Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Yang, Dan; Mu, Hailin; Zhang, Xu; Wang, Zhigang; Yi, Cancan; Liu, Changming

doi:10.1155/2017/2641546

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Moreover, to demonstrate the capability of the newly constructed CV-SSA-SVC, Random Forest Classification (RFC) model [97], Backpropagation Artificial Neural Network (BPANN) [98,99], and Convolutional Neural Network (CNN) models [100] have been selected as benchmark approaches. e RFC, BPANN, and CNN are capable classifiers and have been widely employed in pattern recognition and particularly in data-driven or structural health monitoring based on computer vision [101][102][103][104][105][106][107][108][109][110][111][112].…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

Hoang

Huynh

Tran

et al. 2022

Advances in Civil Engineering

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During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. Moreover, the capability of identifying these two defects can help reduce the false-positive rate for pavement crack detection. Because crack and sealed crack are both line-based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack. This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. Image processing is used to compute features that characterize visual appearance and texture of the pavement image. Subsequently, Salp Swarm Algorithm integrated with multiclass support vector machine is employed for pattern recognition. Based on experimental results, the newly developed method has achieved the most desired predictive performance with an accuracy rate = 91.33% for crack detection and 92.83% for sealed crack detection.

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Section: Resultsmentioning

confidence: 99%

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

Hoang

Huynh

Tran

et al. 2022

Advances in Civil Engineering

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“…AdaBoost Advances in Civil Engineering ensemble of CTrees can be defined as a combination of multiple CTrees in which the final prediction result is obtained by combining the outputs of individual trees. Based on previous works [52][53][54][55][56], ensemble models have demonstrated better performance than individual models in a wide range of applications. e AdaBoost algorithm is demonstrated in Figure 6.…”

Section: Adaptive Boosting Classificationmentioning

confidence: 99%

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

Hoang

Nguyen

2018

Advances in Civil Engineering

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Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks. A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree. The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%. Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.

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“…The WPT is more practical in fault diagnosis schemes because of its better time–frequency resolution. Numerous studies investigating the time domain, frequency domain, and time–frequency domain features have been carried out to design fault diagnosis schemes using vibration signals in collaboration with machine learning (ML) methods (e.g., regression models, support vector machines, and artificial neural networks (ANNs)) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Huo et al [ 37 ] presented a multi-speed fault diagnosis scheme with the help of self-adaptive wavelet transform components.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Sohaib

Kim

2017

Sensors

172

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Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

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Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Cited by 5 publications

References 21 publications

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

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