Fault Detection of Complex Processes Using nonlinear Mean Function Based Gaussian Process Regression: Application to the Tennessee Eastman Process

2020 ◽  
Author(s):  
Avinash Maran Beena ◽  
Ajaya Kumar Pani
Keyword(s):  
Fault Detection ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Tennessee Eastman Process ◽  
Complex Processes ◽  
Mean Function

scholarly journals Gaussian process regression for monitoring and fault detection of wastewater treatment processes

2017 ◽  
Vol 75 (12) ◽  
pp. 2952-2963 ◽  
Author(s):  
Oscar Samuelsson ◽  
Anders Björk ◽  
Jesús Zambrano ◽  
Bengt Carlsson
Keyword(s):  
Wastewater Treatment ◽  
Missing Data ◽  
Fault Detection ◽  
Gaussian Process ◽  
Flow Rate ◽  
Sequential Monte Carlo ◽  
Estimation Method ◽  
Gaussian Process Regression ◽  
Local Optima ◽  
Efficient Operation

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.

scholarly journals Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Haopeng Zhang ◽  
Cong Zhang ◽  
Zhiguo Jiang ◽  
Yuan Yao ◽  
Gang Meng
Keyword(s):  
Gaussian Process ◽  
Pose Estimation ◽  
Gaussian Process Regression ◽  
Regression Function ◽  
Mean Value ◽  
Training Data ◽  
Simulated Image ◽  
Lighting Conditions ◽  
Mean Function ◽  
Imaging Sensors

In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

scholarly journals An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

2018 ◽  
Vol 71 (5) ◽  
pp. 1055-1068 ◽  
Author(s):  
Jianli Zhao ◽  
Xiang Gao ◽  
Xin Wang ◽  
Chunxiu Li ◽  
Min Song ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Gaussian Process ◽  
Predictive Model ◽  
Gaussian Process Regression ◽  
Environment Changes ◽  
Model Based ◽  
Map Updating ◽  
Mean Function ◽  
Radio Map ◽  
Indoor Localisation

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.

scholarly journals Improving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: utilizing Gaussian process regression with a new method to estimate dynamic input uncertainty

2019 ◽  
Vol 12 (2) ◽  
pp. 935-953 ◽  
Author(s):  
Maosi Chen ◽  
Zhibin Sun ◽  
John M. Davis ◽  
Yan-An Liu ◽  
Chelsea A. Corr ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Average Method ◽  
Moving Average ◽  
Gaussian Process Regression ◽  
Input Uncertainty ◽  
In Situ Calibration ◽  
The Mean ◽  
Mean Function

Abstract. To recover the actual responsivity for the Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g., unstable, noisy, and with gaps) time series of its in situ calibration factors (V0) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian process (GP) regression provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied to a synthetic signal and showed significantly smaller RMSEs than a Gaussian process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR V0 time series at three ground sites. The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results at the three test sites (i.e., HI02 at Mauna Loa, Hawaii; IL02 at Bondville, Illinois; and OK02 at Billings, Oklahoma) demonstrated that the agreement among aerosol optical depths (AODs) at the 368 nm channel calculated using V0 determined by the GP mean function and the equivalent AERONET AODs were consistently better than those calculated using V0 from standard techniques (e.g., moving average). For example, the average AOD biases of the GP method (0.0036 and 0.0032) are much lower than those of the moving average method (0.0119 and 0.0119) at IL02 and OK02, respectively. The GP method's absolute differences between UV-MFRSR and AERONET AOD values are approximately 4.5 %, 21.6 %, and 16.0 % lower than those of the moving average method at HI02, IL02, and OK02, respectively. The improved accuracy of in situ UVMRP V0 values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.

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

2016 ◽  
Vol 69 (4) ◽  
pp. 905-919 ◽  
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.

Automatic fault detection in seismic data using Gaussian process regression

2019 ◽  
Vol 163 ◽  
pp. 117-131 ◽  
Author(s):  
Maryam Noori ◽  
Hossein Hassani ◽  
Abdolrahim Javaherian ◽  
Hamidreza Amindavar ◽  
Siyavash Torabi
Keyword(s):  
Fault Detection ◽  
Gaussian Process ◽  
Seismic Data ◽  
Gaussian Process Regression
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