Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults

Many signals of interest are corrupted by faults of an unknown type. We propose an approach that uses Gaussian processes and a general "fault bucket" to capture a priori uncharacterised faults, along with an approximate method for marginalising the potential faultiness of all observations. This gives rise to an efficient, flexible algorithm for the detection and automatic correction of faults. Our method is deployed in the domain of water monitoring and management, where it is able to solve several fault detection, correction, and prediction problems. The method works well despite the fact that the data is plagued with numerous difficulties, including missing observations, multiple discontinuities, nonlinearity and many unanticipated types of fault.

[1]  Richard J. Wagner,et al.  Guidelines and Standard Procedures for Continuous Water-Quality Monitors: Station Operation, Record Computation, and Data Reporting , 2014 .

[2]  W. Press,et al.  Numerical Recipes in Fortran: The Art of Scientific Computing.@@@Numerical Recipes in C: The Art of Scientific Computing. , 1994 .

[3]  Jonathan S. Maltz,et al.  NEURAL NETWORKS FOR PNEUMATIC ACTUATOR FAULT DETECTION , 1999 .

[4]  Rolf Isermann.  Model-based fault-detection and diagnosis - status and applications § , 2004 .

[5]  J. Hespanha,et al.  Forecasting COVID-19 cases based on a parameter-varying stochastic SIR model , 2019, Annual Reviews in Control.

[6]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[7]  Thomas G. Dietterich,et al.  Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns , 2011, TOSN.

[8]  Carl E. Rasmussen,et al.  Bayesian Monte Carlo , 2002, NIPS.

[9]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[10]  Thomas G. Dietterich.  Adaptive computation and machine learning , 1998 .

[11]  Michael A. Osborne.  Bayesian Gaussian processes for sequential prediction, optimisation and quadrature , 2010 .

[12]  Shehroz S. Khan,et al.  A Survey of Recent Trends in One Class Classification , 2009, AICS.

[13]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[14]  Steven Reece,et al.  Sequential Bayesian Prediction in the Presence of Changepoints and Faults , 2010, Comput. J..

[15]  Aki Vehtari,et al.  Gaussian process regression with Student-t likelihood , 2009, NIPS.

[16]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[17]  William H. Press,et al.  Numerical Recipes in C The Art of Scientific Computing , 1995 .

[18]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[19]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[20]  Sean B. Holden,et al.  Robust Regression with Twinned Gaussian Processes , 2007, NIPS.

[21]  Sarvapali D. Ramchurn,et al.  2008 International Conference on Information Processing in Sensor Networks Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes , 2022 .

[22]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[23]  Kevin Murphy,et al.  Switching Kalman Filters , 1998 .

[24]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Fault Detection , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[25]  Wolfram Burgard,et al.  Most likely heteroscedastic Gaussian process regression , 2007, ICML '07.