Fault Location for the Intermittent Connection Problems on CAN Networks

Lei, Yong; Xie, Huimin; Yuan, Y.F.; Chang, Qing

doi:10.1109/tie.2015.2442518

Cited by 13 publications

(10 citation statements)

References 16 publications

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“…IIoT / ICPS: [96]- [127] WSAN: [128]- [151] NCS: [152]- [162] Industrial Robots: [163]- [192] Assembly Line: [193]- [221] M2M communication: [222]- [257] Data Centric Industrial Services AR / VR: [258], [259] Camera / Vision: [260]- [262] Prognostics: [263]- [268] Anomalies Detection: [269]- [273] Fault Diagnosis: [274]- [280] Multi-Agent Systems: [281]- [289] Decision Making: [290]- [292] Job Scheduling: [293]- [301] Machine Learning [302]- [314] Big Data Analytics: [315]- [324] Ontologies / Semantics: [325]- [345] Human-in-the-loop: [346]- [352] Security: [353]- [363] Energy Management: [364]- [399] Cloud: [400]- [409] Fig. 9.…”

Section: Data Enabling Industrial Technologiesmentioning

confidence: 99%

“…5) Fault diagnosis: Fault detection, isolation and reconstruction methods are essential to improve the reliability, safety of the automatic control systems. In [274], the authors develop a model-based fault location method is developed for intermittent connection problems on controller area networks. In this type of networks time critical data are transmitted, hence, the reliability of the network not only has a direct impact on the system performance but also affects the safety of the system operations.…”

Section: B Data Centric Industrial Services 1) Ar / Vrmentioning

confidence: 99%

See 1 more Smart Citation

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Data Enabling Industrial Technologiesmentioning

confidence: 99%

Section: B Data Centric Industrial Services 1) Ar / Vrmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…Since permanent faults will not disappear once they occur, they will not give intermittent symptoms. Intermittent faults are common problems in electronics interconnection systems (wires and connectors), especially for autonomous vehicles, aircrafts, and satellites [21]. Detecting intermittent fault is challenging and frustrating due to its random and unpredictable nature [22].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is critical to detect, isolate, and estimate the intermittent faults soon enough such that preventive maintenance can be taken in a timely manner, which ultimately improves the system reliability [23]. In recent years, fault diagnoses of intermittent faults have been widely investigated [21][22][23][24][25][26]. In [24], a chaotic spread spectrum sequence based method is developed for synchronous online diagnosis of intermittent faults in power cables.…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties

Wang

et al. 2020

Actuators

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This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results.

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“…To tackle the challenges, a dynamic ensemble selection system (DESS) that guarantees detection performance and robustness is proposed. The DESS for anomaly detection method consists of four successive steps: first, since the physical-layer of the CAN protocol contains most of the network performance and failure information [10]- [13], feature sets extracted from the physical-layer information are divided into separate training, validation, and testing sets in different fault types. Second, multiple base classifiers are generated, and the individual output and support function are adjusted for the more advanced combining rule.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Controller Area Network in Braking Control System With Dynamic Ensemble Selection

Yang

Wang

et al. 2019

IEEE Access

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The controller area networks (CAN) in the braking control system of metro trains are used to transmit the important control instruction and condition information, whose anomaly will endanger the security of trains running seriously. Due to the harsh work environment, there are various known and previously unknown fault types, current scheduled maintenance cannot detect early anomaly in time, and constructing an accurate and stable anomaly detector is a challenging task. In this paper, an anomaly detection approach is proposed to detect anomaly based on a dynamic ensemble selection system (DESS) without the expert knowledge, which involves two-class and one-class classifiers, and the base classifiers are trained with the network features extracted from the physical-layer information. To conduct the fusion, the support function of ''distance-based'' classifier is redefined as a class-conditional probability density function, and the source competence of base classifier is estimated by the entropy-based method in validate space and extended to entire decision space using the normalized Gaussian potential function. For different fault types, the competence classifiers are selected and the anomaly detection result is finally achieved by weighted majority voting. The comparative experiments are included in this paper to demonstrate the effectiveness and robustness in anomaly detection, including varying fault types.

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Fault Location for the Intermittent Connection Problems on CAN Networks

Cited by 13 publications

References 16 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties

Anomaly Detection for Controller Area Network in Braking Control System With Dynamic Ensemble Selection

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