Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks
Abstract In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control ch...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-56884-8 |
| _version_ | 1797247268794925056 |
|---|---|
| author | Pei-Hsi Lee Shih-Lung Liao |
| author_facet | Pei-Hsi Lee Shih-Lung Liao |
| author_sort | Pei-Hsi Lee |
| collection | DOAJ |
| description | Abstract In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data. |
| first_indexed | 2024-04-24T19:56:00Z |
| format | Article |
| id | doaj.art-6ab1ca3c9be44ffa8b7c93a424b440b0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:56:00Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6ab1ca3c9be44ffa8b7c93a424b440b02024-03-24T12:20:00ZengNature PortfolioScientific Reports2045-23222024-03-0114111210.1038/s41598-024-56884-8Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networksPei-Hsi Lee0Shih-Lung Liao1Department of Information Management, Chaoyang University of TechnologyDepartment of Information Management, Chaoyang University of TechnologyAbstract In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.https://doi.org/10.1038/s41598-024-56884-8Deep learning methodsConditional expected valueConditional medianRight-censored dataExponentially weighted moving average chart |
| spellingShingle | Pei-Hsi Lee Shih-Lung Liao Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks Scientific Reports Deep learning methods Conditional expected value Conditional median Right-censored data Exponentially weighted moving average chart |
| title | Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks |
| title_full | Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks |
| title_fullStr | Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks |
| title_full_unstemmed | Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks |
| title_short | Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks |
| title_sort | monitoring gamma type i censored data using an exponentially weighted moving average control chart based on deep learning networks |
| topic | Deep learning methods Conditional expected value Conditional median Right-censored data Exponentially weighted moving average chart |
| url | https://doi.org/10.1038/s41598-024-56884-8 |
| work_keys_str_mv | AT peihsilee monitoringgammatypeicensoreddatausinganexponentiallyweightedmovingaveragecontrolchartbasedondeeplearningnetworks AT shihlungliao monitoringgammatypeicensoreddatausinganexponentiallyweightedmovingaveragecontrolchartbasedondeeplearningnetworks |