Fault diagnosis in a wastewater treatment plant using dynamic Independent Component Analysis

Villegas, T.; Fuente, José Luis de la; Sainz-Palmero, G.I.

doi:10.1109/med.2010.5547760

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…The threshold value (95% and 99% limits used here) can be interpreted as measuring a process's systematic or normal variation. Under the assumption that training data are normal conditions or normal behavior, an observation found beyond the threshold would indicate that the systemic variations of that observation are abnormal (Villegas et al 2010).…”

Section: Combined Modelmentioning

confidence: 99%

“…The PLS method allows for the whole spectrum of UV-Vis absorption to be used to best predict DOC concentration without problems due to multicollinearity while providing consistency checks for new data used to inform the model's performance. Hotelling's T 2 and squared prediction errors (SPE; also known as the Q-statistic) are statistics built into PLS models that can detect and diagnose faults or outlier observations (Kaistha and Upadhyaya 2001;Villegas et al 2010;Wise and Roginski 2015;Dunn 2023). However, the full potential of these statistics has not been utilized or reported in previous studies where PLS models were used to predict in situ DOC concentration from absorption-(Table 1) or fluorescence-based monitoring (Lee et al 2015;Ruhala and Zarnetske 2017).…”

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confidence: 99%

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Self‐diagnosis of model suitability for continuous measurements of stream‐dissolved organic carbon derived from in situ UV–visible spectroscopy

Gaviria Salazar,

Roebuck,

Myers‐Pigg

et al. 2023

Limnology & Ocean Methods

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Application of high‐frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land‐use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site‐specific models were developed using data from a hillslope‐dominated forest or a low‐relief wetland‐pond‐dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L−1, mean = 8.38 mg C L−1, training RMSE = 0.34 mg C L−1, internal validation RMSE = 0.50 mg C L−1, external validation RMSE = 2.43 mg C L−1). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T2 and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T2 statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA254 values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L−1. The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. Implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.

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Section: Combined Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Self‐diagnosis of model suitability for continuous measurements of stream‐dissolved organic carbon derived from in situ UV–visible spectroscopy

Gaviria Salazar,

Roebuck,

Myers‐Pigg

et al. 2023

Limnology & Ocean Methods

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“…In practical industry cases, it is common for companies to tackle this problem by first improving the quality of data samples and then applying statistical methods like Multivariate Statistical Process Control (MSPC) (1,2) .…”

Section: Introductionmentioning

confidence: 99%

Improving Diagnosis with Correlation based Grouping and Stacked Unsupervised Models

Santana

Murakami

Osada

et al. 2023

The Proceedings of the 11th IIAE International Conference on Industrial Application Engineering 2023

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Anomaly or fault diagnosis is a prominent challenge in industry applications. Being often the case that only samples of normal behavior are available, and true labels are not present for validation, it is hard to determine a single general model to use; as well as how to select or remove features in order to improve results. This paper proposes the use of a set of stacked unsupervised linear/non-linear models to improve the general diagnosis. Experiments on 10 real-world datasets showed the value of the implemented approach, when compared to other examples of unsupervised statistical and machine learning methods employed to this task.

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“…Different methods have been developed in order to data mine and select the significant variables for fault detection. Multivariate statistical process control (MSPC) methods such as control charts [14,15], partial least squares (PLS) [16,17], independent component analysis (ICA) [18,19], and principal component analysis (PCA) [11,20] have been used for in-deep monitoring, feature extraction, and fault detection. Due to their intrinsic detection capacity, multivariate statistical methods show potential for efficiently finding the faults occurring in time-varying, ill-defined, and nonlinear systems [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Data Driven Detection of Different Dissolved Oxygen Sensor Faults for Improving Operation of the WWTP Control System

et al. 2021

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Sensor faults frequently occur in wastewater treatment plant (WWTP) operation, leading to incomplete monitoring or poor control of the plant. Reliable operation of the WWTP considerably depends on the aeration control system, which is essentially assisted by the dissolved oxygen (DO) sensor. Results on the detection of different DO sensor faults, such as bias, drift, wrong gain, loss of accuracy, fixed value, or complete failure, were investigated based on Principal Components Analysis (PCA). The PCA was considered together with two statistical approaches, i.e., the Hotelling’s T2 and the Squared Prediction Error (SPE). Data used in the study were generated using the previously calibrated first-principle Activated Sludge Model no.1 for the Anaerobic-Anoxic-Oxic (A2O) reactors configuration. The equation-based model was complemented with control loops for DO concentration control in the aerobic reactor and nitrates concentration control in the anoxic reactor. The PCA data-driven model was successfully used for the detection of the six investigated DO sensor faults. The statistical detection approaches were compared in terms of promptness, effectiveness, and accuracy. The obtained results revealed the way faults originating from DO sensor malfunction can be detected and the efficiency of the detection approaches for the automatically controlled WWTP.

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Fault diagnosis in a wastewater treatment plant using dynamic Independent Component Analysis

Cited by 12 publications

References 10 publications

Self‐diagnosis of model suitability for continuous measurements of stream‐dissolved organic carbon derived from in situ UV–visible spectroscopy

Self‐diagnosis of model suitability for continuous measurements of stream‐dissolved organic carbon derived from in situ UV–visible spectroscopy

Improving Diagnosis with Correlation based Grouping and Stacked Unsupervised Models

Data Driven Detection of Different Dissolved Oxygen Sensor Faults for Improving Operation of the WWTP Control System

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