Parameter estimation based fault diagnosis of uncertain nonlinear three tank system using HOSM differentiator observer

Iqbal, M. Tariq; Bhatti, Aamer Iqbal; Iqbal, Sohail; Khan, Qudrat

doi:10.1109/inmic.2009.5383150

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The fusion architecture proposed here is supplied with real data from a mobile robot and assessed using two diagnosers: one based on HOSM [25,26], and the other based on the proposed extension to a predefined-time observer.…”

Section: Resultsmentioning

confidence: 99%

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

Begovich,

Lizárraga,

Ramírez-Treviño

2024

Algorithms

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This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system’s redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system’s dynamics—the input observer and the system model—which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheeled omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach’s efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults.

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Section: Resultsmentioning

confidence: 99%

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

Begovich,

Lizárraga,

Ramírez-Treviño

2024

Algorithms

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“…The unavailable system parameters can disturb the control system performance, the diagnostic algorithms, and degrade the overall system reliability and safety. Such abnormalities when occurred occasionally make the system faults occur [6].…”

Section: Introductionmentioning

confidence: 99%

Three Tank System Sensors and Actuators Faults Detection Employing Unscented Kalman Filter

Sayed

Aboelhassan

Hebala

et al. 2021

2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC)

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Fault detection is critical for industrial applications to maintain a stable operation and to reduce maintenance costs. Many fault detection techniques have been introduced recently to cope with the increasing demand for more safe operations. One of the most promising fault detection algorithms is the Unscented Kalman Filter (UKF). UKF is a model-based algorithm that could be used to detect different fault types for a given system. On the other hand, the three-tank system is a well-known benchmark that simulates many industrial applications. The fault detection of the three-tank system is quite challenging as it is a Multi-Input Multi-Output (MIMO) nonlinear system. Therefore, UKF will be employed as a fault detection strategy for this system to detect sensor and actuator faults. The performance of the UKF will be investigated under different operating and fault conditions to show its merits for the given case study.

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Modelling and Diagnosis of Hybrid Dynamic Systems by a Multi-Model Approach

Maaref,

Dhouibi,

Simeu-Abazi

et al. 2024

IJMEA

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Fault diagnosis is an essential task in ensuring the smooth operation of complex dynamic systems. The consequences of faults can be serious, leading to loss of life, harmful emissions to the environment, high repair costs and economic losses caused by unplanned production line stoppages. The work developed in this paper concerns the modeling and diagnosis of faults (sensor faults, system faults, actuator faults) in hybrid dynamic systems using our multi-model approach (which combines two sub-models, one continuous and the other discrete). The aim is to integrate three well-known tools in the literature: the Bond Graph, the Observer and the Timed Automata, to design a global diagnostic model. The hybrid dynamic system is modeled by connecting the tools for the continuous part, i.e. the bond graph and the observer, to the timed automata for the discrete part. The resulting model is used for fault diagnosis in two stages: The first is fault detection by analyzing the residuals generated by the system output and that of the observer. The second step involves fault localization, which results from analysis of the signature matrix and temporal identification of the system. The proposed method combines the advantages of these tools to obtain the best performance, particularly in the fault location phase. The simulation results prove the effectiveness of the proposed model for the hybrid dynamic system. Moreover, these results also evaluate the performance of the proposed diagnostic approach while reducing non-detections, detection delays and false alarms.

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Parameter estimation based fault diagnosis of uncertain nonlinear three tank system using HOSM differentiator observer

Cited by 3 publications

References 25 publications

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

Three Tank System Sensors and Actuators Faults Detection Employing Unscented Kalman Filter

Modelling and Diagnosis of Hybrid Dynamic Systems by a Multi-Model Approach

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