Publications

@conference{Houiji2018810b,

title = {Fault diagnosis and fault tolerant control against simultaneous sensor and actuator faults for linear stochastic systems},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060587807\&doi=10.1109%2fSSD.2018.8570481\&partnerID=40\&md5=2374c560f4bc8c459a08488691d1ba21},

doi = {10.1109/SSD.2018.8570481},

year  = {2018},

date = {2018-01-01},

journal = {2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018},

pages = {810 \textendash 815},

abstract = {This paper presents the problem of robust fault diagnosis and accommodation for a class of linear stochastic systems where simultaneous actuator and sensor faults may occur at a given time. Firstly, based on Augmented Robust Three stage Kalman filters (ARThSKF) we obtained both the fault estimation and the residual signal. Then, residual evaluation is achieved by making use of an adaptive threshold adjustment algorithm based on the grey theory. Obtained results show that the false-alarm rates and the missing alarm rates are minimized by the developed method; also this approach detects small or incipient faults more effectively than the classical robust fault detection algorithms with fixed thresholds. Finally, an additive control input is introduced for cancelling out the fault's effect on the system. We evaluate our proposal through simulation and we demonstrate its feasibility. © 2018 IEEE.},

note = {Cited by: 1},

keywords = {Actuator and sensor faults, Actuators, Adaptive threshold adjustments, Adaptive thresholds, ARThSK, Failure analysis, Fault accommodation, Fault detection, Linear stochastic system, Robust fault detection, Robust fault diagnosis, Stochastic control systems, Stochastic systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Houiji2018810,

title = {Fault diagnosis and fault tolerant control against simultaneous sensor and actuator faults for linear stochastic systems},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060587807\&doi=10.1109%2fSSD.2018.8570481\&partnerID=40\&md5=2374c560f4bc8c459a08488691d1ba21},

doi = {10.1109/SSD.2018.8570481},

year  = {2018},

date = {2018-01-01},

journal = {2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018},

pages = {810 \textendash 815},

abstract = {This paper presents the problem of robust fault diagnosis and accommodation for a class of linear stochastic systems where simultaneous actuator and sensor faults may occur at a given time. Firstly, based on Augmented Robust Three stage Kalman filters (ARThSKF) we obtained both the fault estimation and the residual signal. Then, residual evaluation is achieved by making use of an adaptive threshold adjustment algorithm based on the grey theory. Obtained results show that the false-alarm rates and the missing alarm rates are minimized by the developed method; also this approach detects small or incipient faults more effectively than the classical robust fault detection algorithms with fixed thresholds. Finally, an additive control input is introduced for cancelling out the fault's effect on the system. We evaluate our proposal through simulation and we demonstrate its feasibility. © 2018 IEEE.},

note = {Cited by: 1},

keywords = {Actuator and sensor faults, Actuators, Adaptive threshold adjustments, Adaptive thresholds, ARThSK, Failure analysis, Fault accommodation, Fault detection, Linear stochastic system, Robust fault detection, Robust fault diagnosis, Stochastic control systems, Stochastic systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Houiji2016229b,

title = {Fault detection performances analysis for stochastic systems based on adaptive threshold},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974575297\&doi=10.1109%2fSSD.2016.7473701\&partnerID=40\&md5=0f9cdd28ba3852316e53997164690401},

doi = {10.1109/SSD.2016.7473701},

year  = {2016},

date = {2016-01-01},

journal = {13th International Multi-Conference on Systems, Signals and Devices, SSD 2016},

pages = {229 \textendash 234},

abstract = {This paper investigates the problem of fault detection for discrete linear systems subjected to unknown disturbances, actuator and sensor faults. A bank of Augmented Robust Three stage Kalman filters is adapted to estimate both the state and the fault as well as to generate the residuals. Besides, this paper presents the evaluation of the residuals with Bayes test of binary hypothesis test for fault detection to adaptive threshold compared with fixed threshold. This test allow the detection of low magnitude faults as fast as possible with a minimum risk of errors, the increase of detection probability and the reduction of false alarm probability. © 2016 IEEE.},

note = {Cited by: 2},

keywords = {Actuator and sensor faults, Adaptive thresholds, Binary hypothesis tests, Detection delays, Detection performance, Detection probabilities, Discrete linear systems, False alarm probability, Fault detection, Linear systems, Signal detection, Stochastic systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Houiji2014b,

title = {Detection time for deterministic and stochastic systems with unknown inputs},

author = {Marwa Houiji and Rim Hamdaoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946686815\&doi=10.1109%2fCISTEM.2014.7076982\&partnerID=40\&md5=0be64cd709ae198f91a4d2b9aecc2dae},

doi = {10.1109/CISTEM.2014.7076982},

year  = {2014},

date = {2014-01-01},

journal = {2014 International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2014},

abstract = {This paper investigates the detection and diagnosis of actuator faults by using an Unknown Input Observer (UIO).The proposed UIO design guarantees robust residual generation through decoupling the disturbances effects from the faults ones. The discrimination between the faults and the effects of uncertain signals and perturbations on the residues minimizes the duration of fault detection for deterministic systems. Then these results were extended to the general case of stochastic linear systems by using an optimal observers for systems with unknown disturbances and noise. A simulation is done on an aeronautic model to illustrate the theoretical development. © 2014 IEEE.},

note = {Cited by: 1},

keywords = {Detection and diagnosis, Deterministic systems, Fault detection, Linear systems, Optimal observers, Robust residuals, Stochastic linear systems, Stochastic systems, Theoretical development, Unknown disturbance, Unknown input observer},

pubstate = {published},

tppubtype = {conference}

}