Pub Date : 2020-07-20DOI: 10.1109/indin45582.2020.9442137
{"title":"Artificial Intelligence in Industrial Applications","authors":"","doi":"10.1109/indin45582.2020.9442137","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442137","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133194721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9478915
G. Funchal, T. Pedrosa, Marco V. B. A. Vallim, P. Leitão
One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.
{"title":"Security for a Multi-Agent Cyber-Physical Conveyor System using Machine Learning","authors":"G. Funchal, T. Pedrosa, Marco V. B. A. Vallim, P. Leitão","doi":"10.1109/INDIN45582.2020.9478915","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9478915","url":null,"abstract":"One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129198243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/indin45582.2020.9442109
{"title":"Vibration Suppression Control for Underactuated Systems","authors":"","doi":"10.1109/indin45582.2020.9442109","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442109","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115404189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PM2.5 (particular matter with a diameter of 2.5µm or less) is one of the most important indicators of air pollution. In the field of environmental science, how to forecast PM2.5 is an important topic. We construct a previous 24-hour indicator before the predicted point to construct an enhanced dataset for PM2.5 concentration prediction. However, with a large scale of features, the performances of fundamental neural networks are not stable or accurate enough. As a result, an ensemble GRU (Gate Recurrent Unit) neural network is proposed for short-term PM2.5 prediction. This approach can improve accuracy while maintaining stability by combining the outputs after varying training. In this study, a dataset, which recording 6 indicators (PM2.5, PM10, CO, NO2, O3, SO2) for more than 20,000 hours in Shenzhen, is adopted to evaluate the proposed approach. Experimental results indicate that the proposed ensemble GRU model provides the lowest scores in MSE, RMSE criteria, and the best average-results in R2, MSE, RMSE scores.
{"title":"Short-term PM2.5 Forecasting with a Hybrid Model Based on Ensemble GRU Neural Network","authors":"Wei Jiang, Songyan Li, Zefeng Xie, Wanling Chen, Choujun Zhan","doi":"10.1109/INDIN45582.2020.9442178","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442178","url":null,"abstract":"PM2.5 (particular matter with a diameter of 2.5µm or less) is one of the most important indicators of air pollution. In the field of environmental science, how to forecast PM2.5 is an important topic. We construct a previous 24-hour indicator before the predicted point to construct an enhanced dataset for PM2.5 concentration prediction. However, with a large scale of features, the performances of fundamental neural networks are not stable or accurate enough. As a result, an ensemble GRU (Gate Recurrent Unit) neural network is proposed for short-term PM2.5 prediction. This approach can improve accuracy while maintaining stability by combining the outputs after varying training. In this study, a dataset, which recording 6 indicators (PM2.5, PM10, CO, NO2, O3, SO2) for more than 20,000 hours in Shenzhen, is adopted to evaluate the proposed approach. Experimental results indicate that the proposed ensemble GRU model provides the lowest scores in MSE, RMSE criteria, and the best average-results in R2, MSE, RMSE scores.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115514224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442198
Bin Zhou, Jinsong Bao, Yahui Liu, Dengqiang Song
As the industrial production mode is shifting towards digitalization and intelligence in the new era. Enterprises put forward higher requirements for efficient processing and utilization of accumulated unstructured data. At present, the knowledge and data contained in a large number of unstructured documents are scattered. The types of entities and relationships are diverse. And the constraints of production rules are complicated, which increases the difficulty of knowledge management and utilization. Therefore, this paper studies the semantic knowledge graph generation and reuse method for industrial documents, which can form standardized production resources, the knowledge related to the industry, and question and answer strategies for industrial processing. The challenge of the research is to explore a feasible process knowledge model and efficient industrial information extraction method to effectively provide structured knowledge of process documents. We build process knowledge representation models and information extraction models and algorithms based on process knowledge representation model and natural language processing. The entities and relations of the main production factors are extracted. The knowledge representation model associates the extracted entities and relations to form an industrial knowledge graph, which provides information support for processing knowledge retrieval and question answering methods. Finally, the approach is evaluated by employing the aerospace machining documents. And the proposed method can obtain valuable information in the document and improve utilization of industrial unstructured data.
