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Test signals originating from the seven different sensors under the following conditions: (a) Norm, (b) C_G, (c) B_G, (d) W_P, (e) B_G_C_W_P, and (f) C_G_C_W_P.
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In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SS...
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This Letter investigates the transition to synchronization of oscillator ensembles encoded by simplicial complexes in which pairwise and higher-order coupling weights adapt with time through the Hebbian learning mechanism. These concurrently evolving disparate adaptive coupling weights lead to a novel phenomenon in that the in-phase synchronization...
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... The gear fault vibration signals collected from the QPZZ-II rotating machinery vibration test bench [37] is used to further verify the proposed method, and Fig. 12 shows the experiment rig. Ten different kinds of gear failures are simulated on the test bench, and the teeth numbers of the test gears are 75 and 55 respectively. ...
Aiming at solving the problems of limited training data, single input information, and limited diagnostic accuracy under the influence of strong background noise in fault diagnosis of rotating machinery, this paper proposes a fault diagnosis method based on the combination of discriminant correlation analysis (DCA) and convolutional neural network (CNN). Firstly, the original vibration signal is divided into several segments in the time domain, and the training data is directly processed by one CNN branch to extract multi-scale time domain features. Simultaneously, the divided data is subjected to discrete wavelet transform (DWT), and processed by another branch of CNN to extract multi-scale time-frequency features. Then, the DCA feature fusion mechanism is adopted to fuse the two-domain features extracted in the parallel branches to improve the model’ detection ability. Finally, the fused features are input into the deep CNN for training and learning to extract new features and output the classification results. Through the experimental analysis of two different types of data, the results show that the proposed method can be used for fault diagnosis of rotating machinery effectively. Compared with the single CNN network, the proposed method combines the multi-domain multi-scale feature extraction module with the DCA feature fusion module to enrich the feature information extraction ability. At the same time, the network performance is improved to get higher fault classification accuracy higher.
... In order to further verify the performance and effectiveness of the method proposed in this chapter, the gearbox fault data is used for experiments. This data set is the real signal of the gearbox collected from the QPZZ-II rotating machinery vibration test bench [23]. The experimental platform is shown in Fig. 13. ...
In order to solve the dependence of convolutional neural networks (CNN) on large samples of training data, an intelligent fault diagnosis method based on spectral kurtosis (SK) and attention mechanism is proposed. Firstly, the SK algorithm is used to obtain two-dimensional fast kurtosis graphs from vibration signals, and the two-dimensional fast spectral kurtosis graphs are converted into one-dimensional kurtosis time-domain samples, which are used as the input of CNN. Then the channel attention module (CAM) is added to CNN, and the weight is increased in the channel domain to eliminate the interference of invalid features. The accuracy of fault identification can reach 99.8 % by applying the proposed method on the fault diagnosis experiment of rolling bearings. Compared with the traditional deep learning (DL) method, the proposed method not only has higher accuracy, but also has lower dependence on the number of samples.
... The effectiveness and efficiency of the proposed AutoBTL algorithm have been validated with three benchmark datasets: the HUST bearing dataset, the QPZZ gearbox dataset [44], and the SEU mixed fault dataset [45]. Fig. 4 shows the diagnostics simulators. ...
Smart manufacturing system pursues automated modeling algorithms for industrial applications in dynamic environments. The prevalent deep transfer learning (DTL) has achieved promising results in cross-domain fault diagnosis. However, most DTL algorithms are dataset- and domain-specific. They require hyperparameter optimization (HPO) calling for prior knowledge to accomplish a promising prediction performance. This dilemma persists when new data from different domains arrive. An automated broad-transfer learning algorithm (AutoBTL) is proposed to improve predictive modeling for cross-domain tasks. AutoBTL includes three components, a broad classifier, an active estimator, and a hyperparameter optimizer for solving the HPO problem in cross-domain fault diagnosis. At each iteration, AutoBTL initiates a gated broad architecture assigned by the optimizer for target prediction. Then, an active estimator samples the target data with reliable pseudo labels for domain adaptation and performance evaluation. Finally, the optimizer updates a surrogate model and optimizes the hyperparameter space. These steps get repeated until satisfactory model is generated. AutoBTL involves a transductive joint validation strategy, which significantly improves the performance of the existing HPO algorithms in cross-domain tasks. The performance of the AutoBTL algorithm is validated with three benchmark datasets, including 14 cross-domain tasks. The computational results have demonstrated the accuracy and efficiency of the proposed algorithm over the widely used predictive modeling algorithms.
