An automatic feature extraction method and its application in fault diagnosis

Wang, Jinrui; Li, Shunming; Jiang, Xingxing; Cheng, Chun

doi:10.21595/jve.2017.17906

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…Instead of focusing on learning the input data distribution, the sparse filtering (SF) algorithm, which is a two-layer unsupervised feature learning algorithm, optimizes the sparsity of the learned representation. It can effectively scale with the input data size [12] and is used in this way frequently. The number of features that ensures optimal solution convergence is the only parameter that needs tuning, making it very easy to use.…”

Section: B Sparse Filteringmentioning

confidence: 99%

“…It is implemented by multiplying two OPs derived from each hybrid OPs (TKP and KTP) [11]. Sparse filtering (SF) is a powerful technique in signal processing and machine learning that aims to extract informative features from high-dimensional data by encouraging sparsity in the representation [12]. Sparse filtering applies an unsupervised learning algorithm to learn a set of filters or basis functions that capture the most salient features of the input data.…”

Section: Introductionmentioning

confidence: 99%

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EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

S. Radeaf,

Mohammed Z. Al-Faiz

2024

Iraqi Journal of ICT

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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed 30% of the overall EEG samples for accuracy to be over 90%.

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Section: B Sparse Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

S. Radeaf,

Mohammed Z. Al-Faiz

2024

Iraqi Journal of ICT

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“…where k σ (x i − y i ) is a kernel function, and σ is the size of the kernel. Gaussian kernel [22] is the most commonly used expressed as (6):…”

Section: Definition Of Correntropymentioning

confidence: 99%

Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method

Gong

et al. 2022

Applied Sciences

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In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD for denoising is mainly to remove singular components (SCs) with small singular value (SV), which ignores the weak signals buried in strong noise. Aiming to extract the weak signals in strong noise, this paper proposed a method of selecting SCs by the correntropy-induced metric (CIM). Then, the frequency components of characteristic signals can be found through cyclic correntropy spectrum (CCES) which is the extension of the correntropy (CE). The proposed method firstly merges the signals collected by the two channels, secondly uses the principal components analysis (PCA) method to reduce the dimensionality, thirdly uses the singular value decomposition method to decompose the signal, fourthly calculates the CIM value to determine the selected singular components for construction, and finally uses the cyclic correntropy spectrum displaying the characteristics of the reconstructed signal. The experimental results show that the proposed method has a good effect on feature extraction.

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“…For each added signal, the number of extracted statistics doubles down, resulting often in a very high-dimensional dataset. For this reason, a second step, named feature selection or feature extraction is performed in order to transform the high-dimensional dataset in a lower dimensional dataset, by automatically selecting or extracting only relevant and non-redundant features , (Wang, Li, Jiang & Cheng, 2017). In our problem, even if signals representing the operating condition are known, the different trends they assume in each condition is unknown.…”

Section: Related Work and Problem Settingmentioning

confidence: 99%

Streaming-based feature extraction and clustering for condition detection in dynamic environments: an industrial case

et al. 2020

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As relevant aspects of PHM, feature extraction and diagnostics represent the focus of many paper related to predictive maintenance. Feature extraction is a fundamental part in PHM, as the accuracy of diagnostic models strongly depends on the goodness of the features. In particular, it is performed when components or systems are provided with many sensors, in order to extract relevant and non-redundant information from the raw dataset. Diagnostic is a typical pattern recognition problem, in which the data are classified according to the operating or fault condition they refer. Thus, to obtain the Remaining Useful Life (RUL) of the component, usually an offline analysis, including feature extraction and diagnostics, is performed, whose results are then used for degradation modelling and finally RUL prediction. However, when systems do not operate in strictly controlled environments, as in case of industrial contexts, it is very challenging to obtain information about the operating condition at the moment of signal acquisition. This results in unlabeled datasets, which cannot be used by supervised learning algorithms, as ANNs and SVMs. In addition, operating conditions change over time and it is not always possible to know a priori all possible conditions. These considerations suggest to resort to streaming applications, in which models can directly learn from new incoming data. As the degradation rate may vary according to the operating condition, influencing the RUL prediction, one should always know in which condition the machinery is operating, or should recognize if a new condition is occurring. In addition, it is not possible to extract good features that distinguish different conditions, if a condition is not known. Therefore, features should be extracted in an unsupervised manner and incrementally, so that if a new condition occurs, eventually better features can be extracted. Furthermore, an incremental clustering should be conducted so to always recognize the condition under which the system is operating, if known, or to detect a new condition. In this paper, a streaming-based procedure for feature extraction and clustering is proposed, which is validated on a real industrial case study. A batch and supervised feature extraction and diagnostics are also performed on the same dataset, to demonstrate that the two approaches have similar results, in terms of accuracy with respect to the known conditions. In addition, thanks to the incremental clustering, the proposed approach is also able to detect and automatically label new machinery operating conditions.

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An automatic feature extraction method and its application in fault diagnosis

Cited by 12 publications

References 29 publications

EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier

Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method

Streaming-based feature extraction and clustering for condition detection in dynamic environments: an industrial case

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