{"title":"BA-IKG: BiLSTM Embedded ALBERT for Industrial Knowledge Graph Generation and Reuse","authors":"Bin Zhou, Jinsong Bao, Yahui Liu, Dengqiang Song","doi":"10.1109/INDIN45582.2020.9442198","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442198","url":null,"abstract":"As the industrial production mode is shifting towards digitalization and intelligence in the new era. Enterprises put forward higher requirements for efficient processing and utilization of accumulated unstructured data. At present, the knowledge and data contained in a large number of unstructured documents are scattered. The types of entities and relationships are diverse. And the constraints of production rules are complicated, which increases the difficulty of knowledge management and utilization. Therefore, this paper studies the semantic knowledge graph generation and reuse method for industrial documents, which can form standardized production resources, the knowledge related to the industry, and question and answer strategies for industrial processing. The challenge of the research is to explore a feasible process knowledge model and efficient industrial information extraction method to effectively provide structured knowledge of process documents. We build process knowledge representation models and information extraction models and algorithms based on process knowledge representation model and natural language processing. The entities and relations of the main production factors are extracted. The knowledge representation model associates the extracted entities and relations to form an industrial knowledge graph, which provides information support for processing knowledge retrieval and question answering methods. Finally, the approach is evaluated by employing the aerospace machining documents. And the proposed method can obtain valuable information in the document and improve utilization of industrial unstructured data.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442233
Negin Piran Nanekaran, Mohammad Esmalifalak, M. Narimani
This paper proposes a new approach to fast detection of abnormal behaviour of cooling and IT systems in micro data centers (MDCs) based on machine learning (ML) techniques. Conventional protection of MDCs focuses on monitoring individual parameters such as temperature at different locations and when these parameters reaches certain high values, then alarm will be triggered. This paper employs ML techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and is able to detect deviations from such behaviours. This provides an efficient way for not only producing health index for the MDC, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on a MDC placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University.
{"title":"Fast Anomaly Detection in Micro Data Centers Using Machine Learning Techniques","authors":"Negin Piran Nanekaran, Mohammad Esmalifalak, M. Narimani","doi":"10.1109/INDIN45582.2020.9442233","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442233","url":null,"abstract":"This paper proposes a new approach to fast detection of abnormal behaviour of cooling and IT systems in micro data centers (MDCs) based on machine learning (ML) techniques. Conventional protection of MDCs focuses on monitoring individual parameters such as temperature at different locations and when these parameters reaches certain high values, then alarm will be triggered. This paper employs ML techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and is able to detect deviations from such behaviours. This provides an efficient way for not only producing health index for the MDC, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on a MDC placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114841737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442246
Jiwei Xu, Yun Yang, Po Yang
In the context of the continuous development of Internet of Things (IoT) technology and Machine learning (ML) technology, its application in the medical field is becoming more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based medical auxiliary diagnosis system, the impact of label noise problems is also increasing. When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the labels of some patients may adversely affect the performance of the algorithm. For example, due to ambiguous patient conditions or poor reliability of diagnostic criteria, even clinical experts may lack confidence in making medical diagnoses for some patients. As a result, some samples used in algorithm training may be mislabeled, which adversely affects the performance of the algorithm. In this paper, we study a classification problem of sample labels with random damage. We propose a new hybrid label noise correction model that generalizes many learning problems, including supervised, unsupervised and semi-supervised learning. This hybrid model can withstand the negative effects of random noise and various non-random label noise. Extensive experimental results using real-world datasets from UCI machine learning repository are provided, the empirical study shows that our approach successfully improves data quality in many cases, in terms of classification accuracy, over existing label noise correction methods.
{"title":"Hybrid Label Noise Correction Algorithm For Medical Auxiliary Diagnosis","authors":"Jiwei Xu, Yun Yang, Po Yang","doi":"10.1109/INDIN45582.2020.9442246","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442246","url":null,"abstract":"In the context of the continuous development of Internet of Things (IoT) technology and Machine learning (ML) technology, its application in the medical field is becoming more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based medical auxiliary diagnosis system, the impact of label noise problems is also increasing. When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the labels of some patients may adversely affect the performance of the algorithm. For example, due to ambiguous patient conditions or poor reliability of diagnostic criteria, even clinical experts may lack confidence in making medical diagnoses for some patients. As a result, some samples used in algorithm training may be mislabeled, which adversely affects the performance of the algorithm. In this paper, we study a classification problem of sample labels with random damage. We propose a new hybrid label noise correction model that generalizes many learning problems, including supervised, unsupervised and semi-supervised learning. This hybrid model can withstand the negative effects of random noise and various non-random label noise. Extensive experimental results using real-world datasets from UCI machine learning repository are provided, the empirical study shows that our approach successfully improves data quality in many cases, in terms of classification accuracy, over existing label noise correction methods.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124832617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442204
Shaowen Lu, Y. Wen
This paper introduces a machine learning based solution to the practical task of identifying the semi-molten abnormal working condition of fused magnesium furnace. The primary challenge faced by the task is the insufficiency of labeled samples for classifier training. This problem is tackled under the semi-supervised learning framework by combining two complementary features i.e. the smelting currents which are unlabeled and the monitoring images which are partially labeled. An entropy regularized form of cost function is designed which brings the distribution pattern of the smelting current to the training of image based classifier, and an efficient optimization algorithm based on cross entropy method is presented. The proposed solution is tested on industrial dataset showing remarkable result in accuracy.
