Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines Article Swipe
Xiaomo Jiang
,
Fumin Wang
,
Haixin Zhao
,
Shengli Xu
,
Lin Lin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.36001/phmconf.2020.v12i1.1147
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.36001/phmconf.2020.v12i1.1147
Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.
Related Topics
Concepts
Vibration
Fault (geology)
Condition monitoring
Noise (video)
Computer science
Turbomachinery
Convolutional neural network
Fault detection and isolation
Engineering
Control theory (sociology)
Control engineering
Artificial intelligence
Actuator
Acoustics
Control (management)
Mechanical engineering
Geology
Electrical engineering
Seismology
Physics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.36001/phmconf.2020.v12i1.1147
- https://papers.phmsociety.org/index.php/phmconf/article/download/1147/904
- OA Status
- diamond
- Cited By
- 1
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3099216629
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3099216629Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36001/phmconf.2020.v12i1.1147Digital Object Identifier
- Title
-
Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating MachinesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-03Full publication date if available
- Authors
-
Xiaomo Jiang, Fumin Wang, Haixin Zhao, Shengli Xu, Lin LinList of authors in order
- Landing page
-
https://doi.org/10.36001/phmconf.2020.v12i1.1147Publisher landing page
- PDF URL
-
https://papers.phmsociety.org/index.php/phmconf/article/download/1147/904Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://papers.phmsociety.org/index.php/phmconf/article/download/1147/904Direct OA link when available
- Concepts
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Vibration, Fault (geology), Condition monitoring, Noise (video), Computer science, Turbomachinery, Convolutional neural network, Fault detection and isolation, Engineering, Control theory (sociology), Control engineering, Artificial intelligence, Actuator, Acoustics, Control (management), Mechanical engineering, Geology, Electrical engineering, Seismology, Physics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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