... With the development of tensor techniques, tensor-based diagnosis methods have been gradually being applied to the fault diagnosis of mechanical and electrical systems [12,13]. Recently, tensor technology has been gradually applied in complex industrial systems and can be coupled with machine learning models, for example, the deep learning model based on tensor factorization developed by Luo et al. [14], analog circuit fault diagnosis method based on tensor product wavelet developed by Jin et al. [15], and rotation machinery fault diagnosis method based on supervised second-order tensor developed by Wei et al. [16]. ...
With the increase in system complexity and operational performance requirements, nuclear energy systems are developing in the direction of intelligence and unmanned, which also requires a higher demand for its safety so that intelligent fault diagnosis and prediction have become a technology that nuclear power plants need to develop at present. At the same time, due to the rapid development of deep learning technology, it has become a meaningful development direction to predict the fault state of nuclear power plants within the framework of supervised deep learning. Usually, the network structure model used in fault diagnosis and prediction requires professional design, which may cost a lot of time and make it difficult to achieve optimal results. For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. Based on EDN-NPSP presented in this work, we can avoid complicated manual feature extraction and provide the predicted state directly and rapidly. It will provide operators with useful prediction information and enhance the nuclear energy system fault prediction capabilities.
... Similar to the process of data acquisition and interception under working conditions C1, C2, and C3, vibration acceleration data points of inner race fault, outer race fault, rolling element fault, and normal state were collected, and every 512 continuous vibration acceleration data points were viewed as one sample. Also, 220 samples were collected for the three faults and normal state respectively, in other words, there were 220 × 4 samples in total for the working condition C4. Figure 4 presents the gear fault diagnosis experiment platform of type QPZZ-II in Jiangsu Qianpeng Diagnostic Engineering Co., Ltd (JQDECL) [33]. The experimental data under the working condition C5 in table 1 were from the test data of this platform. ...
For fault diagnosis of rolling bearings, it is generally difficult or even impossible to obtain class labels of new working condition samples under actual variable working conditions, which leads to a low fault diagnosis accuracy. On account of this, we propose a novel unsupervised transfer learning method called inter-class repulsive force discriminant transfer learning (IRFDTL) in this paper. In the proposed IRFDTL, to reduce the classification error in source domain and improve the generalization ability of IRFDTL, a nonnegative extended slack matrix is creatively constructed to transform the strict binary label matrix into an extended slack label matrix. Moreover, the joint distribution discrepancy is introduced to reduce the difference between source and target domains, and the inter-class repulsive force term is innovatively designed to promote the discriminative learning effect by increasing the inter-class distance. Finally, the whole framework of IRFDTL is optimized by the alternating direction multiplier method. By using the labeled samples under historical working conditions, the IRFDTL can perform high-precision class discrimination on the testing samples under new working conditions when there are no class labels of testing samples. The proposed IRFDTL-based fault diagnosis method can achieve precise fault diagnosis of the testing samples under new working conditions, and fault diagnosis instances of rolling bearings verify the effectiveness of the proposed method.
... The effectiveness and efficiency of the proposed AUBTL algorithm has been validated with three datasets: the CWRU bearing dataset [47], QPZZ-II gearbox dataset [48] and the SEU mixed fault dataset [49]. Fig. 4 shows diagnostics simulators. ...
