The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal ...characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods.
In recent years, autoencoder has been widely used for the fault diagnosis of mechanical equipment because of its excellent performance in feature extraction and dimension reduction; however, the ...original autoencoder only has limited feature extraction ability due to the lack of label information. To solve this issue, this study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis. Compared with the existing methods, FD-SAE has stronger feature extraction ability and faster network convergence speed. By analyzing the characteristics of original rolling bearing data, it is found that there are evident differences between normal data and faulty data. Therefore, a simple linear support vector machine (SVM) is used to classify normal data and faulty data, and then the proposed FD-SAE is used for fault classification. The novel combination of SVM and FD-SAE has simple structure and little computational complexity. Finally, the proposed method is verified on the rolling bearing data set of Case Western Reserve University (CWRU).
The faults of rolling bearings can result in the deterioration of rotating machine operating conditions, how to extract the fault feature parameters and identify the fault of the rolling bearing has ...become a key issue for ensuring the safe operation of modern rotating machineries. This paper proposes a novel hybrid approach of a random forests classifier for the fault diagnosis in rolling bearings. The fault feature parameters are extracted by applying the wavelet packet decomposition, and the best set of mother wavelets for the signal pre-processing is identified by the values of signal-to-noise ratio and mean square error. Then, the mutual dimensionless index is first used as the input feature for the classification problem. In this way, the best features of the five mutual dimensionless indices for the fault diagnosis are selected through the internal voting of the random forests classifier. The approach is tested on simulation and practical bearing vibration signals by considering several fault classes. The comparative experiment results show that the proposed method reached 88.23% in classification accuracy, and high efficiency and robustness in the models.
Recent works suggest that using knowledge transfer strategies to tackle cross-domain diagnosis problems is promising for achieving engineering diagnosis. This article presents a diagnosis scheme for ...rolling bearing under a challenging domain generalization scenario, in which more potential discrepancies among multiple source domains are eliminated and only normal samples of the target domain are available during the training stage. To achieve sufficient generalization performance, a diagnosis scheme combining some a priori diagnosis knowledge and a deep domain generalization network for fault diagnosis (DDGFD) is elaborated. Through signal preprocessing steps guided by the a priori diagnosis knowledge, the inputs of DDGFD with a primary consistent meaning across domains are constructed from the vibration signal. On this basis, DDGFD would intently release its talent on learning discriminative and domain-invariant fault features from source domains, and then generalize the learned knowledge to identify unseen target samples. On cross-domain tasks organized using broad bearing data sets, the superiority of DDGFD is validated by comparing its performance with various data-driven diagnosis methods.
The digital twin of a life-cycle rolling bearing is significant for its degradation performance analysis and health management. This article proposes a digital twin model of life-cycle rolling ...bearing driven by the data-model combination. With the measured signals and the bearing fault dynamic model, the time-varying defect size is estimated, and the evolution law of bearing defect during the life cycle is revealed by a back propagation neural network. Then, the excitations of evolutionary defects are introduced into the bearing dynamic model, so as to form a life-cycle bearing dynamic model in the virtual space. Finally, the simulation data in the virtual space is mapped into the corresponding data in the physical space via an improved CycleGAN neural network with the smooth cycle consistency loss. By comparing the obtained digital twin result with the measured signal in the time-domain and frequency-domain, the effectiveness of the proposed model is verified.
Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid catastrophic accidents. Domain adaptation is emerging as a critical technology for the intelligent fault ...diagnosis of rolling bearing. Most existing solutions learn domain-invariant features by statistical moment matching, adversarial training, or fusing two algorithms. However, these domain adaptation methodologies overemphasized learning domain-invariant features and ignored the generalization of classification performance on the target domain, which leads to inevitable misclassification. To address this issue, we propose a supervised contrastive learning-based domain adaptation network (SCLDAN) for cross-domain fault diagnosis of the rolling bearing in this paper. The SCLDAN develops a 1-D convolutional residual network to learn the raw signal features and employs the maximum mean discrepancy loss to achieve global domain alignment. In addition, a novel supervised contrastive learning approach is proposed, where a supervised contrastive loss and a mutual information loss are established to learn the class-specific information and improve the reliability of target prediction labels. Thus, the ambiguous data samples residing near the class boundaries of the target domain can be accurately identified, and the diagnosis accuracy is significantly improved. Extensive experiments on two experimental scenarios demonstrate the effectiveness of the proposed method.
For the two shortcomings of singular value decomposition (SVD), the determination of the reconstruction order and the poor noise reduction ability, an enhanced SVD is introduced in this article. The ...core ideas include: first, an efficient method to determine the reconstructed order of SVD and the relative-change rate of the singular envelope kurtosis is presented, composed of improved SVD (ISVD). Then, the method to select the optimal node of wavelet packet transform (WPT) by the criterion of envelope kurtosis maximum is presented, composed of improved WPT (IWPT). The flexible filter design and superior noise reduction abilities of the IWPT and the passband denoise ability of the ISVD are organicly combined to form enhanced singular value decomposition (E-SVD) method. In addition, an indicator is introduced to evaluate the performance of the results. First, the reconstructed signal is obtained by performing ISVD on the original signal. Second, IWPT is executed on the reconstructed signal to achieve the optimal node. Finally, the filtered signal is combined with the envelope power spectrum to extract the bearing fault characteristic frequency. The method's validity and superiority are verified by the analysis of simulated data and actual cases of rolling bearing.
The scope of data-driven fault diagnosis models is greatly extended through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational ...efficiency and feature representation, while the latest Transformer architecture based on attention mechanism has not yet been applied in this field. To solve these problems, we propose a novel time–frequency Transformer (TFT) model inspired by the massive success of vanilla Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract effective abstractions from the time–frequency representation (TFR) of vibration signals. On this basis, a new end-to-end fault diagnosis framework based on time–frequency Transformer is presented in this paper. Through the case studies on bearing experimental datasets, we construct the optimal Transformer structure and verify its fault diagnosis performance. The superiority of the proposed method is demonstrated in comparison with the benchmark models and other state-of-the-art methods.
•A novel model named time–frequency Transformer (TFT) is proposed.•A fresh tokenizer and encoder module are designed to extract effective abstractions.•A new end-to-end fault diagnosis framework based on TFT is presented.•The proposed method shows superiority comparing to other state-of-the-art methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved ...fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.
The fault-induced impulse responses of localized bearing fault are usually interfered by the background noise and other harmonic components. They are strongly coupled together and are hard to be ...separated. It is crucial to develop a fast and reliable method to extract the impulse-based feature for online bearing fault diagnosis in the industry application. In this article, we propose a new sparse elitist group lasso denoising (SEGLD) algorithm in frequency domain to detect the incipient impulse-based fault feature, which is free of utilizing the prior knowledge. We first reveal the sparse characteristics of the bearing fault signals in frequency domain. Then, a tailored denoising model is proposed. To obtain a satisfactory analytical stationary solution, the Douglas-Rachford splitting solver is employed for the denoising model. Moreover, we explore the relationship between the best regularization parameters, the periodic information and the normalization estimated noise of the rolling bearing fault signal. A rule of adaptively selecting the best regularization parameters is demonstrated. Finally, the robustness and effectiveness of the proposed SEGLD algorithm are profoundly verified by the numerical simulation and two evaluation experiments under the conditions of early fault stage and low speed scenario. Also, it is demonstrated that the proposed approach outperforms the state-of-the-art method for extracting the weak fault feature.
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