Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557449
V. Lešić, Filip Vrbanc, N. Peric, A. Banjac, Hrvoje Novak, L. Jelic
Predictive control and optimization in buildings proved to be a promising approach in increasing energy efficiency of the sector as one of the largest energy consumer. Zone digitalization is still one of the ongoing issues in older buildings, and has only recently started to be interesting in the residential sector due to high prices of required expert knowledge and automation equipment. However, buildings systems digitalization and networking also brought to fore the system security issues. Distributed approach to zone digitalization and predictive control, enabled by recent advances in embedded technology, implies both hardware topology and control algorithm structure. The paper focuses on a case where each zone holds a separate controller with tailored temperature setpoint prediction and model predictive control algorithm, which independently calculate the optimal heating control laws of the corresponding zones. Furthermore, the controllers are mutually and iteratively bidding toward the joint energy efficiency goal of the whole building. Such control structure enables fast digitalization and optimal joint operation of the building while keeping the independency of the users and retaining the data privacy. Only essential data is transmitted to the central coordinator in form of a summed information, which cannot extrapolate particular user data. Additionally, single zone controller security breach does not inflict damage to the whole system. System resiliency to security issues is therefore strongly increased.
{"title":"Distributed Optimal Heating Control of a Residential Building Resilient to Cybersecurity Issues","authors":"V. Lešić, Filip Vrbanc, N. Peric, A. Banjac, Hrvoje Novak, L. Jelic","doi":"10.1109/INDIN45523.2021.9557449","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557449","url":null,"abstract":"Predictive control and optimization in buildings proved to be a promising approach in increasing energy efficiency of the sector as one of the largest energy consumer. Zone digitalization is still one of the ongoing issues in older buildings, and has only recently started to be interesting in the residential sector due to high prices of required expert knowledge and automation equipment. However, buildings systems digitalization and networking also brought to fore the system security issues. Distributed approach to zone digitalization and predictive control, enabled by recent advances in embedded technology, implies both hardware topology and control algorithm structure. The paper focuses on a case where each zone holds a separate controller with tailored temperature setpoint prediction and model predictive control algorithm, which independently calculate the optimal heating control laws of the corresponding zones. Furthermore, the controllers are mutually and iteratively bidding toward the joint energy efficiency goal of the whole building. Such control structure enables fast digitalization and optimal joint operation of the building while keeping the independency of the users and retaining the data privacy. Only essential data is transmitted to the central coordinator in form of a summed information, which cannot extrapolate particular user data. Additionally, single zone controller security breach does not inflict damage to the whole system. System resiliency to security issues is therefore strongly increased.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131438974","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557479
Timon Hoebert, M. Merdan, W. Lepuschitz
Cloud manufacturing is a new manufacturing concept, where manufacturing companies offer their production resources in a networked environment. It is of vital importance to match capacities with available customer orders. However, a unifying approach, able to provide appropriate resource allocation as well as to facilitate the general understanding in such distributed and heterogeneous environments is required. This paper presents an automatic mechanism that integrates semantic representations of a cloud manufacturing system with a planner that generates a concrete structured sequence of tasks, which map production capacities and required order constraints. The proposed Semantic Planning Framework is evaluated using a generic dataset to assess the flexibility and generalizability of the system.
{"title":"An Automated Semantic Planning Framework applied in a Cloud Manufacturing Domain","authors":"Timon Hoebert, M. Merdan, W. Lepuschitz","doi":"10.1109/INDIN45523.2021.9557479","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557479","url":null,"abstract":"Cloud manufacturing is a new manufacturing concept, where manufacturing companies offer their production resources in a networked environment. It is of vital importance to match capacities with available customer orders. However, a unifying approach, able to provide appropriate resource allocation as well as to facilitate the general understanding in such distributed and heterogeneous environments is required. This paper presents an automatic mechanism that integrates semantic representations of a cloud manufacturing system with a planner that generates a concrete structured sequence of tasks, which map production capacities and required order constraints. The proposed Semantic Planning Framework is evaluated using a generic dataset to assess the flexibility and generalizability of the system.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115371000","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}
With the rapid development of deep learning, automatic defect detection has been introduced into various manufacturing pipelines. Many studies on defect inspection focus on training an accurate model that can perform well on a certain defect type. However, as the manufacturing process evolves, new defect types may appear in practice. The model trained on old defect types will struggle to detect the new ones. To address this issue, we propose to use continual lifelong learning for defect detection. The deep model can increasingly learn to detect new defects yet keeping the learned ones non-forgetting without retraining on the previous data. Our approach can build a compact model, which increasingly learns to detect new defect types. Experimental results show that our approach can learn to detect new defect types incrementally while maintaining its original capability to detect the old defect types.
