Intrinsic component filtering for fault diagnosis of rotating machinery

Zhang, Zongzhen; Li, Shunming; Lu, Jiming; Xin, Yu; Ma, Huijie

doi:10.1016/j.cja.2020.07.019

Cited by 29 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a and b are the dilation factor and translation factor, respectively. From Equation (7), it is clear that different c a, b t ð Þ function can be acquired with different values of a and b. For square integrable signal s, that is,…”

Section: Data Preprocessingmentioning

confidence: 99%

Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery

Zou

Lam

2022

Structural Health Monitoring

View full text Add to dashboard Cite

Accurate fault diagnosis technology is essential for ensuring reliable operation of rotating machinery. However, complex conditions and various damage forms bring challenges to present diagnosis technology. In this study, a novel fault diagnosis method is proposed utilizing the newly developed adaptive resize-residual deep neural networks. The usage of the proposed method consists of three steps. First, the continuous wavelet transform is used to transfer the acquired vibration signals into time–frequency images. Second, the histogram equalization algorithm is applied to enhance the contrast of these images. Finally, the enhanced images are used as the input of newly proposed adaptive resize-residual networks, in which the adaptive resize block can deduce the dimensions of input data by self-learning and feed them into the residual block for pattern recognition. Two experimental cases are designed to evaluate the performance of proposed method. The experimental results indicate that the proposed adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery

Zou

Lam

2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Zhang et al introduced generalized normalization to the sparse filtering and discussed the lifetime and population sparsity [24]. In [25,26], Intrinsic Component Filtering (ICF) and Cr-SF, the improved variants of standard SF, were proposed for the in intelligent fault diagnosis, weak feature extraction, and compound separation. Cr-SF is a variant of SF.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Cr-SF can be regarded as an unsupervised minimum entropy feature learning method, in which the entropy of the extracted features is measured as cross-sparsity. Considering the advantages of Cr-SF in extracting features of small sample [25], this paper proposes parallel Cr-SF networks.…”

Section: Introductionmentioning

confidence: 99%

Parallel Cross-Sparse Filtering Networks and Its Application on Fault Diagnosis of Rotating Machinery

et al. 2022

Self Cite

View full text Add to dashboard Cite

Intelligent diagnosis method has become a new focus for researchers, which can get rid of the dependence of diagnostic experience and prior knowledge. However, in practical application, to deal with the new fault type of mechanical equipment, the number of fault labels of the diagnosis model needs to be increased. We must retrain the whole training model, which is a time-consuming process. To solve this problem, higher requirements are put forward for the generalization ability and universality of the algorithm. In view of the feature extraction advantages of cross-sparse filtering (Cr-SF), which can be regarded as an unsupervised minimum entropy learning method using the maximization of the proxy of sparsity, this paper proposed a parallel network based on Cr-SF. The feature extraction process of each sample is independent, and the feature extraction and classifier training process are separated. Therefore, the most prominent advantage of the proposed method is that when a new fault occurs, it only needs to extract the feature of the new fault separately and then input it to the classifier at the last layer for training. The experimental results show that the proposed method can obtain high accuracy and stability and can significantly improve the adaptability of intelligent fault diagnosis in practical application.

show abstract

“…The ℓ 2 regularization is not enforced, and the results of the feature selection are poor. Numerous types of research based on the ℓ 1 form, ℓ 1 / ℓ 2 form, and ℓ 1 + ℓ 2 form have emerged to induce sparsity for feature selection in learning [ 38 ]. The ℓ 1 regularization is widely utilized to select features during the machine learning process.…”

Section: Introductionmentioning

confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

Materials

View full text Add to dashboard Cite

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

show abstract

Intrinsic component filtering for fault diagnosis of rotating machinery

Cited by 29 publications

References 29 publications

Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery

Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery

Parallel Cross-Sparse Filtering Networks and Its Application on Fault Diagnosis of Rotating Machinery

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Contact Info

Product

Resources

About