Sensor Fault Detection and Signal Restoration in Intelligent Vehicles

Byun, Yeun-Sub; Kim, Baek-Hyun; Jeong, Rag-Gyo

doi:10.3390/s19153306

Cited by 12 publications

(11 citation statements)

References 38 publications

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“… Fail-x systems and fault diagnosis (3) Fail-x vehicle positioning (2): [ 29 , 30 ]. Fault diagnosis of vehicle motion sensors (1): [ 31 ]. Vehicle positioning and path planning (5) Simultaneous Localization and Mapping (1): [ 32 ].…”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

“…Fail-x systems and fault diagnosis (3) Fail-x vehicle positioning (2): [ 29 , 30 ]. Fault diagnosis of vehicle motion sensors (1): [ 31 ]. …”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

“…Sensor fault diagnosis is a necessary step to design fail-x systems. In [ 31 ] a fault diagnosis logic and signal restoration algorithms for vehicle motion sensors are presented. The primary idea of the proposed fault detection system is the conversion of measured wheel speeds into vehicle central axis information and the selection of a reference central axis speed based on this information.…”

Section: Fail-x Systems and Fault Diagnosismentioning

confidence: 99%

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Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

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Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

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Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

“…Fail-x systems and fault diagnosis (3) Fail-x vehicle positioning (2): [ 29 , 30 ]. Fault diagnosis of vehicle motion sensors (1): [ 31 ]. …”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

Section: Fail-x Systems and Fault Diagnosismentioning

confidence: 99%

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Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

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“…Early sensor fault diagnosis methods using deviations are usually effective, but excessive use of sensors will increase the cost and make the system more complicated [2]. In addition, although both quantitative analysis and qualitative analysis can realize sensor fault diagnosis [3]- [9], as modern control systems have become more versatile and fault types have become more complex, the previous methods could not meet the current requirements anymore. In recent years, the development of computer technology has provided a new theoretical basis for fault diagnosis technology [2].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Diagnosis Based on Adaptive Arc Fuzzy DBN-Petri Net

Zhao

Cheng

2021

IEEE Access

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The adjustable parameters of the traditional fuzzy Petri net (FPN) are single and mostly depend on expert experience. This approach lacks the adaptability to the complex network of sensors, which will result in insufficient accuracy of fault diagnosis. We propose a method combining the FPN with an adaptive arc and deep belief network (DBN) and improved a fast Gibbs sampling (FGS) algorithm to realize sensor fault diagnosis. First, we present the concept of adaptive arcs with label-weights based on the confidence-weights of directed arcs, which is an important component of the sensor fault model. Then, the improved FGS algorithm optimizes the model layer-by-layer, and the adjustment of the transition threshold relies on the marginal distribution of a restricted Boltzmann machine (RBM). Finally, the optimized dualweights and dual-transition influence factors are applied to the forward and backward fuzzy reasoning of the model to achieve network adaptability. Our studies showed that this method has obvious advantages in terms of the accuracy and adaptability of complex networks compared to other FPN fault diagnosis methods. The fault reasoning confidence can provide an effective reference for maintenance personnel and improve maintenance efficiency, ensuring the reliable operation of sensors and related systems. INDEX TERMS Adaptive arc, fuzzy Petri net, deep belief network, fast Gibbs sampling, sensor fault diagnosis.

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“…The fault-tolerant robust non-fragile

filtering problem for networked control systems with sensor failures is studied in [ 19 ]. Finally, vehicle motion sensors failure is detected and recovered in [ 20 ], where the authors show that the fault detection and restoration algorithm can satisfy the control performance for each sensor failure. In addition, there exists proposals in the literature based on using classical machine learning approaches to process sensor arrays values, like in [ 8 , 10 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks

Ortega

González-Prieto

Bobadilla

et al. 2020

Sensors

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Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods.

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Sensor Fault Detection and Signal Restoration in Intelligent Vehicles

Cited by 12 publications

References 38 publications

Sensors and Sensing for Intelligent Vehicles

Sensors and Sensing for Intelligent Vehicles

Sensor Fault Diagnosis Based on Adaptive Arc Fuzzy DBN-Petri Net

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks

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