The Experts below are selected from a list of 45549 Experts worldwide ranked by ideXlab platform
Bosuk Yang - One of the best experts on this subject based on the ideXlab platform.
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support vector machine in machine condition monitoring and Fault Diagnosis
Mechanical Systems and Signal Processing, 2007Co-Authors: Achmad Widodo, Bosuk YangAbstract:Recently, the issue of machine condition monitoring and Fault Diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and Fault Diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and Diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and Fault Diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and Diagnosis. Until 2006, the use of SVM in machine condition monitoring and Fault Diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and Fault Diagnosis using SVM will be future works.
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multi agent decision fusion for motor Fault Diagnosis
Mechanical Systems and Signal Processing, 2007Co-Authors: Gang Niu, Bosuk Yang, Tian Han, Andy C C TanAbstract:Improvement of recognition rate is ultimate aim for Fault Diagnosis researchers using pattern recognition techniques. However, the unique recognition method can only recognise a limited classification capability which is insufficient for real-life application. An ongoing strategy is the decision fusion techniques. In order to avoid the shortage of single information source coupled with unique decision method, a new approach is required to obtain better results. This paper proposes a decision fusion system for Fault Diagnosis, which integrates data sources from different types of sensors and decisions of multiple classifiers. First, non-commensurate sensor data sets are combined using an improved sensor fusion method at a decision level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent classifiers fusion algorithm is employed as the core of the whole Fault Diagnosis system. The efficiency of the proposed system was demonstrated through Fault Diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best individual classifier with single-source data.
Yujie Cheng - One of the best experts on this subject based on the ideXlab platform.
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Fault Diagnosis for rotating machinery a method based on image processing
PLOS ONE, 2016Co-Authors: Chen Lu, Minvydas Ragulskis, Yang Wang, Yujie ChengAbstract:Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and Fault Diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, Fault Diagnosis methods based on multi-disciplines are becoming the focus in the field of Fault Diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for Fault Diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and Fault Diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact Fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main Fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for Fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to Fault Diagnosis for rotating machinery.
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Fault Diagnosis for rotating machinery a method based on image processing
PLOS ONE, 2016Co-Authors: Chen Lu, Minvydas Ragulskis, Yang Wang, Yujie ChengAbstract:Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and Fault Diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, Fault Diagnosis methods based on multi-disciplines are becoming the focus in the field of Fault Diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for Fault Diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and Fault Diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact Fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main Fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for Fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to Fault Diagnosis for rotating machinery.
Yuyan Zhang - One of the best experts on this subject based on the ideXlab platform.
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a new convolutional neural network based data driven Fault Diagnosis method
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Xinyu Li, Yuyan ZhangAbstract:Fault Diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven Fault Diagnosis becomes a hot topic. However, the traditional data-driven Fault Diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for Fault Diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven Fault Diagnosis method has achieved significant improvements.
Chen Lu - One of the best experts on this subject based on the ideXlab platform.
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Fault Diagnosis for rotating machinery a method based on image processing
PLOS ONE, 2016Co-Authors: Chen Lu, Minvydas Ragulskis, Yang Wang, Yujie ChengAbstract:Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and Fault Diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, Fault Diagnosis methods based on multi-disciplines are becoming the focus in the field of Fault Diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for Fault Diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and Fault Diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact Fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main Fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for Fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to Fault Diagnosis for rotating machinery.
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Fault Diagnosis for rotating machinery a method based on image processing
PLOS ONE, 2016Co-Authors: Chen Lu, Minvydas Ragulskis, Yang Wang, Yujie ChengAbstract:Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and Fault Diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, Fault Diagnosis methods based on multi-disciplines are becoming the focus in the field of Fault Diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for Fault Diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and Fault Diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact Fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main Fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for Fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to Fault Diagnosis for rotating machinery.
Xinyu Li - One of the best experts on this subject based on the ideXlab platform.
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a new convolutional neural network based data driven Fault Diagnosis method
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Xinyu Li, Yuyan ZhangAbstract:Fault Diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven Fault Diagnosis becomes a hot topic. However, the traditional data-driven Fault Diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for Fault Diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven Fault Diagnosis method has achieved significant improvements.
