Pub Date : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613360
Ivan Pisa, A. Morell, J. Vicario, R. Vilanova
The incursion of the Industry 4.0 paradigm and the Artificial Neural Networks (ANNs) is changing the way as the industrial systems are conceived and controlled. Now, it is more common to talk about data-driven methods either supporting conventional industrial control strategies, or acting as the control itself. Thus, one can find that in the last years it is more common to find control systems which are purely based on data leaving aside the highly complex mathematical models. However, data-driven models and ANNs have to be correctly trained in order to offer a good performance and therefore, be contemplated as the core part of a control strategy. This can become a time-demanding and tedious process. For that reason, Transfer Learning (TL) techniques can be adopted to ease the conception, design and training processes of the data-based and ANNs methods, since the efforts have to be mainly focused on training a unique net which will be then transferred into the other scenarios. In that sense, we present here a TL approach to design and implement the whole control of a Wastewater Treatment Plant (WWTP). First, the control of the quickest dynamics under control is performed by means of a Long Short-Term Memory cell (LSTM) based Proportional Integral (PI) controller (LSTM-based PI). Once the LSTM is trained and tested, its knowledge will be transferred into the remaining WWTP control loops. In that way, an ease and reduction in the time involved in the design and training of the control as well as in its complexity is achieved. Results have shown a twofold achievement: (i) the LSTM-based PI achieves an improvement of the control performance with respect to a conventional PI controller around a 93.56% and a 99.07% in terms of the Integrated Absolute (IAE) and Integrated Squared (ISE) errors between the desired measurement and the obtained one, respectively, and (ii) the LSTM-based PI controller achieves an average improvement in the IAE and ISE around a 9.55% and 15.25%, respectively, when it is transferred into a different WWTP control loop.
{"title":"Transfer Learning Approach for the Design of Basic Control Loops in Wastewater Treatment Plants","authors":"Ivan Pisa, A. Morell, J. Vicario, R. Vilanova","doi":"10.1109/ETFA45728.2021.9613360","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613360","url":null,"abstract":"The incursion of the Industry 4.0 paradigm and the Artificial Neural Networks (ANNs) is changing the way as the industrial systems are conceived and controlled. Now, it is more common to talk about data-driven methods either supporting conventional industrial control strategies, or acting as the control itself. Thus, one can find that in the last years it is more common to find control systems which are purely based on data leaving aside the highly complex mathematical models. However, data-driven models and ANNs have to be correctly trained in order to offer a good performance and therefore, be contemplated as the core part of a control strategy. This can become a time-demanding and tedious process. For that reason, Transfer Learning (TL) techniques can be adopted to ease the conception, design and training processes of the data-based and ANNs methods, since the efforts have to be mainly focused on training a unique net which will be then transferred into the other scenarios. In that sense, we present here a TL approach to design and implement the whole control of a Wastewater Treatment Plant (WWTP). First, the control of the quickest dynamics under control is performed by means of a Long Short-Term Memory cell (LSTM) based Proportional Integral (PI) controller (LSTM-based PI). Once the LSTM is trained and tested, its knowledge will be transferred into the remaining WWTP control loops. In that way, an ease and reduction in the time involved in the design and training of the control as well as in its complexity is achieved. Results have shown a twofold achievement: (i) the LSTM-based PI achieves an improvement of the control performance with respect to a conventional PI controller around a 93.56% and a 99.07% in terms of the Integrated Absolute (IAE) and Integrated Squared (ISE) errors between the desired measurement and the obtained one, respectively, and (ii) the LSTM-based PI controller achieves an average improvement in the IAE and ISE around a 9.55% and 15.25%, respectively, when it is transferred into a different WWTP control loop.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567789","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613573
M. Lewke, S. Nielsen, A. List, F. Gärtner, T. Klassen, A. Fay
In recent years, cold spraying (CS) has emerged as a promising technology for repair applications, particularly for oxidation-sensitive materials. In order to obtain an optimum repair result that fulfills the highest requirements regarding material properties, simple geometric shape restoration is not sufficient. Any additive manufacturing process results in particular features in microstructure, possible defects and respective - potentially even anisotropic - mechanical properties. To systematically tailor these microstructures and properties to the specific component and geometry requires complex routines. This work proposes the design of a knowledge-based cold spray repair system that facilitates a complete individual repair procedure for aircraft components. This system includes the elements of (i) reverse engineering to analyze, classify and generate digital data of the damaged component, (ii) pre-processing to obtain the ideal conditions for the CS process, (iii) toolpath planning to optimize robotics for the CS process, (iv) on-line monitoring to ensure process quality, (v) post-processing and (vi) performance testing of the material properties to meet the challenging requirements of the aerospace industry. By using an industrial robot and computer-aided planning of the trajectories, components are to be repaired under cold spray and geometrical conditions for ideal material deposition. The goal is to obtain repaired components that fulfill the required property profile equally well as respective new parts.
