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| Title | Pollution concentration monitoring using a new Birnbaum-Saunders control chart |
| Creator | 楊素芬 Yang, Su-Fen;Lu, Ming-Che |
| Contributor | 統計系 |
| Key Words | air pollution monitoring; exponentially weighted moving average control chart; fatigue life; statistical process control |
| Date | 2024-11 |
| Date Issued | 24-Feb-2025 15:36:52 (UTC+8) |
| Summary | Air pollution monitoring is an important issue in environmental science. The Birnbaum–Saunders (BS) distribution, originally applied to describe product failure time distribution to fatigue failures and general random wear failures, is also well to describe the pollutant concentration data due to accumulations of various pollutants in the air over time. Sulfur dioxide (SO2) is a critical factor in air pollution. Hence, it is important to monitor its concentration variation for air pollution prevention. Due to the complexity of its distribution form, there is no reliable and easy-to-use control chart for monitoring pollutant concentrations based on the BS distribution. We found that the SO2 concentration data follows the BS distribution. In this study, we propose a new median control chart based on the exact sampling distribution of the monitoring statistic to detect shifts in the median of BS distribution. Thus, given the false alarm rate, the control limits for such control charts can be obtained precisely satisfying a preset in-control average run length using Monte Carlo simulations. The out-of-control average run lengths are calculated by simulation to evaluate the detection performance of the proposed chart when the median shifts occur. We further compare the detection performance of the proposed chart and those of the existing control charts based on asymptotic sampling distributions. In order to improve the detection ability of the proposed chart for small median shifts, an exponentially weighted moving average (EWMA) control chart is constructed. The results of numerical analyses demonstrated that the proposed EWMA chart performs much better than all existing control charts for monitoring the median of BS distribution. Finally, the proposed control charts are applied to monitor the median of SO2 concentrations for air pollution control, showing that both charts can effectively detect a shift in the median of SO2 concentrations. The proposed EWMA control chart even detects out a small shift in the median of SO2 concentrations. The results provide a continuous monitoring solution for air pollution prevention. |
| Relation | Quality and Reliability Engineering International, Vol.40, No.7, pp.3913-3933 |
| Type | article |
| DOI | https://doi.org/10.1002/qre.3608 |
| dc.contributor | 統計系 | |
| dc.creator (作者) | 楊素芬 | |
| dc.creator (作者) | Yang, Su-Fen;Lu, Ming-Che | |
| dc.date (日期) | 2024-11 | |
| dc.date.accessioned | 24-Feb-2025 15:36:52 (UTC+8) | - |
| dc.date.available | 24-Feb-2025 15:36:52 (UTC+8) | - |
| dc.date.issued (上傳時間) | 24-Feb-2025 15:36:52 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/155775 | - |
| dc.description.abstract (摘要) | Air pollution monitoring is an important issue in environmental science. The Birnbaum–Saunders (BS) distribution, originally applied to describe product failure time distribution to fatigue failures and general random wear failures, is also well to describe the pollutant concentration data due to accumulations of various pollutants in the air over time. Sulfur dioxide (SO2) is a critical factor in air pollution. Hence, it is important to monitor its concentration variation for air pollution prevention. Due to the complexity of its distribution form, there is no reliable and easy-to-use control chart for monitoring pollutant concentrations based on the BS distribution. We found that the SO2 concentration data follows the BS distribution. In this study, we propose a new median control chart based on the exact sampling distribution of the monitoring statistic to detect shifts in the median of BS distribution. Thus, given the false alarm rate, the control limits for such control charts can be obtained precisely satisfying a preset in-control average run length using Monte Carlo simulations. The out-of-control average run lengths are calculated by simulation to evaluate the detection performance of the proposed chart when the median shifts occur. We further compare the detection performance of the proposed chart and those of the existing control charts based on asymptotic sampling distributions. In order to improve the detection ability of the proposed chart for small median shifts, an exponentially weighted moving average (EWMA) control chart is constructed. The results of numerical analyses demonstrated that the proposed EWMA chart performs much better than all existing control charts for monitoring the median of BS distribution. Finally, the proposed control charts are applied to monitor the median of SO2 concentrations for air pollution control, showing that both charts can effectively detect a shift in the median of SO2 concentrations. The proposed EWMA control chart even detects out a small shift in the median of SO2 concentrations. The results provide a continuous monitoring solution for air pollution prevention. | |
| dc.format.extent | 96 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Quality and Reliability Engineering International, Vol.40, No.7, pp.3913-3933 | |
| dc.subject (關鍵詞) | air pollution monitoring; exponentially weighted moving average control chart; fatigue life; statistical process control | |
| dc.title (題名) | Pollution concentration monitoring using a new Birnbaum-Saunders control chart | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1002/qre.3608 | |
| dc.doi.uri (DOI) | https://doi.org/10.1002/qre.3608 |