The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing

Li, Hongru; Wang, Yukui; Wang, Bing; Sun, Jizhou; Li, Yaolong

doi:10.1016/j.ymssp.2016.05.038

Cited by 43 publications

(23 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, ω i d ] T indicates the weight vector between the input layer and the i-th hidden layer neuron, β i � [β i1 , β i2 , · · · , β is ] T represents the connection weight of the i-th hidden node to the output layer, b i denotes the bias of the i-th hidden node, and o j implies the output of the ELM model of the j-th sample. Another form of equation (21) can be described as the following equality:…”

Section: Elm For Fault Pattern Identificationmentioning

confidence: 99%

“…On the other hand, the studies of the MM-based intelligent fault diagnosis approach are also universal (e.g., one-dimensional adaptive rank-order morphological filter [14], the combination of morphological filter and k-nearestneighbor classifier [15], the combination of morphological operators and fuzzy inference [16], the combination of MM and support vector machine [17], the combination of morphological filter and local tangent space alignment [18], and the combination of morphological filter and grey relational degree [19]). Meanwhile, most MM-based fault diagnosis studies focus on three aspects including morphological fractal dimension [20], morphological particle [21], and pattern spectrum (PS) [22]. ereinto, PS can reveal shape characteristics of bearing vibration signal based on the morphological opening or closing operation with multiscale SE, while pattern spectrum entropy (PSE) is defined based on PS, which is regarded as a newly spawned index of complexity estimation [23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

et al. 2020

View full text Add to dashboard Cite

Feature extraction is recognized as a critical stage in bearing fault diagnosis. Pattern spectrum (PS) and pattern spectrum entropy (PSE) in recent years have been smoothly applied in feature extraction, whereas they easily ignore the partial impulse signatures hidden in bearing vibration data. In this paper, the pattern gradient spectrum (PGS) and pattern gradient spectrum entropy (PGSE) are firstly presented to improve the performance of fault feature extraction of two approaches (PS and PSE). Nonetheless, PSE and PGSE are only able to evaluate dynamic behavior of the time series on a single scale, which implies there is no consideration of feature information at other scales. To address this problem, a novel approach entitled multiscale pattern gradient spectrum entropy (MPGSE) is further implemented to extract fault features across multiple scales, where its key parameters are determined adaptively by grey wolf optimization (GWO). Meanwhile, a Laplacian score-(LS-) based feature selection strategy is employed to choose the sensitive features and establish a new feature set. Finally, the selected new feature set is imported into extreme learning machine (ELM) to identify different health conditions of rolling bearing. Performance of our designed algorithm is tested on two experimental cases. Results confirm the availability of our proposed algorithm in feature extraction and show that our method can recognize effectively different bearing fault categories and severities. More importantly, the designed approach can achieve higher recognition accuracies and provide better stability by comparing with other entropy-based methods involved in this paper.

show abstract

Section: Elm For Fault Pattern Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Raj and Murali [17] introduced a new method for the selection of SEs that depends on kurtosis, which is effective and robust in bringing out the impulses from bearing fault signals. Li et al [18] calculated the general mathematical morphology particle from the normal state to failure; the calculated index was proven to be a valuable indicator of the degradation of the bearing performance. Yu et al [19] applied an improved morphological component analysis to separate the meshing and periodic impulse components.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

Jun

Shen

Zhu

2017

International Journal of Rotating Machinery

View full text Add to dashboard Cite

Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.

show abstract

“…The types of features are usually classified into three categories, time domain features, frequency domain features, and time-frequency domain features. The time-frequency domain features are always based on timefrequency analysis, combined with the concept of spectrum, entropy, and complexity, for example, the Rényi entropy [5], the permutation entropy [6], and the general mathematical morphology particle [7]. In general, mechanical equipment undergoes a complete degradation from normal stage to failure.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

Wang

et al. 2017

International Journal of Rotating Machinery

Self Cite

View full text Add to dashboard Cite

By focusing on the issue of rolling element bearing (REB) performance degradation assessment (PDA), a solution based on variational mode decomposition (VMD) and Gath-Geva clustering time series segmentation (GGCTSS) has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD), VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF) as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely.

show abstract

The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing

Cited by 43 publications

References 35 publications

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

Contact Info

Product

Resources

About