PCA and PLS monitoring approaches for fault detection of wastewater treatment process

Chen, Ao; Zhou, Hongpeng; An, Yujian; Sun, Wei

doi:10.1109/isie.2016.7745032

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…They have become one of the most popular PHM methods in the aerospace industry. Data-driven methods can be classified into statistical methods (such as principal component analysis (PCA) [12] and its extensions [13], [14], independent component analysis (ICA) [15]- [17], and partial least squares [18]- [20]) and artificial intelligence methods.…”

Section: Introductionmentioning

confidence: 99%

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

Wang

Wen

et al. 2022

IEEE Access

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Fault diagnosis and classification (FDC) is an important part of prognostics and health management for ensuring safety and performance in the flight. However, it is challenging to achieve accurate FDC only based on single senor readings. In this paper, a fused FDC model among multiple different sensors is stabled by a hybrid deep learning architecture combining a sparse autoencoder (SAE) and a convolutional neural network (CNN). The hybrid model uses the SAE to enhance the hidden fault signal features in the multiple sensor signals, and then classifies the obtained feature map using the CNN. This method, which combines the advantages of the SAE in feature extraction and of the CNN in local feature recognition, fully utilizes the spatiotemporal coupling characteristics of multi-sensor signals. The FDC accuracy obtained by the proposed method when applied to a flight test data set is 93.78%, compared with 66.67% obtained using the combined SAE and feedforward neural network method and 83.11% obtained using the CNN only.INDEX TERMS convolutional neural network, fault classification, flight test data, fault diagnosis, sparse autoencoder.

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Section: Introductionmentioning

confidence: 99%

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

Wang

Wen

et al. 2022

IEEE Access

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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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“…Feng L V et al [3]applied PLS to the fault diagnosis of wind turbine, indicating that compared with PCA, PLS makes full use of sample space information and can perform fault diagnosis more effectively. A. Chen et al [4] used PCA and PLS in the fault process of the wastewater treatment, and concluded that both could achieve the purpose of the fault diagnosis. S W Choi et al [5] conducted fault diagnosis on the chemical process based on multi-block partial least square method, which could find fault blocks or fault variables.…”

Section: Introductionmentioning

confidence: 99%

Fault identification for chiller sensor based on partial least square method

Zhou

et al. 2021

E3S Web Conf.

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Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

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PCA and PLS monitoring approaches for fault detection of wastewater treatment process

Cited by 11 publications

References 17 publications

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

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

Fault identification for chiller sensor based on partial least square method

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