Fault isolation for a complex decentralized waste water treatment facility
Fault isolation for a complex decentralized waste water treatment facility
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DOI：
10.1111/rssc.12429
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发表时间：
2020-07
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影响因子：
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通讯作者：
M. Klanderman;Kathryn B. Newhart;T. Cath;A. Hering
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文献类型：
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作者：
M. Klanderman;Kathryn B. Newhart;T. Cath;A. Hering
分散的废水处理设施监控许多复杂相关的功能。检测故障的开始和准确地识别由于故障而改变的变量的能力对于维持适当的系统操作和高质量的采出水是至关重要的。已经提出了各种多变量方法来执行故障检测和隔离，但是这些方法要求数据在过程受控时是独立和同分布的，并且大多数需要分布假设。我们提出了一种自相关非平稳多变量过程的无分布回溯变点检测方法。我们通过使用受控时间段的观察结果来降低数据的趋势，以解释由于外部或用户控制因素引起的预期变化。接下来，我们执行融合套索，惩罚连续观测的差异，以检测故障并识别移位变量。为了解释自相关性，正则化参数是通过使用扩展贝叶斯信息准则中的估计有效样本量来选择的。我们证明了我们的方法相比，在模拟的竞争对手的性能。最后，我们将我们的方法应用于废水处理设施的数据与已知的故障，我们提出的方法所识别的变量是一致的操作员的诊断故障的原因。
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.