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

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 suff...

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Bibliographic Details
Published in:PloS one Vol. 11; no. 10; p. e0164111
Main Authors: Lu, Chen, Wang, Yang, Ragulskis, Minvydas, Cheng, Yujie
Format: Journal Article
Language:English
Published: United States Public Library of Science 06.10.2016
Public Library of Science (PLoS)
Subjects:
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 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
Beijing China
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary: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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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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0164111