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Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

Lu, Chen ; Wang, Yang ; Ragulskis, Minvydas ; Cheng, Yujie Deng, Zhaohong

PloS one, 2016-10, Vol.11 (10), p.e0164111 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Fault Diagnosis for Rotating Machinery: A Method based on Image Processing
Autor: Lu, Chen ; Wang, Yang ; Ragulskis, Minvydas ; Cheng, Yujie
Deng, Zhaohong
Assuntos: Accuracy ; Algorithms ; Artificial intelligence ; Artificial neural networks ; Axial flow pumps ; Biology and Life Sciences ; Centrifugal pumps ; Centrifugation - instrumentation ; Computation ; Computer and Information Sciences ; Condition monitoring ; Contours ; Decomposition ; Diagnosis ; Earth Sciences ; Embedding ; Engineering ; Engineering and Technology ; Environmental engineering ; Equipment and supplies ; Equipment Failure Analysis - methods ; Fault diagnosis ; Feature extraction ; Image processing ; Image processing equipment ; Image Processing, Computer-Assisted ; Industrial applications ; International conferences ; Laboratories ; Machinery ; Machinery and equipment ; Machinery condition monitoring ; Mechanical equipment ; Medical diagnosis ; Methods ; Neural networks ; Noise ; Pattern recognition ; Physical Sciences ; Priming ; Research and Analysis Methods ; Researchers ; Rotating machinery ; Rotation ; Science and technology ; Shape ; Signal processing ; Stochasticity ; Technology ; Vibration
É parte de: PloS one, 2016-10, Vol.11 (10), p.e0164111
Notas: Conceptualization: YC. Data curation: YW. Formal analysis: YC. Funding acquisition: CL. Investigation: YC. Methodology: YC YW. Project administration: CL. Resources: CL. Software: YW YC. Supervision: MR CL. Validation: YW. Visualization: YW. Writing – original draft: YW. Writing – review & editing: YC YW.
Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

Lu, Chen ; Wang, Yang ; Ragulskis, Minvydas ; Cheng, Yujie Deng, Zhaohong

United States: Public Library of Science

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