{"title":"Defect Detection Using Deep Lifelong Learning","authors":"Chien-Hung Chen, Cheng-Hao Tu, Jia-Da Li, Chu-Song Chen","doi":"10.1109/INDIN45523.2021.9557417","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557417","url":null,"abstract":"With the rapid development of deep learning, automatic defect detection has been introduced into various manufacturing pipelines. Many studies on defect inspection focus on training an accurate model that can perform well on a certain defect type. However, as the manufacturing process evolves, new defect types may appear in practice. The model trained on old defect types will struggle to detect the new ones. To address this issue, we propose to use continual lifelong learning for defect detection. The deep model can increasingly learn to detect new defects yet keeping the learned ones non-forgetting without retraining on the previous data. Our approach can build a compact model, which increasingly learns to detect new defect types. Experimental results show that our approach can learn to detect new defect types incrementally while maintaining its original capability to detect the old defect types.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121462951","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557516
Kaja Balzereit, Alexander Diedrich, Jonas Ginster, Stefan Windmann, O. Niggemann
AI methods for fault handling in Cyber-Physical Production Systems (CPPS) such as production plants and tank systems are an emerging research topic. In the last years many methods for the detection of anomalies and faults, the diagnosis of the root cause and the automated repair have been developed. However, most of the methods are barely evaluated using a wide range of systems but applicability is shown using single use cases. In this paper, an ensemble of simulated benchmark systems is presented, which allows for a broad evaluation of AI methods for fault handling. The ensemble consists of seven different tank systems from process engineering with varying sizes and complexities and is made publicly available on Github. The suitability of the ensemble is shown using AI methods for fault handling such as anomaly detection, diagnosis and reconfiguration.
{"title":"An Ensemble of Benchmarks for the Evaluation of AI Methods for Fault Handling in CPPS","authors":"Kaja Balzereit, Alexander Diedrich, Jonas Ginster, Stefan Windmann, O. Niggemann","doi":"10.1109/INDIN45523.2021.9557516","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557516","url":null,"abstract":"AI methods for fault handling in Cyber-Physical Production Systems (CPPS) such as production plants and tank systems are an emerging research topic. In the last years many methods for the detection of anomalies and faults, the diagnosis of the root cause and the automated repair have been developed. However, most of the methods are barely evaluated using a wide range of systems but applicability is shown using single use cases. In this paper, an ensemble of simulated benchmark systems is presented, which allows for a broad evaluation of AI methods for fault handling. The ensemble consists of seven different tank systems from process engineering with varying sizes and complexities and is made publicly available on Github. The suitability of the ensemble is shown using AI methods for fault handling such as anomaly detection, diagnosis and reconfiguration.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122492679","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557505
B. Vogel‐Heuser, C. Huber, Suhyun Cha, Bernhard Beckert
Cyber Physical Production Systems (CPPS) operate for a long time and face continuous and incremental changes to follow up varying requirements. Interdisciplinary engineering of CPPS is often subject to delay and cost overrun; and quality control may even fail due to the lack of efficient information exchange between multiple involved actors. We propose to integrate a formal requirement specification approach, namely Generalized Test Tables including tool support, into industrial workflows and present the approach through extended notations of Business Process Model and Notation (BPMN), namely BPMN++*, with the tool-coupling aspect. The suggested tooling enables automation engineers to follow the defined workflow systematically and communicate easier through the formally represented change requirement. The approach is demonstrated by two typical use cases of changing a CPPS’ control software and showing the result by means of an extended BPMN++ model exemplarily.
