Diagnosis of hybrid systems: Part 2- residual generator selection and diagnosis in the presence of unreliable residual generators

Mohammadi, R.; Hashtrudi-Zad, S.; Khorasani, K.

doi:10.1109/icsmc.2009.5346205

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the use of hardware redundancy is very expensive. FDI techniques based on faut detection observer were proposed [10][11][12] as the fault detection observer design based on the H ∞ /H index criterion [13], [14]. Furthermore, Kalman filters were used in model-based fault diagnosis [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman observer based sensor fault detection

Hatem¹,

Mostefai²,

Aktouf³

2019

IJECE

View full text Add to dashboard Cite

<p>This article discusses the Kalman observer based fault detection approach. The calculation of the residues can detect faults, but if there are noises, uncertainties become very important. To reduce the influence of these noises, a calculation of the instantaneous energy of the residues gave a better precision. The Kalman observer was used to estimate system performance and eliminate unknown noise and external disturbances. Instantaneous Power Calculation (IPCFD) based fault detection can detect potential sensor faults in hybrid systems. The effectiveness of the proposed approach is illustrated by the main application.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Extended Kalman observer based sensor fault detection

Hatem¹,

Mostefai²,

Aktouf³

2019

IJECE

View full text Add to dashboard Cite

show abstract

“…Generally, procedures for diagnosing hybrid systems are designed using extended observers that estimate the system state under normal and fault conditions [3][4][5]. Researchers describe systems using hybrid automata [6][7][8], Petri nets [9][10][11][12], or switched systems [13,14] and make a diagnosis according to values measured from a real system. Alternatively, they design an observer [15][16][17][18][19] to describe a system [19], estimate a system's status (failure or not) [18], or improve the stability [14].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

Ma¹,

Guo

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that the proposed method could identify the known and unknown failure events of the studied system. In particular, for a system with less response information under a failure event, the accuracy of diagnosis seems to be higher. The presented method integrates the advantages of current quantitative and qualitative diagnostic procedures and can distinguish between failures caused by parametric and abrupt structure faults. Another advantage of our method is that it can remember unknown failure types and automatically extend the adaptive resonance theory neural network library, which is extremely useful for complex hybrid systems.

show abstract

“…We introduce a notion of diagnosability in hybrid automata and show that a failure mode is diagnosable in a hybrid automaton if it is diagnosable in the extended DES model. In [8], we study the problem of residual generator selection in hybrid automata. We also investigate fault diagnosis for the cases that some residual generators generate incorrect output.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of hybrid systems: Part 1- diagnosability

Mohammadi

Hashtrudi-Zad

Khorasani

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

Self Cite

View full text Add to dashboard Cite

In this paper, we investigate fault diagnosis and diagnosability in hybrid systems modeled by hybrid automata. Generally, in hybrid systems, there are discrete sensors generating discrete outputs available at the discrete-event system representation of the system and continuous sensors generating continuous outputs available at the continuous dynamics. We assume that there is a bank of residual generators (using continuous sensors) designed for the continuous dynamics of the system. We present a hybrid diagnosis approach in which faults are diagnosed by integrating the information generated by the residual generators and the information at the discrete-event system representation of the system. We investigate the diagnosability of faults in the hybrid diagnosis framework.Index Terms-Fault diagnosis, hybrid systems, finite-state automata, diagnosability.

show abstract

Diagnosis of hybrid systems: Part 2- residual generator selection and diagnosis in the presence of unreliable residual generators

Cited by 5 publications

References 8 publications

Extended Kalman observer based sensor fault detection

Extended Kalman observer based sensor fault detection

Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

Diagnosis of hybrid systems: Part 1- diagnosability

Contact Info

Product

Resources

About