{"title":"Semi-supervised Learning Approach to Abnormality Detection with Complementary Features","authors":"Shaowen Lu, Y. Wen","doi":"10.1109/INDIN45582.2020.9442204","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442204","url":null,"abstract":"This paper introduces a machine learning based solution to the practical task of identifying the semi-molten abnormal working condition of fused magnesium furnace. The primary challenge faced by the task is the insufficiency of labeled samples for classifier training. This problem is tackled under the semi-supervised learning framework by combining two complementary features i.e. the smelting currents which are unlabeled and the monitoring images which are partially labeled. An entropy regularized form of cost function is designed which brings the distribution pattern of the smelting current to the training of image based classifier, and an efficient optimization algorithm based on cross entropy method is presented. The proposed solution is tested on industrial dataset showing remarkable result in accuracy.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442235
Yongli Ma, Wenjun Xu, Sisi Tian, Jiayi Liu, Zude Zhou, Yang Hu, Hao Feng
The industrial cloud robotics (ICR) has the characteristics of intelligence, reliability, and scalability. In the smart manufacturing environment, ICR can be encapsulated as services through virtualization and servilization technology, enabling the rapid matching of personalized manufacturing capabilities and services for end users. However, the manufacturing resources are physically isolated and the physical workshop environment is vulnerable to dynamic disturbances, which reduces manufacturing system performance. In this context, taking the cycle time into consideration, the manufacturing service scheduling model for ICR is established and the digital twin (DT) enhanced scheduling optimization mechanism is proposed. When disturbances occur, the digital twin platform interacts with the cloud layer and physical workshop to analyze multi-source data in order to monitor the manufacturing environment in real time and optimize the production efficiency. Meanwhile, the manufacturing service scheduling based on an improved discrete differential evolution (IDDE) algorithm is proposed, in which the adaptive mutation and crossover operator and double mutation strategies are applied to converge to the optimal scheduling sequence. Finally, the case study is implemented to verify the proposed mechanism shows better performance compared with the existing optimization algorithms.
{"title":"Digital Twin Enhanced Optimization of Manufacturing Service Scheduling for Industrial Cloud Robotics","authors":"Yongli Ma, Wenjun Xu, Sisi Tian, Jiayi Liu, Zude Zhou, Yang Hu, Hao Feng","doi":"10.1109/INDIN45582.2020.9442235","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442235","url":null,"abstract":"The industrial cloud robotics (ICR) has the characteristics of intelligence, reliability, and scalability. In the smart manufacturing environment, ICR can be encapsulated as services through virtualization and servilization technology, enabling the rapid matching of personalized manufacturing capabilities and services for end users. However, the manufacturing resources are physically isolated and the physical workshop environment is vulnerable to dynamic disturbances, which reduces manufacturing system performance. In this context, taking the cycle time into consideration, the manufacturing service scheduling model for ICR is established and the digital twin (DT) enhanced scheduling optimization mechanism is proposed. When disturbances occur, the digital twin platform interacts with the cloud layer and physical workshop to analyze multi-source data in order to monitor the manufacturing environment in real time and optimize the production efficiency. Meanwhile, the manufacturing service scheduling based on an improved discrete differential evolution (IDDE) algorithm is proposed, in which the adaptive mutation and crossover operator and double mutation strategies are applied to converge to the optimal scheduling sequence. Finally, the case study is implemented to verify the proposed mechanism shows better performance compared with the existing optimization algorithms.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121513195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442081
A. Roque, N. Jazdi, E. P. Freitas, C. Pereira
This paper presents an approach to monitor the performance degradation in CAN networks and the fault effect on time constraints of periodic control tasks. The work proposes the use of a test-based method supported by fault and error injection that helps the engineer to define how these faults degrade the system performance. In addition, a runtime jitter monitoring technique is proposed and applied to a CAN-based vehicular network. The runtime jitter analyses the performance oscillation according to a time window and with a tolerance range defined during previous performance tests. A case study was conducted monitoring a critical control system during error injection and the analysis technique was applied in order to verify the performance oscillation detection. Results show that with a typical CAN rate of 1 Mbps, the runtime jitter detect anomalies in performance during a short time period up to 30% of busload. The experiments show the degradation of 4,2 times on average jitter between 10% and 30% of busload with fault injection. The detection is also possible with higher busload 50% and 80%, but with an increase in the detection time. The present study emphasizes the importance of performance monitoring with the recent advances in automotive electronics.
{"title":"Performance analysis of in-vehicle distributed control systems applying a real-time jitter monitor","authors":"A. Roque, N. Jazdi, E. P. Freitas, C. Pereira","doi":"10.1109/INDIN45582.2020.9442081","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442081","url":null,"abstract":"This paper presents an approach to monitor the performance degradation in CAN networks and the fault effect on time constraints of periodic control tasks. The work proposes the use of a test-based method supported by fault and error injection that helps the engineer to define how these faults degrade the system performance. In addition, a runtime jitter monitoring technique is proposed and applied to a CAN-based vehicular network. The runtime jitter analyses the performance oscillation according to a time window and with a tolerance range defined during previous performance tests. A case study was conducted monitoring a critical control system during error injection and the analysis technique was applied in order to verify the performance oscillation detection. Results show that with a typical CAN rate of 1 Mbps, the runtime jitter detect anomalies in performance during a short time period up to 30% of busload. The experiments show the degradation of 4,2 times on average jitter between 10% and 30% of busload with fault injection. The detection is also possible with higher busload 50% and 80%, but with an increase in the detection time. The present study emphasizes the importance of performance monitoring with the recent advances in automotive electronics.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123221698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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