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: | Pei-Hsi Lee, Shih-Lung Liao |
---|---|
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 |
Similar Items
-
New modified exponentially weighted moving average-moving average control chart for process monitoring
by: Khanittha Talordphop, et al.
Published: (2022-12-01) -
On designing new mixed modified exponentially weighted moving average - exponentially weighted moving average control chart
by: Khanittha Talordphop, et al.
Published: (2023-06-01) -
Nonparametric mixed exponentially weighted moving average-moving average control chart
by: Muhammad Ali Raza, et al.
Published: (2024-03-01) -
Use of improved memory type control charts for monitoring cancer patients recovery time censored data
by: Syed Muhammad Muslim Raza, et al.
Published: (2024-03-01) -
Residual Control Chart Based on a Convolutional Neural Network and Support Vector Regression for Type-I Censored Data with the Weibull Model
by: Pei-Hsi Lee, et al.
Published: (2023-12-01)