Application of Adaptive Thresholds in Robust Fault Detection of an Electro-Mechanical Single-Wheel Steering Actuator

Ho, Lok Man

doi:10.3182/20120829-3-mx-2028.00150

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…This is why the threshold is required for responding to uncertainty. Many studies mainly focus on developing an adaptive threshold generator to reduce the modeling uncertainties or to enhance sensitivity by minimizing the residual [10,14,17]. In fact, they do not deal with the modeling uncertainties directly since the distribution of modeling errors is unknown.…”

Section: Problems By Data Analysismentioning

confidence: 99%

“…Furthermore, there are its own inherent risks because of the environment and model uncertainties, which are normally unknown and time-varying [9]. To address this problem regarding the static threshold and improve the detection performance such as low false positive alarms and high accuracy, there has been increasing interest in research in adopting an adaptive threshold to minimize model uncertainty in the model-based detection [10][11][12][13][14][15][16][17]. Several attempts have determined an adaptive threshold considering uncertainties bounded using intervals in the design of the state-observer [11,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Several attempts have determined an adaptive threshold considering uncertainties bounded using intervals in the design of the state-observer [11,15,16]. Some have developed a dynamic threshold generator and tried to minimize the residual in order to enhance sensitivity [10,14,17]. However, the effect of the modeling uncertainties on the system response is not captured by model parameters describing a physical system.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Baek

2018

Applied Sciences

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Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To enhance safety and security in CPS, a common and cost-effective strategy is to employ the model-based detection technique; however, detecting faults in practice is challenging due to model and environment uncertainties. In this paper, we present a novel generation method of the adaptive threshold required for providing dependability for the model-based fault detection system. In particular, we focus on statistical and information theoretic analysis to consider the model and environment uncertainties, and non-linear programming to determine an adaptive threshold as an equilibrium point in terms of adaptability and sensitivity. To do this, we assess the normality of the data obtained from real sensors, define performance measures representing the system requirements, and formulate the optimal threshold problem. In addition, in order to efficiently exploit the adaptive thresholds, we design the storage so that it is added to the basic structure of the model-based detection system. By executing the performance evaluation with various fault scenarios by varying intensities, duration and types of faults injected, we prove that the proposed method is well designed to cope with uncertainties. In particular, against noise faults, the proposed method shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. It also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. These results indicate that the proposed method achieves a significantly better performance than existing dynamic threshold methods. Consequently, an extensive performance evaluation demonstrates that the proposed method is able to accurately and reliably detect the faults and achieve high levels of adaptability and sensitivity, compared with other dynamic thresholds.

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Section: Problems By Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Baek

2018

Applied Sciences

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“…This is of paramount relevance to ensure system safety. Addressing these challenges require advances in fault management and fault tolerant control, both research topics in the RoboMobil project [7].…”

Section: Challenges and Contributionmentioning

confidence: 99%

Central Vehicle Dynamics Control of the Robotic Research Platform RoboMobil

Bünte¹,

Ho²,

Satzger³

et al. 2014

ATZ Elektron Worldw

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“…Robustness can be achieved either by active approaches or by passive ones [ 20 ]. Active approaches consider the desired robustness from the beginning of the design procedure [ 30 – 32 ], while passive approaches utilize adaptive thresholds architecture in decision making block to achieve the desired robustness [ 20 ]. In this paper, by utilizing a comprehensive nonlinear model of the WECS, an FDS is suggested which has the capability to detect faults in the generator's angular velocity sensor, pitch sensors, and pitch actuators.…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

Talebi

Sadrnia

Darabi

2014

Computational Intelligence and Neuroscience

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Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

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Application of Adaptive Thresholds in Robust Fault Detection of an Electro-Mechanical Single-Wheel Steering Actuator

Cited by 8 publications

References 6 publications

Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Central Vehicle Dynamics Control of the Robotic Research Platform RoboMobil

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

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