Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis

Miao, Mengqi; Sun, Yuanhang; Yu, Jianbo

doi:10.1016/j.knosys.2022.108116

Cited by 31 publications

(9 citation statements)

References 30 publications

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“…With the increasing arrangement of monitoring systems, a prodigious volume of unlabeled mechanical signals is amassed for the formulation of cost-efficient, swift, and robust intelligent fault diagnosis (IFD) methodologies. These DL-based fault diagnosis methods, which utilize time-domain [15,16], frequency-domain [17,18], or even unprocessed raw data as input [19,20], provide great frameworks for extracting salient fault-characterizing features. In addition, by combining unsupervised learning approaches like domain adaptation IFD methods can extract unlabeled data's features by using existing models and data without relying on labeling.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Li,

Liu,

Zhang

et al. 2024

Meas. Sci. Technol.

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Utilizing unsupervised domain adaptation in intelligent fault diagnosis has demonstrated significant potential for ensuring the security of machinery systems. Nonetheless, the imbalance attribute of data inherently affects the optimized performance of diagnostic model. Especially, for machinery operating under varied conditions, the acquired unlabeled data frequently exhibits characteristics with diverse degrees of distributional deviations, thus further undermining the model's generalization capability. To address this challenge, we introduce a method termed Dynamic Unsupervised Imbalanced Domain Adaptation (DUIDA) for fault diagnosis. Employment of class rebalancing and label-dependent margin regularization strategies optimizes the selection of decision boundaries which counteract the discrepancies introduced by the imbalance. In addition, by integrating a dynamic weighting mechanism, encompassing both adversarial-based and MMD-based domain adaptation, our model becomes versatile across varied UIDA tasks, assigning elevated weights to salient fault features. Finally, our empirical analyses on two faulty bearing datasets substantiate the efficacy and superior performance of the proposed framework across diverse operational scenarios.

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Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Li,

Liu,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Yang et al [27] proposed a neural network model that combines CNN, GRU, and attention to explore the process of neural network feature extraction on a bearing dataset. Miao et al [28] combined sparse representation with attention to achieving efficient feature learning and fault diagnosis. Guo et al [29] used dimensionality reduction and visualization techniques to examine the feature distribution of each layer in the model and attempt to understand the internal mechanism of feature extraction in each layer of the network.…”

Section: Introductionmentioning

confidence: 99%

Knowledge features enhanced intelligent fault detection with progressive adaptive sparse attention learning for high-power diesel engine

Liu

Kong

et al. 2023

Meas. Sci. Technol.

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High-power diesel engines are core power equipment in some key fields, and fault diagnosis is of great significance for improving their long-term operational reliability and safety. The lack of sufficient fault samples, known as few-shot conditions, has resulted in the performance degradation of most current intelligent diagnostic methods under complex and variable working conditions. Moreover, the end-to-end deep learning diagnostic models based on implicit layer features do not have clear physical meanings, making it difficult for intelligent diagnostic models to be recognized by equipment managers in key fields. Mechanism-based fusion methods can effectively alleviate the above problems by incorporating fault-sensitive features with clear physical meanings. However, the lack of sufficient knowledge-related features that can characterize fault information is a challenge facing current mechanism fusion methods. In this work, we propose a progressive adaptive sparse attention mechanism-guided knowledge feature incremental mining method. Fault-sensitive segments are obtained based on attention-supervised learning with an improved activation function. Both knowledge-related features and hidden layer features from these segments are incorporated into the feature library. A differential constraint is designed between the distribution of knowledge features and hidden layer features, encouraging the model to focus on learning new knowledge-related features in the subsequent iteration process. Finally, a feature set containing a large amount of knowledge features is constructed to enhance fault diagnosis. Testing on a V12 diesel engine test bench under the conditions of rich and few-shot data sets, shows that the proposed method significantly enhances the fault recognition performance of diesel engines under complex variable working conditions and few-shot data set conditions.

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“…Among them, the fault diagnosis technology based on vibration signal processing is the most mature [2][3][4]. From the traditional signal processing to the intelligent diagnosis of machine learning and deep learning [5,6], the processing based on vibration signal is able to effectively complete the fault diagnosis. However, in the process of train operation, the vibration signal analysis method cannot complete the diagnosis task simply and efficiently due to the limitation of many complex and complicated environments.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

et al. 2023

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Trackside acoustic signals are useful for non-contact measurements as well as early warnings in the diagnosis of train wheelset bearing faults. However, there are two important problems when using roadside acoustic signals to diagnose wheel-to-wheel bearing faults; one is the presence of strong interference from strong noise and high harmonics in the signal, and the other is the low efficiency of bearing fault identification caused by it. Therefore, from the viewpoint of solving the two problems, a sparse operation method is proposed for denoising and detuning the modulation of the roadside acoustic signal, and a machine learning classifier with a Genetic Algorithm (GA)-optimized Radial Basis Neural Network (RBFNN) is proposed to improve the rate at which the features of roadside acoustic signal faults are recognized. Firstly, the background noise is filtered out from the Doppler-corrected acoustic signal using the Sparse Representation method, and the inverse wavelet transform is reconstructed into a noiseless signal. Secondly, the interference high-harmonic signal in the signal is filtered out using the Resonant Sparse Signal Decomposition (RSSD) method. Then, the GA is selected to optimize the parameters of the RBF neural network and build a fault diagnosis model. Finally, the extracted acoustic signal feature set is trained on the network model, and the trained model is used for testing. In summary, the sparse operation on the roadside acoustic signal processing and the GA-RBFNN diagnosis model were verified as being very effective in the diagnosis of roadside acoustic train wheel pair faults through the simulation experiment.

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Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis

Cited by 31 publications

References 30 publications

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Knowledge features enhanced intelligent fault detection with progressive adaptive sparse attention learning for high-power diesel engine

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

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