{"title":"Knowledge-based Optimization of Cold Spray for Aircraft Component Repair","authors":"M. Lewke, S. Nielsen, A. List, F. Gärtner, T. Klassen, A. Fay","doi":"10.1109/ETFA45728.2021.9613573","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613573","url":null,"abstract":"In recent years, cold spraying (CS) has emerged as a promising technology for repair applications, particularly for oxidation-sensitive materials. In order to obtain an optimum repair result that fulfills the highest requirements regarding material properties, simple geometric shape restoration is not sufficient. Any additive manufacturing process results in particular features in microstructure, possible defects and respective - potentially even anisotropic - mechanical properties. To systematically tailor these microstructures and properties to the specific component and geometry requires complex routines. This work proposes the design of a knowledge-based cold spray repair system that facilitates a complete individual repair procedure for aircraft components. This system includes the elements of (i) reverse engineering to analyze, classify and generate digital data of the damaged component, (ii) pre-processing to obtain the ideal conditions for the CS process, (iii) toolpath planning to optimize robotics for the CS process, (iv) on-line monitoring to ensure process quality, (v) post-processing and (vi) performance testing of the material properties to meet the challenging requirements of the aerospace industry. By using an industrial robot and computer-aided planning of the trajectories, components are to be repaired under cold spray and geometrical conditions for ideal material deposition. The goal is to obtain repaired components that fulfill the required property profile equally well as respective new parts.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809219","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613505
Giorgio Nicola, S. Ghidoni
In this paper we tackle motion planning in industrial human-robot cooperative scenarios modeled as a reinforcement learning problem solved in a simulated environment. The agent learns the most effective policy to reach the designated target position while avoiding collisions with a human, performing a pick and place task in the robot workspace, and with fixed obstacles. The policy acts as a feedback motion planner (or reactive motion planner), therefore at each time-step it senses the surrounding environment and computes the action to be performed. In this work a novel formulation of the action that guarantees the trajectory derivatives continuity is proposed to create smooth trajectories that are necessary for maximizing the human trust in the robot. The action is defined as the sub-trajectory the agent must follow for the duration of a time-step, therefore the complete trajectory is the concatenation of all the trajectories computed at each time-step. The proposed method does not require to infer the action the human is currently performing and/or foresee the space occupied by the human. Indeed, during the training phase in a simulated environment the agent experience how the human behaves in the specific scenario, therefore it learns the policy that best adapts to the human actions and movements. The proposed method is finally applied in a scenario of human-robot cooperative pick and place.
{"title":"Deep Reinforcement Learning for Motion Planning in Human Robot cooperative Scenarios","authors":"Giorgio Nicola, S. Ghidoni","doi":"10.1109/ETFA45728.2021.9613505","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613505","url":null,"abstract":"In this paper we tackle motion planning in industrial human-robot cooperative scenarios modeled as a reinforcement learning problem solved in a simulated environment. The agent learns the most effective policy to reach the designated target position while avoiding collisions with a human, performing a pick and place task in the robot workspace, and with fixed obstacles. The policy acts as a feedback motion planner (or reactive motion planner), therefore at each time-step it senses the surrounding environment and computes the action to be performed. In this work a novel formulation of the action that guarantees the trajectory derivatives continuity is proposed to create smooth trajectories that are necessary for maximizing the human trust in the robot. The action is defined as the sub-trajectory the agent must follow for the duration of a time-step, therefore the complete trajectory is the concatenation of all the trajectories computed at each time-step. The proposed method does not require to infer the action the human is currently performing and/or foresee the space occupied by the human. Indeed, during the training phase in a simulated environment the agent experience how the human behaves in the specific scenario, therefore it learns the policy that best adapts to the human actions and movements. The proposed method is finally applied in a scenario of human-robot cooperative pick and place.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977599","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613215
P. Blanke, Shubham Sharma, S. Storms, C. Brecher
With the advent of collaborative robots, the demand for manipulators is constantly increasing, especially in SMEs. Hence, the risk of collisions and resulting damage due to programs written by inexperienced users is also simultaneously increasing. Due to the lack of 3D maps of the environment, there are no comprehensive support systems available to check programmed sequences for collision-free execution or to optimize them subsequently. This leads to longer process times and complicates commissioning, especially for inexperienced users. In this paper, a method is presented in which a collaborative robot can autonomously create a 3D map of its environment. Subsequently, the created environment map can be used to optimize existing processes, guarantee collision-free motions and support the operator during commissioning.
