Fault detection and diagnosis of batch process using dynamic ARMA-based control charts
利用动态 ARMA 控制图检测与诊断批次过程故障AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要方法核心是 ARMA 动态建模与序列相关处理，应用场景为工业批次监控。
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利用动态 ARMA 控制图检测与诊断批次过程故障
- 研究问题
- 批次过程同时具有批次间变异与批次内序列相关，如何调整时间序列推断以监控新批次，并定位动态扰动来源？
- 方法与思路
- 作者对历史批次分别用普通最小二乘估计 ARMA 系数，推导修正的 Hotelling 与 t 分布，再构建用于监控和诊断的控制图。
- 材料陈述的贡献
- 摘要提出同时考虑批次间变异和批次内相关的控制图体系，并称模拟与真实数据显示其在故障检测和诊断上表现良好。
- 阅读关注点
- 核对 ARMA 系数的估计实现、修正分布的推导条件、控制限与误报率，以及控制图如何将异常对应到扰动来源。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出误报率、检测能力或样本规模，适用性不能仅凭定性评价判断。
原文摘要
A wide range of approaches for batch processes monitoring can be found in the literature. This kind of process generates a very peculiar data structure, in which successive measurements of many process variables in each batch run are available. Traditional approaches do not take into account the time series nature of the data. The main reason is that the time series inference theory is not based on replications of time series, as it is in batch process data. It is based on the variability in a time domain. This fact demands some adaptations of this theory in order to accommodate the model coefficient estimates, considering jointly the batch to batch samples variability (batch domain) and the serial correlation in each batch (time domain). In order to address this issue, this paper proposes a new approach grounded in a group of control charts based on the classical ARMA model for monitoring and diagnostic of batch processes dynamics. The model coefficients are estimated (through the ordinary least square method) for each historical time series sample batch and modified Hotelling and t-Student distributions are derived and used to accommodate those estimates. A group of control charts based on that distributions are proposed for monitoring the new batches. Additionally, those groups of charts help to fault diagnosis, identifying the source of disturbances. Through simulated and real data we show that this approach seems to work well for both purposes.