RETRACTED: Signal fusion-based deep fast random forest method for machine health assessment

Yuan, Nanqi; Yang, Wenli; Kang, Byeong Ho; Xu, Shuxiang; Li, Chengjiang

doi:10.1016/j.jmsy.2018.05.004

Cited by 23 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the statistical theory and artificial intelligence are becoming the most widely adopted methods for fault diagnosis and condition monitoring of mechanical equipment, researchers in this field have achieved a wealth of results. A local mean decomposition method, the multimodal deep support vector classification, and the synchronously averaged electric motor signals were applied for gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…5 And the recently proposed iterative atomic decomposition thresholding (IADT) method which can extract true constituent components of complex signals and suppress background noise interferences also succeeded in fault diagnosis of wind turbine. 6 As the statistical theory and artificial intelligence are becoming the most widely adopted methods for fault diagnosis and condition monitoring of mechanical equipment, 7 researchers in this field have achieved a wealth of results. A local mean decomposition method, 8 the multimodal deep support vector classification, 9 and the synchronously averaged electric motor signals 10 were applied for gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…18 As one of the classical manifold learning algorithms, Laplacian Eigenmaps (LE) algorithm has been successfully applied to dimensionality reduction and data representation. 7 In contrast, the principal manifold describes the original dataset through constructing an embedded manifold structure (curve or surface) by using a geometric interpretation of principal component analysis (PCA) and approximating the curve or surface to the data distribution and curvature degree of the manifold. 19 The Hausdorff algorithm is generally used to evaluate the similarity among curves, which has been widely studied in the field of object matching and recognition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Yuan

Yang

Kang

et al. 2018

Energy Science & Engineering

Self Cite

View full text Add to dashboard Cite

As wind energy is experiencing an unprecedented development in today's world, the condition monitoring of wind turbine systems which can avoid serious accidents and economic losses are gathering more and more attentions. Considering the evaluation methods based on machine learning are complicated and unstable in terms of model training and parameter selection, this paper proposed a novel assessment algorithm based on similarity analysis of fuzzy k‐principal curves (FKPCs) in manifold space. Initially, 38 fusion features containing time‐domain, frequency‐domain, and wavelet node energy features are extracted from the vibration signal of the wind turbine bearing. Then, the nonlinear local algorithm Laplacian eigenmaps was introduced to transform the original features to the projected lower dimensional space and obtain the more typical parameters. Combining fuzzy clustering with the k‐principal curve creatively, smooth principal curves of the feature sets were then extracted according to point distribution in three‐dimensional manifold space. Finally, the Hausdorff distance algorithm was employed to calculate the similarity between the principal curves of healthy and test datasets to assess the running condition of wind turbine. To validate the present method, the experiments of accelerated bearing degradation were carried out. A series of comprehensive and persuasive comparisons and analysis with different feature conversion methods (principal component analysis [PCA] and Isomap), different distance analysis algorithms (Euclidean distance and Mahalanobis distance), and different assessment models (hidden Markov model [HMM] and deep belief network [DBN]) verified that the proposed FKPC technique can monitor the operating status of the machine more accurately without complex model training and parameter selection processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Yuan

Yang

Kang

et al. 2018

Energy Science & Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The sample p1 is input into the network, then the output yk p1 (k=0,1,…m-1   (10) Assume that wsq is the connection weight between any two neurons in the network, and the correction value using the gradient method to correct each wsq element is defined as:…”

Section: Fault Identification Using Bp Neural Networkmentioning

confidence: 99%

“…Wang et al [9] calculated the cone-shaped nuclear distribution of the vibration signal under different faults of the diesel engine and identified the fault of the diesel engine valve mechanism. Yuan et al [10] effectively monitored and evaluated the health and reliability of the engine based on the vibration signal method. Janssens et al [11] proposed a multi-sensors system for fault diagnosis of rotating machinery by combining vibration sensor with other sensors.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis method of diesel engine by combining rule-based algorithm and Bayesian/neural networks

Cai

Sun

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring and identification of faults are of great significance. At present, the fault research on diesel engine still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a constant speed. Therefore, a rule-based algorithm for fault diagnosis is proposed. Bayesian networks (BNs) and BP neural networks are used to identify seven faults at different speeds. Changchai EV80 diesel engine is taken as an example, and the feature values are extracted from the vibration signals measured from the cylinder head. The signals are processed by wavelet threshold de-noising and Ensemble Empirical Mode Decomposition (EEMD). The signal-sensitive feature values extracted from the decomposed Intrinsic Mode Function are used to distinguish different faults. After obtaining the feature values, a rule-based algorithm using IF... THEN's logic statement is proposed. BNs and BP neural networks established by parameter learning method are used for fault identification. Furthermore, this paper considers the uncertain factors and the interference of the external environment. Gaussian white noise is added to the raw signal and external excitation interference is applied to the diesel engine when it is running under normal operation condition. The results show that the proposed fault diagnostic method can accurately identify the faults.

show abstract

A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM

Wang,

Qiu,

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

RETRACTED: Signal fusion-based deep fast random forest method for machine health assessment

Cited by 23 publications

References 30 publications

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

Fault detection and diagnosis method of diesel engine by combining rule-based algorithm and Bayesian/neural networks

A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM

Contact Info

Product

Resources

About