Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method
It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need fo...
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The Prognostics and Health Management Society
2024-10-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3856 |
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| author | Ken Ueno Shigeru Maya Kiyoku Endo |
| author_facet | Ken Ueno Shigeru Maya Kiyoku Endo |
| author_sort | Ken Ueno |
| collection | DOAJ |
| description | It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers. |
| format | Article |
| id | doaj-art-b3e04e07c44d4073a348541fb54c46f1 |
| institution | Kabale University |
| issn | 2153-2648 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-b3e04e07c44d4073a348541fb54c46f12025-08-20T03:49:17ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482024-10-01153110https://doi.org/10.36001/ijphm.2024.v15i3.3856Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation MethodKen Ueno0Shigeru Maya1Kiyoku Endo2System AI Lab., Corporate R&D Center, Toshiba Corporation, Kawasaki, Kanagawa 212-8582, JapanSystem AI Lab., Corporate R&D Center, Toshiba Corporation, Kawasaki, Kanagawa 212-8582, JapanToshiba Automation Systems Service Co., Ltd., Kawasaki, Kanagawa 210-8541, JapanIt is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.https://papers.phmsociety.org/index.php/ijphm/article/view/3856anomaly sign detectionhistogrammechatronicsautomatic ticket gatesfare collection systemrailway |
| spellingShingle | Ken Ueno Shigeru Maya Kiyoku Endo Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method International Journal of Prognostics and Health Management anomaly sign detection histogram mechatronics automatic ticket gates fare collection system railway |
| title | Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method |
| title_full | Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method |
| title_fullStr | Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method |
| title_full_unstemmed | Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method |
| title_short | Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method |
| title_sort | anomaly sign detection for automatic ticket gates by the histogram limitation method |
| topic | anomaly sign detection histogram mechatronics automatic ticket gates fare collection system railway |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/3856 |
| work_keys_str_mv | AT kenueno anomalysigndetectionforautomaticticketgatesbythehistogramlimitationmethod AT shigerumaya anomalysigndetectionforautomaticticketgatesbythehistogramlimitationmethod AT kiyokuendo anomalysigndetectionforautomaticticketgatesbythehistogramlimitationmethod |