{"title":"Integration of a formal specification approach into CPPS engineering workflow for machinery validation","authors":"B. Vogel‐Heuser, C. Huber, Suhyun Cha, Bernhard Beckert","doi":"10.1109/INDIN45523.2021.9557505","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557505","url":null,"abstract":"Cyber Physical Production Systems (CPPS) operate for a long time and face continuous and incremental changes to follow up varying requirements. Interdisciplinary engineering of CPPS is often subject to delay and cost overrun; and quality control may even fail due to the lack of efficient information exchange between multiple involved actors. We propose to integrate a formal requirement specification approach, namely Generalized Test Tables including tool support, into industrial workflows and present the approach through extended notations of Business Process Model and Notation (BPMN), namely BPMN++*, with the tool-coupling aspect. The suggested tooling enables automation engineers to follow the defined workflow systematically and communicate easier through the formally represented change requirement. The approach is demonstrated by two typical use cases of changing a CPPS’ control software and showing the result by means of an extended BPMN++ model exemplarily.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756059","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557456
N. Nevaranta
To guarantee higher bandwidths and faster diagnostics in electrical drives systems, oversampling-based routines have been receiving increasing attention, particularly for the purpose to improve the limitations of motor control approaches and increase the overall reliability of the drive. This paper reviews recent research findings of oversampling approaches applied to parameter estimation, sensorless control, fast current control, and diagnostics. As the computational capacity of modern electrical drives is increasing when traditional solutions are being replaced by field programmable gate array (FPGA) hardware-based digital control systems, it is important to highlight the opportunities toward next generation motor drive systems provided with intelligent oversampling routines. The possibilities that oversampling could provide for closed-loop control diagnostics or component fault detection have not yet been exploited to the full, but there is a need for robust and fast approaches. This paper provides concise review of the oversampling approaches and their possible solutions in future drives.
{"title":"Review on Oversampling Approaches for Control and Estimation in Electrical Drives","authors":"N. Nevaranta","doi":"10.1109/INDIN45523.2021.9557456","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557456","url":null,"abstract":"To guarantee higher bandwidths and faster diagnostics in electrical drives systems, oversampling-based routines have been receiving increasing attention, particularly for the purpose to improve the limitations of motor control approaches and increase the overall reliability of the drive. This paper reviews recent research findings of oversampling approaches applied to parameter estimation, sensorless control, fast current control, and diagnostics. As the computational capacity of modern electrical drives is increasing when traditional solutions are being replaced by field programmable gate array (FPGA) hardware-based digital control systems, it is important to highlight the opportunities toward next generation motor drive systems provided with intelligent oversampling routines. The possibilities that oversampling could provide for closed-loop control diagnostics or component fault detection have not yet been exploited to the full, but there is a need for robust and fast approaches. This paper provides concise review of the oversampling approaches and their possible solutions in future drives.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"28 Spec No 1 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675570","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557469
Alberto Morato, Giovanni Peserico, Tommaso Fedullo, F. Tramarin, S. Vitturi
In recent years, Factory Automation is evolving towards the so-called Industry 4.0, and the creation of a smart factory ecosystem comprising of ubiquitously interconnected objects, namely the Industrial Internet of Things (IIoT), is gaining much research interest. This paradigm aims at developing new smart technological equipment and protocols, thus providing interconnection among "factory objects" anywhere and at any time. In this context, people and machines have to safely cooperate and a high level of protection needs to be guaranteed for both operators and the surrounding environment. For this reason, safety systems, aiming at decreasing risks and failure probabilities, are nowadays of uttermost importance. Several Functional Safety communication protocols have been developed during these years pointing to increase data integrity and guarantee protection in a safety system. Popular examples are Fail Safe over EtherCAT (FSoE), ProfiSAFE, and OPC-UA Safety. These protocols, al-though conceived for wired networks, can be in principle adopted also by wireless communication, as they are developed by using a black channel approach. Nevertheless, the implementation of these protocols over different wireless networks is challenging as they might not ensure the required Safety Integrated Level (SIL). This paper, moving from the aforementioned observations and the need for wireless solutions in the IIoT context, focuses on proposing a possible implementation of FSOE over Wi-Fi, running UDP at the transport layer. In particular, by using suitable experimental outcomes, an OMNeT++ simulator has been calibrated, thus enabling the possibility to analyze the proposed protocol in wide industrial systems.
