DeviceNet network health monitoring using physical layer parameters

Lei, Yong; Djurdjanović, Dragan; Barajas, Leandro G.; Workman, Gary L.; Biller, Stephan; Ni, Jun

doi:10.1007/s10845-009-0291-9

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…The features extracted from the physical layer signals in3 can be grouped into three categories. Dominant state features.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In reality, as indicated in Reference1, the reliability of a network has a great and profound impact on the performance of the networked automation system. The reliability of an industrial network can be affected by several factors including the electromagnetic interferences (EMI), cable defects, network bandwidth overuse as well as other factors2, 3. The growing complexity of distributed automation systems has led to the need to develop network health monitoring tools for network reliability assessment.…”

Section: Introductionmentioning

confidence: 99%

“…It usually does not provide fully the information on individual node performance and how a node affects the overall performance. For example, when the network has an intermittent connection problem, which is one of the most frequent and impacting failure modes observed in industrial networks, the network errors are induced by unreliable connections between the field device and the network backbone3. In this case, using BER could only indicate that the network experiences errors, but could not indicate which node causes the problems and how long the network nodes can sustain.…”

Section: Introductionmentioning

confidence: 99%

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DeviceNet reliability assessment using physical and data link layer parameters

Lei

Djurdjanović

2010

Quality & Reliability Eng

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Since the 1990s, the increasing deployments of networked automation systems led to increased manufacturing productivity, improved interchangeability of devices from different vendors, facilitated flexibility and reconfigurability for various applications and improved reliability, while reducing installation and maintenance costs. However, the reliability of a network has great impact on the reliability of a networked automation system. This paper presents a novel network reliability assessment method that provides diagnostic and prognostic information for DeviceNet. This work proposes a hybrid network error analysis method using combined physical and datalink layer features to provide complete communication log information. Furthermore, a network/node time to failure (bus-off) prediction algorithm was developed based on the analysis of the patterns of the interrupted packets on the network. The method developed in this study can be used for network reliability evaluation and diagnosis, facilitating better network maintenance decision making. A laboratory testbed was constructed and the experiments on network and node time to failure were conducted to demonstrate the concept. Experimental results show that the proposed method can fully reconstruct the communication log, and predict the network/node bus-off time successfully.

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“…The features extracted from the physical layer signals in3 can be grouped into three categories. Dominant state features.…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeviceNet reliability assessment using physical and data link layer parameters

Lei

Djurdjanović

2010

Quality & Reliability Eng

Self Cite

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“…Lei et al . have studied the health evaluation method of the controller area network (CAN) . A gaussian mixture model has been built based on the waveform features sampled from the normal condition.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of the train communication network based on weighted support vector machine

Wang

Yang

2020

IEEJ Transactions Elec Engng

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Multifunction vehicle bus (MVB) is the most widely used train communication network which transmits controlling and supervising data. The faults of MVB will heavily affect the train's safe and stable operation. Due to the harsh operating environment and distributed structure, the MVB fault diagnosis has always been a difficult issue in the maintenance of the train. Many MVB faults will distort the physical waveforms and cause serious packet loss. Thus, we have extracted waveform features to characterize different MVB faults and turned the fault diagnosis into a pattern recognition problem. Then a classifier based on weighted support vector machine (WSVM) has been trained to diagnose and locate network faults. Considering that samples locating in different positions of the feature space have different influences on the support vector machine (SVM) hyperplane, we have proposed a multi‐hop edge approaching method to assign sample weights in WSVM. To identify the position of the tested sample, the hops from its location to the classification margin have been counted. The less the hop‐count, the closer to the classification margin and the larger the sample weight. Compared with SVM and other WSVM methods, the proposed method has better performance on the artificial synthetic datasets, the MVB dataset, and the benchmark datasets. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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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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DeviceNet network health monitoring using physical layer parameters

Cited by 15 publications

References 18 publications

DeviceNet reliability assessment using physical and data link layer parameters

DeviceNet reliability assessment using physical and data link layer parameters

Fault diagnosis of the train communication network based on weighted support vector machine

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

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