Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer
The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 23(2023), 14 vom: 16. Juli |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Siyu [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Diesel engine |
|---|
| Anmerkungen: |
Date Revised 01.08.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/s23146447 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM360146112 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM360146112 | ||
| 003 | DE-627 | ||
| 005 | 20231226082405.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s23146447 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1200.xml |
| 035 | |a (DE-627)NLM360146112 | ||
| 035 | |a (NLM)37514741 | ||
| 035 | |a (PII)6447 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Siyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 01.08.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a diesel engine | |
| 650 | 4 | |a fault status identification | |
| 650 | 4 | |a reliability | |
| 650 | 4 | |a synchro squeezing S-transform | |
| 650 | 4 | |a time-frequency analysis | |
| 650 | 4 | |a vision transformer | |
| 700 | 1 | |a Liu, Zichang |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Yunbin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Rongcai |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Enzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Zhonghua |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 23(2023), 14 vom: 16. Juli |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2023 |g number:14 |g day:16 |g month:07 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s23146447 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 23 |j 2023 |e 14 |b 16 |c 07 | ||