{"title":"Flexible creation of a 3D-Map in an unknown environment by a robot","authors":"P. Blanke, Shubham Sharma, S. Storms, C. Brecher","doi":"10.1109/ETFA45728.2021.9613215","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613215","url":null,"abstract":"With the advent of collaborative robots, the demand for manipulators is constantly increasing, especially in SMEs. Hence, the risk of collisions and resulting damage due to programs written by inexperienced users is also simultaneously increasing. Due to the lack of 3D maps of the environment, there are no comprehensive support systems available to check programmed sequences for collision-free execution or to optimize them subsequently. This leads to longer process times and complicates commissioning, especially for inexperienced users. In this paper, a method is presented in which a collaborative robot can autonomously create a 3D map of its environment. Subsequently, the created environment map can be used to optimize existing processes, guarantee collision-free motions and support the operator during commissioning.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114703767","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613563
Roman Matvejev, Yar Muhammad, Naveed Muhammad
Flow sensing has been investigated in the context of underwater and aerial robotics in the past decade. It has not been explored for applications in autonomous ground robotics. In this work-in-progress paper, we investigate the use of air-flow sensing for the applications in autonomous driving. More precisely, we investigate the use of air-flow sensing for vehicle length estimation by conducting computational-fluid-dynamics (CFD) simulations.
{"title":"Air-flow sensing for vehicle length estimation in autonomous driving applications","authors":"Roman Matvejev, Yar Muhammad, Naveed Muhammad","doi":"10.1109/ETFA45728.2021.9613563","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613563","url":null,"abstract":"Flow sensing has been investigated in the context of underwater and aerial robotics in the past decade. It has not been explored for applications in autonomous ground robotics. In this work-in-progress paper, we investigate the use of air-flow sensing for the applications in autonomous driving. More precisely, we investigate the use of air-flow sensing for vehicle length estimation by conducting computational-fluid-dynamics (CFD) simulations.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117156666","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613188
G. Franzl, T. Leopold, S. Wilker, T. Sauter
In the course of the energy transition novel solutions are needed to solve challenges that arise. Many customer owned energy resources become connected and shall contribute to the demand-supply balancing process, eventually. Small size suggest to solve related volatility issues also locally. Flexibilities of customer appliances provide the means to mitigate/manage the impact of volatile energy production close to the source. This paper presents an approach to rate appliance specific flexibility offers. Provider and user needs are addressed independently via the metrics benefit and quality. Introducing the problem and the proposed 2D-rating, we sketch exemplarily how PV and electric room heating flexibility can be predicted, offered, and used.
{"title":"Flexibility Offering and Rating for Multi-objective Energy Balancing","authors":"G. Franzl, T. Leopold, S. Wilker, T. Sauter","doi":"10.1109/ETFA45728.2021.9613188","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613188","url":null,"abstract":"In the course of the energy transition novel solutions are needed to solve challenges that arise. Many customer owned energy resources become connected and shall contribute to the demand-supply balancing process, eventually. Small size suggest to solve related volatility issues also locally. Flexibilities of customer appliances provide the means to mitigate/manage the impact of volatile energy production close to the source. This paper presents an approach to rate appliance specific flexibility offers. Provider and user needs are addressed independently via the metrics benefit and quality. Introducing the problem and the proposed 2D-rating, we sketch exemplarily how PV and electric room heating flexibility can be predicted, offered, and used.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123217229","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613378
A. Casavola, G. Franzé, Francesco Tedesco
In this paper, an adaptive sensor selection architecture is developed to deal with distributed state estimation problems for multi-agent networked systems consisting of three different classes of nodes (plants, sensors and agents). Specifically, the problem of adequately fusing the sensors data coming from the plants and delivered to the agents, is addressed by evaluating their trustworthiness. This is achieved by exploiting a well-established approach in the power electronics: the Perturb&Observe algorithm that in the present framework allows one to select the more adequate group of sensors so as to compute at each time instant the best state estimate according to a given performance index. Some simulations are finally reported to testify the effectiveness of the proposed methodology.
{"title":"Sensors Selection via a Distributed Reputation Mechanism: An Information Fusion Approach","authors":"A. Casavola, G. Franzé, Francesco Tedesco","doi":"10.1109/ETFA45728.2021.9613378","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613378","url":null,"abstract":"In this paper, an adaptive sensor selection architecture is developed to deal with distributed state estimation problems for multi-agent networked systems consisting of three different classes of nodes (plants, sensors and agents). Specifically, the problem of adequately fusing the sensors data coming from the plants and delivered to the agents, is addressed by evaluating their trustworthiness. This is achieved by exploiting a well-established approach in the power electronics: the Perturb&Observe algorithm that in the present framework allows one to select the more adequate group of sensors so as to compute at each time instant the best state estimate according to a given performance index. Some simulations are finally reported to testify the effectiveness of the proposed methodology.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079676","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613457
S. Ramasamy, K. Eriksson, Saptha Peralippatt, Balasubramanian Perumal, F. Danielsson
Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of PRM (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes; computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, PRM with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry.
