Real-Time Model-Based Fault Detection and Isolation for UGVs

Monteriù, Andrea; Asthana, Prateek; Valavanis, Kimon P.; Longhi, Sauro

doi:10.1007/s10846-009-9321-2

Cited by 22 publications

(6 citation statements)

References 26 publications

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“…A threshold policy must be then used, to achieve a robust fault detection. Robustness is achieved using adaptive thresholds [15]. These proposed moving thresholds are built using a linear combination of mean value, variance and a constant value updated on a moving window, which must be tuned according to the application using a trial and error approach.…”

Section: Residual Evaluationmentioning

confidence: 99%

A Diagnostic Thau Observer for a Class of Unmanned Vehicles

Freddi

Longhi

Monteriù

2012

J Intell Robot Syst

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This paper addresses the problem of sensor fault detection for a wide class of Unmanned Vehicles (UVs). First a general model for UVs, based on the dynamics of a 6 Degrees Of Freedom (6-DOF) rigid body, subject to gravity and actuation forces, is presented. This model is shown to satisfy the necessary conditions to the existence of a non-linear observer (Thau) when proper assumptions for the actuation forces are made. The observer can thus be used to generate diagnostic residuals inside a Fault Detection (FD) system. Finally, the proposed approach is customized for sensor fault detection on an unmanned quad-rotor vehicle, and simulation results show the effectiveness of the adopted solution

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Section: Residual Evaluationmentioning

confidence: 99%

A Diagnostic Thau Observer for a Class of Unmanned Vehicles

Freddi

Longhi

Monteriù

2012

J Intell Robot Syst

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“…Model-based methods are more economical and efficient as they do not require one to process massive datasets. An example is demonstrated in [26], which shows that with accurate fault information, the corresponding FTC could be implemented precisely and immediately when faults occur. In some papers, nonlinear adaptive thresholds are designed to enhance the robustness and fault sensitivity of the properties FDI algorithm [27,28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Observer-Based Fault Detection and Fault-Tolerant Control of Quadrotors under Rotor Failure Conditions

2020

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This paper aims to propose a strategy for the flight control of quad-rotors under single rotor failure conditions. The proposed control strategy consists of two stages—fault detection (FD) and fault tolerant control (FTC). A dual observer-based strategy for FD and fault estimation is developed. With the combination of the results from both observers, the decision making in whether a fault actually happened or the observed anomaly was caused by an external disturbance could be distinguished. Following the FD result, a control strategy for normal flight, as well as the abnormal one, is presented. The FTC considers a real-time coordinate transformation scheme to manipulate the target angles for the quad-rotor to follow a prescribed trajectory. When a rotor fault happens, it is going to be detected by the dual observers and then the FTC is activated to stabilize the system such that the trajectory following task can still be fulfilled. Furthermore, in order to achieve robust flight in the presence of external wind perturbation, the sliding mode control (SMC) theory is further integrated. Simulations illustrate the effectiveness and feasibility of the proposed method.

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“…12,13 For instance, the state chi-square test (SCST) is the most classic method for an integrated navigation system that detects the fault through a constructed statistic between measurements and predictions of the recursion filter. Monteriu et al 13 presented a model-based multiple sensors fault detection and isolation (FDI) by using the “structural analysis” that includes the residual generation and ad hoc residual evaluation for unmanned ground vehicles (UGVs). Moreover, H ∞ estimation is another effective way for the navigation system FD by the residual generator design.…”

Section: Introductionmentioning

confidence: 99%

“…However, analytical methods require lots of prior knowledge such as accurate parameters of the dynamic model. 12,13 For instance, the state chi-square test (SCST) is the most classic method for an integrated navigation system that detects the fault through a constructed statistic between measurements and predictions of the recursion filter. Monteriu et al 13 presented a model-based multiple sensors fault detection and isolation (FDI) by using the ''structural analysis'' that includes the residual generation and ad hoc residual evaluation for unmanned ground vehicles (UGVs).…”

Section: Introductionmentioning

confidence: 99%

“…gyroscopes, accelerometers, and global positioning system (GPS) receiver) at stable performance and monitor the corresponding estimation states, many FD strategies have been proposed so far by the researchers. 8–18 Conventional model-based approaches and data-driven approaches were proposed accordingly for intelligent FD and fault diagnosis. 8 Moreover, considering extensive applications of unmanned operation systems and complicated integrated navigation systems, finding a reliable real-time FD method is of great importance.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid data-driven fault detection strategy with application to navigation sensors

Yang¹,

Chen²,

Wang³

2020

Measurement and Control

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The integrated navigation system highly relies on the accuracy of measurements of sensors that are susceptible to unknown disturbances. In order to improve the reliability and safety of the navigation system, there is an increasing need for the fault detection of the sensors. In the present study, a hybrid data-driven fault detection strategy is proposed, which is based on residual sequence analysis. Currently, the one-class support vector machine is one of the most popular fault detection methods for navigation systems with many successful cases. Therefore, the one-class support vector machine is combined with time-series similarity measure and modified principal components analysis approaches. The similarity measurement of multi-sequence residuals between a real-time sample and normal condition samples is computed to construct learning features for one-class support vector machine. Similarly, the modified principal components analysis scheme is applied to project residuals onto subspaces and obtain learning features. Moreover, the one-class support vector machine model is applied for abnormal detection if unexpected sensor faults exhibit in measurements and residuals. Finally, experiments are carried out to evaluate the performance of the proposed strategy for abrupt faults and soft faults on navigation sensors. Experimental results show that the hybrid data-driven fault detection strategy can effectively detect these faults with short time delay and high accuracy.

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Real-Time Model-Based Fault Detection and Isolation for UGVs

Cited by 22 publications

References 26 publications

A Diagnostic Thau Observer for a Class of Unmanned Vehicles

A Diagnostic Thau Observer for a Class of Unmanned Vehicles

Adaptive Observer-Based Fault Detection and Fault-Tolerant Control of Quadrotors under Rotor Failure Conditions

A hybrid data-driven fault detection strategy with application to navigation sensors

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