Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies

Lopez, Israel; Sarigul‐Klijn, Nesrin

doi:10.1016/j.ymssp.2009.02.014

Cited by 23 publications

(10 citation statements)

References 13 publications

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“…Its applications in damage detection and fault diagnosis are also reported in the literature [14,[18][19][20]. Therefore, only a brief introduction of CPN is given in the paper as for how the CPN works.…”

Section: Counter-propagation Network (Cpn)mentioning

confidence: 99%

A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection

Jiang

Zhang

et al. 2013

International Journal of Distributed Sensor Networks

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This paper proposes a revised counter-propagation network (CPN) model by integrating rough set in structural damage detection, applicable for processing redundant and uncertain information as well as assessing structural health states. Firstly, rough set is used in the model to deal with a large volume of data; secondly, a revised training algorithm is developed to improve the capabilities of the CPN model; and lastly, the least input vectors are input to the revised CPN (RCPN) model, hence the rough set-based RCPN is proposed in the paper. To validate the model proposed, numerical experiments are conducted, and, as a result, six damage patterns have been successfully identified in a steel frame. The influence of measurement noise, the network models adopted, and the data preprocessing methods on damage identification is also discussed in the paper. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also significantly reduces the data storage requirement and saves computing time.

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Section: Counter-propagation Network (Cpn)mentioning

confidence: 99%

A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection

Jiang

Zhang

et al. 2013

International Journal of Distributed Sensor Networks

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“…The technique can be realized through several algorithms including locally linear embedding (LLE) [17], isometric feature mapping (IsoMap) [18], local tangent space alignment (LTSA) [19], and Laplacian eigenmaps (LE) [20], etc. Many studies have been conducted by applying manifold learning to the machinery fault diagnosis [21][22][23][24][25][26][27][28][29][30][31][32]. In recent years, the application of manifold learning in mechanical fault diagnosis can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the application of manifold learning in mechanical fault diagnosis can be divided into two categories. On one hand, manifold learning is used to extract non-linear features for fault classification [21][22][23][24][25][26][27][28], on the other hand, this technique is applied to extract the transient signals from the noise contaminated signal [29][30][31][32]. The above mentioned studies have demonstrated that manifold learning is effective to extract the intrinsic manifold features related to non-linear dynamics of the mechanical system.…”

Section: Introductionmentioning

confidence: 99%

Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

Wang

et al. 2015

Mechanical Systems and Signal Processing

209

113

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“…17 An approach to solve the problem is applying the dimension reduction technique to these features so that it can facilitate classification and compression of the pattern space. 18 One purpose of feature reduction is to obtain a more compact representation of the high-dimensional features. Another aim is to discover the inherent diagnosis information from the original data which is limited by the FE techniques.…”

Section: Introductionmentioning

confidence: 99%

Identification of bearing faults using linear discriminate analysis and continuous hidden Markov model

Liu

Chen

Dong

2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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It is important to diagnose the bearing fault to prevent the serious accident of equipment. This paper introduces a bearing fault identification scheme based on envelope power spectrum analysis, linear discriminate analysis and continuous hidden Markov model. First the envelope power spectrum features are extracted from amplitude demodulated vibration signals from fault bearings. Then, linear discriminate analysis is employed to reduce the feature dimensions, which are helpful for improving the computing speed and diagnosing accuracy. At last, the new linear discriminate analysis features are input into continuous hidden Markov model to train the models under different conditions, respectively. In bearing fault identification, test data are input into the pretrained continuous hidden Markov models, and the bearing state can be detected by the output of continuous hidden Markov model. To validate the effectiveness of the proposed method, experimental samples of four bearing conditions at different fault sizes and loads are utilized to test the continuous hidden Markov model and back-propagation neural network. The result shows that continuous hidden Markov model and linear discriminate analysis-based method have higher accuracy and efficiency than back-propagation neural network.

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Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies

Cited by 23 publications

References 13 publications

A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection

A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection

Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

Identification of bearing faults using linear discriminate analysis and continuous hidden Markov model

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