# Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

## TL;DR

This paper introduces a new control chart method using deep learning to monitor gamma type-I censored data more efficiently than traditional approaches.

## Contribution

The novel approach combines deep learning with EWMA control charts for gamma type-I censored data.

## Key findings

- The proposed method outperforms traditional EWMA charts in various skewness and censoring rates.
- Statistical simulations and a real-world case validate the improved efficiency of the new method.

## 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.