Sensor Fault Isolation in a Liquid Flow Process Using Kalman Filter

Investigation of the effect of sensor fault in liquid process loop and designing a suitable fault isolation technique using the Kalman filter is proposed in this work. The objective of the work is to design an observer to estimate the process parameters, so as to compare with sensor functionality and thus identify and isolate faults in sensor. Based on the derived system model, observer is designed using Kalman filter approach. The proposed system with observer is subject to test in simulation and validated using physical system. The designed observer was successfully able to isolate drift, short circuit and open circuit faults in sensor. Practical validation shows the system with observer was able to track the set point with the root mean square of 0.986% error, even with faulty sensor.

[1]  K. Santhosh.  A technique for measuring quantity of oil in a flowing water-oil mixture , 2019 .

[2]  Robert Miklos,et al.  A Simple Method for Estimation of Parameters in First order Systems , 2014 .

[3]  Faisal Khan,et al.  Process system fault detection and diagnosis using a hybrid technique , 2018, Chemical Engineering Science.

[4]  Amar Khoukhi,et al.  Fault detection and classification using Kalman filter and genetic neuro-fuzzy systems , 2011, 2011 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  Wigbert Fehse,et al.  Automated Rendezvous and Docking of Spacecraft , 2003 .

[6]  Haris M. Khalid,et al.  Fault Modeling, Detection and Classification using Fuzzy Logic, Kalman Filter and Genetic Neuro-Fuzzy Systems , 2011 .

[7]  Stephen Kemble,et al.  Automated Rendezvous and Docking of Spacecraft , 2007 .

[8]  José Francisco Gómez-Aguilar,et al.  Experimental validation of an actuator fault tolerant control system using virtual sensor: Application in a double pipe heat exchanger , 2015 .

[9]  Jie Zhang,et al.  Actuator fault monitoring and fault tolerant control in distillation columns , 2015, ICAC.

[10]  Alireza Yazdizadeh,et al.  Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR) , 2017, Pattern Analysis and Applications.

[11]  Amar Khoukhi,et al.  Hybrid computing techniques for fault detection and isolation, a review , 2015, Comput. Electr. Eng..

[12]  Cao Yuyan,et al.  Fuzzy dynamic fault tree analysis for electro-mechanical actuator based on algebraic model with common-cause failures , 2016 .

[13]  Marco Sorrentino,et al.  A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems , 2017 .

[14]  Celso J. Munaro,et al.  Isolation of plant-wide faults using causality detection methods , 2016 .

[15]  Ciencias Aplicadas-Universidad,et al.  Sensor fault compensation via software sensors: Application in a heat pump's helical evaporator , 2014 .

[16]  Adrián Rodríguez Ramos,et al.  An approach to multiple fault diagnosis using fuzzy logic , 2019, J. Intell. Manuf..

[17]  M A López Castro,et al.  Sensor fault detection and isolation system for a condensation process. , 2016, ISA transactions.

[18]  Junghui Chen,et al.  Data-driven sensor fault diagnosis systems for linear feedback control loops , 2017 .

[19]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[20]  Qibing Jin,et al.  Fault isolation based on residual evaluation and contribution analysis , 2017, J. Frankl. Inst..