{"title":"Tuning of a simulation model for the assessment of Functional Safety over Wi-Fi","authors":"Alberto Morato, Giovanni Peserico, Tommaso Fedullo, F. Tramarin, S. Vitturi","doi":"10.1109/INDIN45523.2021.9557469","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557469","url":null,"abstract":"In recent years, Factory Automation is evolving towards the so-called Industry 4.0, and the creation of a smart factory ecosystem comprising of ubiquitously interconnected objects, namely the Industrial Internet of Things (IIoT), is gaining much research interest. This paradigm aims at developing new smart technological equipment and protocols, thus providing interconnection among \"factory objects\" anywhere and at any time. In this context, people and machines have to safely cooperate and a high level of protection needs to be guaranteed for both operators and the surrounding environment. For this reason, safety systems, aiming at decreasing risks and failure probabilities, are nowadays of uttermost importance. Several Functional Safety communication protocols have been developed during these years pointing to increase data integrity and guarantee protection in a safety system. Popular examples are Fail Safe over EtherCAT (FSoE), ProfiSAFE, and OPC-UA Safety. These protocols, al-though conceived for wired networks, can be in principle adopted also by wireless communication, as they are developed by using a black channel approach. Nevertheless, the implementation of these protocols over different wireless networks is challenging as they might not ensure the required Safety Integrated Level (SIL). This paper, moving from the aforementioned observations and the need for wireless solutions in the IIoT context, focuses on proposing a possible implementation of FSOE over Wi-Fi, running UDP at the transport layer. In particular, by using suitable experimental outcomes, an OMNeT++ simulator has been calibrated, thus enabling the possibility to analyze the proposed protocol in wide industrial systems.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359245","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557489
A. Zhang, Xinglong Yu, Yang Zhang
In order to improve the classification accuracy of analog circuit failure modes, this paper proposes an ultra-lightweight subspace attention module (ULSAM) classification method, which combines lightweight (reducing parameters) with attention mechanism to improve convolutional neural networks (CNN) feature extraction and classification performance. This article uses depthwise separable (DWS) convolution, by decomposing the standard convolution into depthwise convolution (feature extraction) and pointwise convolution (feature aggregation). Meanwhile, the attention mechanism is applied, only one 1×1 filter is used after depthwise convolution, which can compute efficient interaction of cross-channel information, and uses the linear relationship between feature maps to avoid the use of multi-layer perceptron (MLP). The application of the failure modes of analog circuits shows that the proposed ULSAM method can realize the pattern classification of analog circuit faults more quickly and accurately.
