Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/4/1371 |
| _version_ | 1797299033765576704 |
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| author | Yi He Yunan Yao Hongsen Ou |
| author_facet | Yi He Yunan Yao Hongsen Ou |
| author_sort | Yi He |
| collection | DOAJ |
| description | Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions. |
| first_indexed | 2024-03-07T22:44:44Z |
| format | Article |
| id | doaj.art-90581829e02749e6b396f8b12ba719c5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-07T22:44:44Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-90581829e02749e6b396f8b12ba719c52024-02-23T15:05:47ZengMDPI AGApplied Sciences2076-34172024-02-01144137110.3390/app14041371Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-EncoderYi He0Yunan Yao1Hongsen Ou2School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaMarine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions.https://www.mdpi.com/2076-3417/14/4/1371marine centrifugal pumpsstacked sparse auto-encodervibration signalstatus recognition |
| spellingShingle | Yi He Yunan Yao Hongsen Ou Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder Applied Sciences marine centrifugal pumps stacked sparse auto-encoder vibration signal status recognition |
| title | Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder |
| title_full | Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder |
| title_fullStr | Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder |
| title_full_unstemmed | Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder |
| title_short | Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder |
| title_sort | status recognition of marine centrifugal pumps based on a stacked sparse auto encoder |
| topic | marine centrifugal pumps stacked sparse auto-encoder vibration signal status recognition |
| url | https://www.mdpi.com/2076-3417/14/4/1371 |
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