Fault Detection for Automotive Coil Spring Using Signal Processing Analysis
Shock absorber failure can be easily detected during shock absorber utilization in the vehicle. The failure usually happened due to crack propagation under fatigue life of compress and extend operation. To prevent any failures during utilization it is preemptive to detect any possible fault during m...
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| Format: | Conference or Workshop Item |
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Springer, Singapore
2022
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| _version_ | 1796995347923337216 |
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| author | Alam, Mohammad Khurshed M. H., Mohammed Faozi A. R., Yusoff M. Z., Zainol Z., Khalil |
| author_facet | Alam, Mohammad Khurshed M. H., Mohammed Faozi A. R., Yusoff M. Z., Zainol Z., Khalil |
| author_sort | Alam, Mohammad Khurshed |
| collection | UMP |
| description | Shock absorber failure can be easily detected during shock absorber utilization in the vehicle. The failure usually happened due to crack propagation under fatigue life of compress and extend operation. To prevent any failures during utilization it is preemptive to detect any possible fault during manufacturing quality check inspection process. However, it is very difficult to do full check to all finished product due to high time consumption they require. In order to shorten the time, automated checking method are desire. In this study, automotive coil spring health are recognized using signal processing analysis to enable automated line quality check inspection. Fatigue testing machine was use to excite the spring in order to create signal needed in the processing analysis. The analysis was carried out using excitation signal detected along cycle time. Output data for both healthy and faulted springs (pre-inserted cracked) were processed and compared using signal processing analysis. This method shown an accurate consistency for fault detection of crack occurred in automotive spring where the number of peaks and valley of the signal as well as their maximum values not only able to show defective characteristics but also the severity degree of the defect where higher number and frequency density are more severe than not. This method will definitely able to shorten time needed for quality check inspection of cracks when applied in fabrication line compared to conventional method using naked eyes where micro cracks are very hard to detect. |
| first_indexed | 2024-03-06T13:01:00Z |
| format | Conference or Workshop Item |
| id | UMPir35490 |
| institution | Universiti Malaysia Pahang |
| last_indexed | 2024-03-06T13:01:00Z |
| publishDate | 2022 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | UMPir354902023-04-27T08:26:09Z http://umpir.ump.edu.my/id/eprint/35490/ Fault Detection for Automotive Coil Spring Using Signal Processing Analysis Alam, Mohammad Khurshed M. H., Mohammed Faozi A. R., Yusoff M. Z., Zainol Z., Khalil TJ Mechanical engineering and machinery Shock absorber failure can be easily detected during shock absorber utilization in the vehicle. The failure usually happened due to crack propagation under fatigue life of compress and extend operation. To prevent any failures during utilization it is preemptive to detect any possible fault during manufacturing quality check inspection process. However, it is very difficult to do full check to all finished product due to high time consumption they require. In order to shorten the time, automated checking method are desire. In this study, automotive coil spring health are recognized using signal processing analysis to enable automated line quality check inspection. Fatigue testing machine was use to excite the spring in order to create signal needed in the processing analysis. The analysis was carried out using excitation signal detected along cycle time. Output data for both healthy and faulted springs (pre-inserted cracked) were processed and compared using signal processing analysis. This method shown an accurate consistency for fault detection of crack occurred in automotive spring where the number of peaks and valley of the signal as well as their maximum values not only able to show defective characteristics but also the severity degree of the defect where higher number and frequency density are more severe than not. This method will definitely able to shorten time needed for quality check inspection of cracks when applied in fabrication line compared to conventional method using naked eyes where micro cracks are very hard to detect. Springer, Singapore 2022 Conference or Workshop Item PeerReviewed Alam, Mohammad Khurshed and M. H., Mohammed Faozi and A. R., Yusoff and M. Z., Zainol and Z., Khalil (2022) Fault Detection for Automotive Coil Spring Using Signal Processing Analysis. In: Enabling Industry 4.0 through Advances in Manufacturing and Materials: Selected Articles from iM3F 2021, Malaysia , 20 September 2021 , Virtually hosted by Universiti Malaysia Pahang. pp. 415-426.. ISBN 978-981-19-2890-1 https://doi.org/10.1007/978-981-19-2890-1_40 |
| spellingShingle | TJ Mechanical engineering and machinery Alam, Mohammad Khurshed M. H., Mohammed Faozi A. R., Yusoff M. Z., Zainol Z., Khalil Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title | Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title_full | Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title_fullStr | Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title_full_unstemmed | Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title_short | Fault Detection for Automotive Coil Spring Using Signal Processing Analysis |
| title_sort | fault detection for automotive coil spring using signal processing analysis |
| topic | TJ Mechanical engineering and machinery |
| work_keys_str_mv | AT alammohammadkhurshed faultdetectionforautomotivecoilspringusingsignalprocessinganalysis AT mhmohammedfaozi faultdetectionforautomotivecoilspringusingsignalprocessinganalysis AT aryusoff faultdetectionforautomotivecoilspringusingsignalprocessinganalysis AT mzzainol faultdetectionforautomotivecoilspringusingsignalprocessinganalysis AT zkhalil faultdetectionforautomotivecoilspringusingsignalprocessinganalysis |