{"title":"Fault Recognition of Analog Circuits Based on Ultra-Lightweight Subspace Attention Module","authors":"A. Zhang, Xinglong Yu, Yang Zhang","doi":"10.1109/INDIN45523.2021.9557489","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557489","url":null,"abstract":"In order to improve the classification accuracy of analog circuit failure modes, this paper proposes an ultra-lightweight subspace attention module (ULSAM) classification method, which combines lightweight (reducing parameters) with attention mechanism to improve convolutional neural networks (CNN) feature extraction and classification performance. This article uses depthwise separable (DWS) convolution, by decomposing the standard convolution into depthwise convolution (feature extraction) and pointwise convolution (feature aggregation). Meanwhile, the attention mechanism is applied, only one 1×1 filter is used after depthwise convolution, which can compute efficient interaction of cross-channel information, and uses the linear relationship between feature maps to avoid the use of multi-layer perceptron (MLP). The application of the failure modes of analog circuits shows that the proposed ULSAM method can realize the pattern classification of analog circuit faults more quickly and accurately.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127901416","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557397
Yang Zhang, A. Zhang, Danlu Yu
In order to solve the problems of low prediction accuracy and long training time that are common in the existing analog circuit fault diagnosis models, this paper proposes a new combination of wavelet packet feature extraction, singular value decomposition(SVD) and dimensionality reduction and support vector machine(SVM) classification method. This method selects wavelet packet analysis with higher accuracy than traditional wavelet analysis, extracts features of analog circuit fault data, and normalizes the extracted feature data; then uses singular value decomposition method to perform fault data matrix decompose to achieve the purpose of dimensionality reduction. The size of the singular value obtained by decomposition reflects the characteristics of the fault information. Selecting the matrix with the largest singular value as a sample can express the fault characteristics more accurately and efficiently; finally, use the support vector machine to decompose the fault after the singular value. The matrix is trained and classified, so as to realize the fault diagnosis of the analog circuit. The simulation experiment results show that, compared with the current diagnosis models such as BAGRNN, the SVD model proposed in this paper improves the fault diagnosis rate of analog circuits, effectively reduces the amount of matrix calculation, and speeds up the diagnosis.
{"title":"Fault Diagnosis of Analog Circuit based On Wavelet Packet Analysis and SVD","authors":"Yang Zhang, A. Zhang, Danlu Yu","doi":"10.1109/INDIN45523.2021.9557397","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557397","url":null,"abstract":"In order to solve the problems of low prediction accuracy and long training time that are common in the existing analog circuit fault diagnosis models, this paper proposes a new combination of wavelet packet feature extraction, singular value decomposition(SVD) and dimensionality reduction and support vector machine(SVM) classification method. This method selects wavelet packet analysis with higher accuracy than traditional wavelet analysis, extracts features of analog circuit fault data, and normalizes the extracted feature data; then uses singular value decomposition method to perform fault data matrix decompose to achieve the purpose of dimensionality reduction. The size of the singular value obtained by decomposition reflects the characteristics of the fault information. Selecting the matrix with the largest singular value as a sample can express the fault characteristics more accurately and efficiently; finally, use the support vector machine to decompose the fault after the singular value. The matrix is trained and classified, so as to realize the fault diagnosis of the analog circuit. The simulation experiment results show that, compared with the current diagnosis models such as BAGRNN, the SVD model proposed in this paper improves the fault diagnosis rate of analog circuits, effectively reduces the amount of matrix calculation, and speeds up the diagnosis.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127405268","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 : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557486
Liang Zhang, A. Lobov
Industry 4.0 aims at the promotion of design and manufacture automation to provide customized products. Knowledge-Based Engineering (KBE) is seen as a competitive way to realise design automation. This paper first introduces the Methodology and software tools Oriented to Knowledge-Based engineering Application (MOKA) method to develop an KBE application integrated with external calculators. Then a simple case of the wood-to-wood connection with different fasteners providing the connection capacity is demonstrated, showing the advantages to establish a model in parametric method integrated with external services. Meanwhile, this case shows the shortcomings of the integration of the current tools representing knowledge in different frameworks. Then a potential better way to integrate engineering knowledge is discussed.
{"title":"Extending design automation by integrating external services for product design","authors":"Liang Zhang, A. Lobov","doi":"10.1109/INDIN45523.2021.9557486","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557486","url":null,"abstract":"Industry 4.0 aims at the promotion of design and manufacture automation to provide customized products. Knowledge-Based Engineering (KBE) is seen as a competitive way to realise design automation. This paper first introduces the Methodology and software tools Oriented to Knowledge-Based engineering Application (MOKA) method to develop an KBE application integrated with external calculators. Then a simple case of the wood-to-wood connection with different fasteners providing the connection capacity is demonstrated, showing the advantages to establish a model in parametric method integrated with external services. Meanwhile, this case shows the shortcomings of the integration of the current tools representing knowledge in different frameworks. Then a potential better way to integrate engineering knowledge is discussed.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547067","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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