An identification method for mechanical fault diagnosis based on generalized matrix norm sparse filtering

Shi, Zhao Ting; Wang, Jinrui; Bao, Huaiqian; Liu, Guifang; Zhang, Guowei; Gao, Tong

doi:10.1088/1361-6501/ac2619

Cited by 3 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ji [ 18 ] proposed an approach based on parallel SF and achieved the extraction of sparse features from acoustic signals. Shi [ 19 ] proposed a novel SF method based on a generalized matrix norm to successfully extract the fault characteristic from acoustic signals. Han [ 20 ] proposed a fast general normalized CSF via the L 1 -L 2 mixed norm, which enhanced the efficiency and robustness of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox

Fan,

Cai,

Zhang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Due to the superior robustness of outlier signals and the unique advantage of not relying on a priori knowledge, Convolution Sparse Filtering (CSF) is drawing more and more attention. However, the excellent properties of CSF is limited by its inappropriate selection of the number and length of its filters. Therefore, the Adaptive Convolution Sparse Filtering (ACSF) method is proposed in this paper to implement an end-to-end health monitoring and fault diagnostic model. Firstly, a novel metric entropy–time function (He−T) is proposed to measure the accuracy and efficiency of signals filtered by the CSF. Then, the filtered signal with the minimum He−T is detected with particle swarm optimization. Finally, the failure mode is diagnosed according to the envelope spectrum of the signal with minimum He−T. The effectiveness and efficiency of the ACSF is demonstrated through the experiment. The results indicate the ACSF can extract the failure characteristic of the gearbox.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox

Fan,

Cai,

Zhang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sparse filtering (SF) is an unsupervised learning method that has been successfully used in intelligent fault diagnosis [17,18]. Jia et al [19] presented a convolutional SF (CSF) and principal component analysis (PCA) to achieve weak feature extraction and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Weak fault diagnosis of machinery using Laplacian eigenmaps and parallel sparse filtering

Ji¹,

Wang²,

Han³

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Machinery fault diagnosis is crucial for maintenance cost reduction and accident prevention. Vibration signal monitoring is an effective and feasible method for machinery fault diagnosis. However, extraction of the fault-related periodic impulses from weakly monitoring signals is basic but difficulty for fault diagnosis. In this paper, a new weak feature extraction model by using Laplacian eigenmaps and parallel sparse filtering (LE-PSF) is presented for mechanical weak fault diagnosis. Specifically, the weak vibration signal is measured from the machinery pedestal. Then, LE is used to extract principal components of the overlapped signal segements, and PSF is employed for weak feature extraction from the principal components. Finally, the extracted features are inputted to softmax regression for fault classification. A simulation study and two experimental cases are employed to testify the effect of the LE-PSF model. Experimental performances show that the LE-PSF can not only achieve accurate fault classification but also is superior to other traditional methods.

show abstract

“…The spectral structure of the vibration signal collected from planetary gearboxes exhibits complex features, such as asymmetrical sidebands in the vicinity of the meshing frequency even if the planetary gearbox is operating under healthy conditions. For this reason, the inherent characteristics of modulation sidebands can result in the invalidation of traditional fault diagnosis methodologies, which have been proven to be effective applied for fixed axial gearboxes [2,3]. In view of this, numerous studies have been implemented in which traditional signal processing techniques have been modified and adapted to effectively diagnose WT planetary gearboxes [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Investigating the vibration response and modulation mechanism for health monitoring of wind turbine planetary gearboxes using a tribodynamics-based analytical model

li¹,

Tong²,

Tong³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

In wind turbine (WT) planetary gearbox system, the characteristics of incipient faults are difficult to recognize with increasing friction under a lack of lubrication condition. To this end, a translational-torsional coupled nonlinear tribodynamic model considering local faults is developed. A fitted time-varying transfer function is obtained using an impact experiment excited by a planet gear with a missing tooth and is further used to represent the modulation effect caused by the carrier. Using the proposed analytical model, the vibration response and modulation mechanism of the resultant signal under the coupling effect of tooth root crack and sliding friction are investigated. It is demonstrated that specific sidebands are greatly excited and the spectrum structure becomes extremely complex after the introduction of friction. Based on these results, sideband indicators are obtained to monitor the lubrication state and detect incipient faults. The experimental results based on an in-house WT drivetrain testbench are in good agreement with the simulation results.

show abstract

An identification method for mechanical fault diagnosis based on generalized matrix norm sparse filtering

Cited by 3 publications

References 25 publications

Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox

Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox

Weak fault diagnosis of machinery using Laplacian eigenmaps and parallel sparse filtering

Investigating the vibration response and modulation mechanism for health monitoring of wind turbine planetary gearboxes using a tribodynamics-based analytical model

Contact Info

Product

Resources

About