基于残余的多变量指数加权移动平均控制图,用于统计过程控制苏拉巴亚市的水质,利用生成对抗网络
Muhammad Ahsan1, Raditya Widi Indarsanto1, Kevin Agung Fernanda Rifki2
1Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
MethodsX
|July 24, 2025
概括
本研究引入了一种使用生成对抗网络 (GAN) 的新框架,以减少水质时间序列数据中的自相关性,改善统计过程控制 (SPC) 并实现更好的水资源管理的早期异常检测.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 工艺工程是过程工程.
背景情况:
- 水质时间序列数据 (pH,度,KMnO4) 的自相关性损害了统计过程控制 (SPC) 的假设.
- 这一问题影响了苏拉巴亚市监测水质的可靠性.
- 现有的SPC方法与环境数据固有的时间依赖性作斗争.
研究的目的:
- 通过解决自身相关性,开发一个新的框架来提高水质的SPC.
- 使用生成对抗网络 (GAN) 来生成脱相关的时间序列余数.
- 将GAN衍生的残留物整合到SPC图表中,以改善过程监测和异常检测.
主要方法:
- 研究了pH,度和KMnO4时间序列数据中的自相关性.
- 开发并验证了一种生成对抗网络 (GAN) 模型,以生成脱相关余量.
- 在多变量指数加权移动平均线 (MEWMA) 控制图中集成GAN衍生的余量,用于I期和II期分析.
主要成果:
- 该GAN模型有效地降低了自身相关性,通过低MSE (0.0054),RMSE (0.0738) 和MAE (0.0556) 得到验证.
- 使用MEWMA图表与GAN残留物进行I阶段分析,确定并纠正初始工艺偏差.
- 第二阶段监测检测到八个重要的失控信号,表明需要持续评估稳定性.
结论:
- 基于GAN的残余分析是减轻环境水质数据中自相关性的强有力的策略.
- 这种方法显著提高了过程监控的准确性,并使得早期异常检测成为可能.
- 该框架支持主动的水质管理,并确保随着时间的推移提高工艺稳定性.
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