Decentralized waste water treatment facilities monitor many features that are complexly related. The ability to detect the onset of a fault and to identify variables accurately that have shifted because of the fault are vital to maintaining proper system operation and high quality produced water. Various multivariate methods have been proposed to perform fault detection and isolation, but the methods require data to be independent and identically distributed when the process is in control, and most require a distributional assumption. We propose a distribution‐free retrospective change‐point‐detection method for auto‐correlated and non‐stationary multivariate processes. We detrend the data by using observations from an in‐control time period to account for expected changes due to external or user‐controlled factors. Next, we perform the fused lasso, which penalizes differences in consecutive observations, to detect faults and to identify shifted variables. To account for auto‐correlation, the regularization parameter is chosen by using an estimated effective sample size in the extended Bayesian information criterion. We demonstrate the performance of our method compared with a competitor in simulation. Finally, we apply our method to waste water treatment facility data with a known fault, and the variables identified by our proposed method are consistent with the operators’ diagnosis of the fault's cause.

中文翻译：

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复杂的分散式废水处理设施的故障隔离

分散式废水处理设施可监控许多复杂相关的功能。检测故障发生并准确识别因故障而发生变化的变量的能力对于维持正常的系统运行和高质量的生产水至关重要。已经提出了各种多元方法来执行故障检测和隔离，但是当过程处于控制状态时，这些方法要求数据是独立的并且均匀分布的，并且大多数都需要分布假设。我们提出了一种用于自动相关和非平稳多元过程的无分布回顾性变化点检测方法。我们通过使用控制期内的观察值来消除数据趋势，以说明由于外部或用户控制因素而产生的预期变化。下一个，我们执行融合的套索，对连续观察中的差异进行惩罚，以检测故障并识别移位的变量。为了考虑自相关，通过在扩展贝叶斯信息准则中使用估计的有效样本大小来选择正则化参数。我们在仿真中证明了我们的方法与竞争对手相比的性能。最后，我们将我们的方法应用于具有已知故障的废水处理设施数据，并且通过我们的方法识别出的变量与操作员对故障原因的诊断是一致的。通过在扩展贝叶斯信息准则中使用估计的有效样本大小来选择正则化参数。我们在仿真中证明了我们的方法与竞争对手相比的性能。最后，我们将我们的方法应用于具有已知故障的废水处理设施数据，并且通过我们的方法确定的变量与操作员对故障原因的诊断相一致。通过在扩展贝叶斯信息准则中使用估计的有效样本大小来选择正则化参数。我们在仿真中证明了我们的方法与竞争对手相比的性能。最后，我们将我们的方法应用于具有已知故障的废水处理设施数据，并且通过我们的方法确定的变量与操作员对故障原因的诊断相一致。