline-formula> norm is used to measure the fault information content of each reconstructed signal to extract more accurate and richer impulse response features caused by bearing faults. The proposed method not only has the ability to detect compound faults but also can effectively reduce noise interference. Through simulation and experimental analysis, the superiority of the proposed method is verified.

A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers.

In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults in the absence of compound fault training data. In an examination of the rotate vector (RV) reducer, a core component of industrial robots, this paper proposes a compound fault identification method that is based on an improved convolutional capsule network for compound fault diagnosis of RV reducers. First, one-dimensional convolutional neural networks are used as feature learners to deeply mine the feature information of a single fault from a one-dimensional time-domain signal. Then, a capsule network with a two-layer stack structure is designed and a dynamic routing algorithm is used to decouple and identify the single fault characteristics for compound faults to undertake the diagnosis of compound faults of RV reducers. The proposed method is verified on the RV reducer fault simulation experimental bench, the experimental results show that the method can not only diagnose a single fault, but it is also possible to diagnose the compound fault that is composed of two types of single faults through the learning of two types of single faults of the RV reducer when the training data of the compound faults of the RV reducer are missing. At the same time, the proposed method is used for compound fault diagnosis of bearings, and the experimental results confirm its applicability.

Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault Diagnosis.

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response.

Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis

The decoupling detection of compound faults in rolling bearing is attracting considerable attentions. In recent years, some time-series decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), symplectic geometry mode decomposition (SGMD) etc., are used to extract the fault characteristics of bearing fault vibration signal and achieve the purpose of fault diagnosis. However, the fault characteristics of compound fault vibration signals are unevenly distributed, some fault characteristics are weakly disturbed by noise, which limit these methods application in compound fault diagnosis. In this paper, the Koopman operator is introduced from the perspective of flow field dynamics information extraction, an adaptive dynamic mode decomposition (ADMD) is proposed to decompose the nonlinear time-series data into a set of dynamic mode components (DMCs). In ADMD, a high-degree polynomial is applied to fit the data sequence of flow field, and a low dimensional subspace can be obtained. The dominant dynamics of flow field can be represented by the eigenvalues and eigenvectors of the low-dimensional subspace, which can be transformed into reconstructed time series with different frequency, and obtained the frequency-based DMCs. The simulation and experimental analysis results show that the proposed method can effectively decouple different fault components of compound fault of rolling bearing.

Rolling Bearing Compound Fault Diagnosis Based on Parameter Optimization MCKD and Convolutional Neural Network

For the sake of solving the problem of the difficulty of extracting fault features under the background of noise and accurately identify the state of the bearing, a compound fault diagnosis method of rolling bearing based on parameter optimization maximum correlated kurtosis deconvolution (MCKD) and convolutional neural network (CNN) is proposed. First, the adaptive multi-strategy cuckoo search algorithm (MSACS) is used to iteratively optimize the important parameters of MCKD. Second, the optimized MCKD is used to filter and denoise the rolling bearing fault signal, and the denoised signal is obtained. Finally, the denoised signal is input to the CNN model for training and testing to obtain the classification result of fault diagnosis. Through the test and evaluation of the fault dataset, the proposed method is compared with particle swarm optimization (PSO) parameter optimization method (PSO-MCKD-CNN) and CNN method without noise reduction. At the same time, it is compared with other advanced methods. The experimental results shows that this method improves the diagnostic performance of the neural network, obtains higher diagnostic accuracy, and is more conducive to the detection of compound faults.

Symplectic Ramanujan Mode Decomposition and its application to compound fault diagnosis of bearings

The existing compound-fault diagnosis methods of rolling bearings have their own defects, which makes their accuracy of fault diagnosis impossible to be guaranteed. Therefore, this paper attempts to combine symplectic similarity transformation with Ramanujan subspace theory, and then a periodic impulse extraction method called symplectic Ramanujan mode decomposition (SRMD) method is proposed. SRMD separates the components with different fault features through symplectic similarity transformation and hierarchical clustering method to obtain symplectic clustering components (SCCs). At the same time, SRMD uses the Ramanujan subspace theory to extract the major periodic impulse components of each component to be extracted, and then obtains symplectic Ramanujan components (SRCs). The results show that SRMD is a resultful method in compound-fault diagnosis of bearings with excellent periodic impulse extraction ability.

Zero-shot learning for compound fault diagnosis of bearings

Due to the concurrency and coupling of various types of faults, and the number of possible fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault samples to identify unknown compound faults is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearings (ZLCFDM). We design an encoding method to express the semantics of single faults and compound faults according to the fault characteristics. A convolutional neural network is developed to extract the time–frequency features of the compound fault signal. Then we embed the semantic feature of the fault into the visual space of the fault data. The Euclidean distance is used to measure the distance between the signal features and the semantic features of the compound faults to identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on a self-built testbed. The results demonstrate that the accuracy of identifying compound fault reached 77.73% when the model was trained without any compound fault samples.