Deep-learning algorithms have produced promising results, however, domain adaptation remains a challenge. In addition, excessive training time and computing resource requirements need to be addressed. Deep-learning algorithms face a domain adaptation issue when the data distribution of a target domain differs from that of the source domain. The emerging concept of broad learning shows potential in addressing the domain adaptation and training time issues. An adaptive unsupervised broad transfer learning (AUBTL) algorithm is proposed to tackle the cross-domain problems. The proposed algorithm utilizes a sparse auto-encoder and random orthogonal mapping to extract and augment the feature space. Then, it initializes the weights of a classifier by solving a ridge regression problem. The logit ranking strategy is applied to develop a transfer estimator to evaluate and sample data in the target domain for an adaptive transfer. Based on the sampled data, AUBTL optimizes the hyper-parameter space. Performance of the AUBTL algorithm is validated with three benchmark datasets including 20 transfer tasks. The computational results demonstrated the efficiency and accuracy of the proposed algorithm over other deep learning algorithms considered in this research.
... Furthermore, the recognition effect is improved. The recognition results obtained when comparing two other traditional algorithms [25,26] in the environment with the SNR of 20dB are shown in Table 2. On the basis of the above data, it can be concluded that the improved PCA algorithm with meanization method can improve the contribution rate of the principal components and the recognition rate, which is beneficial to the identification of radiation source individual equipment, compared with the traditional PCA algorithm, fractal dimension-based algorithm, and Holder theory-based algorithm. The improved PCA algorithm has some advantages for the extraction of the subtle features of the radiation source individual signals. ...
With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extraction and recognition algorithm for radiation source individual signals based on multidimensional hybrid features. Firstly, Hilbert transform was performed on the radiation source signals from 10 identical radio devices, and the subtle features of different radiation sources’ signals were extracted. Then, traditional principal component analysis (PCA) algorithm was used to extract and reduce the principal components of the extracted feature data sets. Aiming at the insufficiency of traditional PCA algorithm, an improved principal component analysis algorithm was proposed. At last, a gray relation algorithm was used to classify and identify the radiation source individual signals, and the recognition rate was calculated. Experimental results show that Hilbert transform combined with the improved PCA algorithm can achieve a recognition rate of 99.67% for the "fingerprint" features of radiation source individual signals under the signal-to-noise ratio (SNR) of 20dB. Compared with the traditional algorithms, the recognition rate increased by 5.67%. Therefore, it provides a powerful theoretical basis for extracting subtle features of radiation source devices under complex electromagnetic environments.
The health monitoring system for equipment is essential in the smooth proceeding of industrial production. However, the fault features to be detected in monitoring systems are generally selected through projects and expertise, which are not capable for complex and ever-changing fault information and may result in incomplete correspondence to the fault types emerged. To dig deeper for the effective features hidden in the data instead of selecting by experience, a feature selection and fusion method based on poll mode and optimized Weighted Kernel Principal Component Analysis (WKPCA) method is then proposed. Specifically, inspired by poll-mode and multi-criteria strategy, a multi-measure hierarchical model is designed to sort the fault features with high sensitivity, acquiring the feature subset with corresponding weight coefficient. Considering the variation in fault information collected by different sensors, the diagnosis rate in Extreme Learning Machine (ELM) is taken as the index for evaluation of each single sensor, then the sensitivity weight matrix of features extracted by multiple sensors is constructed after linear normalization. To integrate the feature information, WKPCA is applied for the weighted fusion of features, and Quantum Genetic Algorithm (QGA) is used to search for the kernel width parameter when the best separability in the samples under the fusion is reached. Finally, such samples are introduced to drive the diagnostic model of the monitoring system in rolling bearing. The experimental results show that, compared with the traditional feature selection and fusion methods, this method is capable for sorting out highly sensitive features with more fault information self-adaptively, and can improve the separability in the subset of fault samples effectively.
