Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

Jun, Shuai; Shen, Changqing; Zhu, Zhongkui

doi:10.1155/2017/2384184

Cited by 10 publications

(10 citation statements)

References 34 publications

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“…The most popular neural network model is the rnultilayer perceptron (MLP), which is an extension of the single layer perceptron proposed by Rosenblatt [16]. Multilayer perceptrons, in general, are feedforward network, having distinct input, output, and hidden layers [20].…”

Section: Results and Analysismentioning

confidence: 99%

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The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

Mengistu

2018

TELKOMNIKA

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This paper presents the effects of segmentation techniques in the identification of Ethiopian coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of these regions very in color shape and texture. We investigated various segmentation techniques for efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans segmentation techniques are considered. For classification of the varieties of Ethiopian coffee beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is achieved when BPNN is used on FCM segmentation technique.

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Section: Results and Analysismentioning

confidence: 99%

“…They are physical dimensional measures that characterize the appearance of an object. For instance, area and perimeter are some of the most commonly measured size features and similarly circularity measures the shape of image compactness [16].…”

Section: Image Processing Systemmentioning

confidence: 99%

The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

Mengistu

2018

TELKOMNIKA

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“…Energy, Entropy, Correlation, Contrast, Homogeneous are some of the features that were calculated using the textural properties and are represented in the Equations () and these Equations are for Energy, Entropy, Contrast, Correlation and Homogeneity features, respectively. The Grey‐level matrix used to calculate the textural structures of the image have been included (Shuai et al, 2017).

italicEnergy goodbreak= \sum_{i, j = 1}^{N - 1} {(P_{i, j})}^{2}

italicEntrophy goodbreak= \sum_{i, j = 1}^{N - 1} goodbreak- \ln ((), P_{i, j}) P_{i, j}

italicContrast goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) {(i - j)}^{2}

italicCorrelation goodbreak= \sum_{i, j = 1}^{N - 1} ((), P_{i, j}) \frac{((), i goodbreak- μ) ((), j goodbreak- μ)}{σ^{2}}

italicHomogeneity goodbreak= \sum_{i, j = 0}^{N - 1} \frac{P_{i, j}}{1 + {(i - j)}^{2}}

where P i, j is normalized symmetrical, N is number of grey levels, μ is mean intensity, σ is variance of intensity.…”

Section: Methodsmentioning

confidence: 99%

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

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“…Even if there is strong noise or distortion in the signal, the basic morphological characteristics of the signal can be preserved after the signal is filtered. Compared with other signal analysis methods, the mathematical morphology filter, which has been widely used in pattern recognition, image processing, computer vision, power signals, ECG and EEG signal processing, mechanical equipment fault diagnosis, and other fields, has the advantages of easy implementation, fast algorithms, and minimal calculations [10][11][12][13][14]. Because the collected vibration signal of the bearing inevitably contains noise, the frequencies of the noise and fault signal will overlap in the frequency spectrum.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

Zhang

et al. 2020

International Journal of Rotating Machinery

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Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

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Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

Cited by 10 publications

References 34 publications

The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

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