Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD

Yang, Boyuan; Liu, Ruonan

doi:10.1109/tii.2017.2662215

Cited by 184 publications

(61 citation statements)

References 30 publications

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“…Commonly, the typical methods can be summarized as time-frequency distributions [5], the empirical mode decomposition [6] and its multivariate extensions [7], the local mean decomposition [8], the reassignment method [9,10] such as the synchrosqueezing transform (SST) [9], the sparsification methods [11,12], and so on.…”

Section: Related Workmentioning

confidence: 99%

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Sun

Miao

2018

Complexity

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We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR). The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.

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Section: Related Workmentioning

confidence: 99%

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Sun

Miao

2018

Complexity

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“…A wide variety of the OMP algorithms are listed in [34–36]. The dictionary learning methods have been used for bearing fault diagnosis [37, 38], which restricted diagnosis signal to shift-invariant in time and assumed hidden Markov model structure.…”

Section: Introductionmentioning

confidence: 99%

Adaptive online dictionary learning for bearing fault diagnosis

Xie

Liang

2018

Int J Adv Manuf Technol

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One of the most common parts to maintain system balance and support the various load in rotating machinery is the rolling element bearing. The breakdown of the element in bearings leads to inefficiency and sometimes catastrophic events across various industries. The main challenge over the last few years for fault diagnosis of bearings is the early detection of fault signature. In this paper, an adaptive online dictionary learning algorithm is developed for early fault detection of bearing elements. The dictionary is trained using a set of vibration signal from a heavily damaged bearing. The enveloped signal of the bearing is obtained using the Kurtogram and split into several sections. The K-SVD algorithm is implemented to the dictionaries corresponding to the enveloped signal. OMP is implemented with the calculated dictionaries to obtain the sparse representation of the testing signal. Then the envelope analysis is implemented to obtain the fault signal from the recovered signal by the dictionaries. The adaptive algorithm is added to the dictionary learning to update the dictionary based on newly acquired data with the weighted least square method. Without retraining the dictionaries using the K-SVD algorithm, the computation speed is significantly improved. The proposed algorithm is compared with a traditional dictionary learning algorithm to show the improvement in detection of new fault frequency and improved signal to noise ratio.

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“…Finally, we can realize the result of SAR image de-noising. However, the traditional sparse representation methods (e.g., K-means singular value decomposition, K-SVD) are sensitive to the position and phase [18], which means that even the same image feature with different position or phase may lead to different atoms of the training dictionary. It is well known that the image is shift-invariant; when we use the traditional K-SVD to do the image de-noising, there will be some Gibbs effects and the training of shift atoms will be time-consuming [19].…”

Section: Introductionmentioning

confidence: 99%

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.

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Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD

Cited by 184 publications

References 30 publications

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Adaptive online dictionary learning for bearing fault diagnosis

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

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