Reduced Gaussian process regression for fault detection of chemical processes

Monitoring and fault detection methods are increasingly important to achieve a robust and resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this paper was to evaluate a promising machine learning method, Gaussian process regression (GPR), for WWTP monitoring applications. We evaluated GPR at two WWTP monitoring problems: estimate missing data in a flow rate signal (simulated data), and detect a drift in an ammonium sensor (real data). We showed that GPR with the standard estimation method, maximum likelihood estimation (GPR-MLE), suffered from local optima during estimation of kernel parameters, and did not give satisfactory results in a simulated case study. However, GPR with a state-of-the-art estimation method based on sequential Monte Carlo estimation (GPR-SMC) gave good predictions and did not suffer from local optima. Comparisons with simple standard methods revealed that GPR-SMC performed better than linear interpolation in estimating missing data in a noisy flow rate signal. We conclude that GPR-SMC is both a general and powerful method for monitoring full-scale WWTPs. However, this paper also shows that it does not always pay off to use more sophisticated methods. New methods should be critically compared against simpler methods, which might be good enough for some scenarios.

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A Robust Fault Detection and Location Prediction Module Using Support Vector Machine and Gaussian Process Regression for AC Microgrid

IEEE Transactions on Industry Applications ◽

10.1109/tia.2021.3129982 ◽

2021 ◽

pp. 1-1

Author(s):

Adhishree Srivastava ◽

S.K. Parida

Keyword(s):

Support Vector Machine ◽

Fault Detection ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Support Vector ◽

Location Prediction ◽

Prediction Module ◽

Robust Fault Detection ◽

Ac Microgrid

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Machine learning based Gaussian process regression for fault detection of Biological Systems

2019 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) ◽

10.1109/iintec48298.2019.9112139 ◽

2019 ◽

Author(s):

Radhia Fezai ◽

Majdi Mansouri ◽

Nasreddine Bouguila ◽

Hazem Nounou ◽

Mohamed Nounou

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Gaussian Process ◽

Biological Systems ◽

Gaussian Process Regression

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A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression

Journal of Navigation ◽

10.1017/s0373463315001034 ◽

2016 ◽

Vol 69 (4) ◽

pp. 905-919 ◽

Cited By ~ 10

Author(s):

Yixian Zhu ◽

Xianghong Cheng ◽

Lei Wang

Keyword(s):

Kalman Filter ◽

Fault Detection ◽

Gaussian Process ◽

Navigation System ◽

Detection Method ◽

Gaussian Process Regression ◽

Navigation Systems ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Chi Squared

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

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