Plug - and - production演示处理行业中的多个过程，适当的路径规划是必不可少的，同时越来越强调更可持续的过程。为了确保这些过程的可持续性和自动化，需要优化路径规划。我们提出了一种路径规划算法的实现，该算法创建了一个平滑的无碰撞路径并考虑了能量使用。在本文中，我们在模拟环境中演示了PRM(概率路线图)路径规划和基于Dijkstra的优化算法的实现，然后在实际的plug and production演示器中进行了测试。为了验证仿真结果，通过信号分析仪在线测量了实际能量。本文概述的测量结果包括:计算时间，沿路径移动时间，以及不同负载下的能量使用。实验和结果表明，PRM和Dijkstra两种算法的结合可以产生无碰撞的优化路径。这里我们将距离作为Dijkstra优化算法的代价函数，并测量了无碰撞优化路径的能量。这项研究的实际意义是作为任何一种应用的推动者，其中有很大的变化的订单，例如，装配技术在制造业的操作。
{"title":"Optimized Online Path Planning Algorithms Considering Energy","authors":"S. Ramasamy, K. Eriksson, Saptha Peralippatt, Balasubramanian Perumal, F. Danielsson","doi":"10.1109/ETFA45728.2021.9613457","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613457","url":null,"abstract":"Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of PRM (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes; computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, PRM with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124273215","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613293
H. Fadhlillah, Bianca Wiesmayr, Michael Oberlehner, Rick Rabiser, Alois Zoitl
Modern production plants are complex Cyber-Physical Production Systems with an ever-increasing share of software controlling and automating their operation. The customization of these systems to the needs of their customers and their frequent evolution over a typically long life-cycle result in a plethora of variants to be managed. However, reuse still is mainly done in an opportunistic way, relying on copy-paste-modify strategies. More systematic, strategic reuse would help to reduce costs and time to market, but requires approaches that can be applied to domain-specific languages for developing distributed control software. In this paper, we propose an approach to manage variability in IEC 61499-based systems using delta-oriented variability modeling. We discuss open challenges and outline a research agenda for variability management in IEC 61499.
{"title":"Towards Delta-Oriented Variability Modeling for IEC 61499","authors":"H. Fadhlillah, Bianca Wiesmayr, Michael Oberlehner, Rick Rabiser, Alois Zoitl","doi":"10.1109/ETFA45728.2021.9613293","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613293","url":null,"abstract":"Modern production plants are complex Cyber-Physical Production Systems with an ever-increasing share of software controlling and automating their operation. The customization of these systems to the needs of their customers and their frequent evolution over a typically long life-cycle result in a plethora of variants to be managed. However, reuse still is mainly done in an opportunistic way, relying on copy-paste-modify strategies. More systematic, strategic reuse would help to reduce costs and time to market, but requires approaches that can be applied to domain-specific languages for developing distributed control software. In this paper, we propose an approach to manage variability in IEC 61499-based systems using delta-oriented variability modeling. We discuss open challenges and outline a research agenda for variability management in IEC 61499.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125441023","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 : 2021-09-07DOI: 10.1109/ETFA45728.2021.9613265
Deniz Neufeld, Ute Schmid
This work focuses on computationally efficient difference metrics of time series and compares two different unsupervised methods for anomaly classification. It takes place in the domain of hardware systems testing for reliability, where several structurally identical devices are tested at the same time with a load expected in their lifetime use. The devices perform different maneuvers in predefined testing cycles. It is possible that rare, unexpected system defects appear. They often show up in the measured data signals of the system, for example as a decrease in the output pressure of a pump. Due to the intended aging of the parts under load, the measured data also exhibits a concept drift, i.e. a shift in the data distribution. It is of interest to detect anomalous behavior as early as possible to reduce cost, save time and enable accurate root-cause-analysis. We formulate this problem as an anomaly detection task on periodic multivariate time series data. Experiments are evaluated using an open access hydraulic test bench data set by Helwig et al. [1]. The method's performance under concept drift is tested by simulating an aging system using the same data set. We find that Mean Squared Error towards the median in combination with the Modified z-Score is the most robust method for this use case. The solution can be applied from the beginning of a hardware testing cycle. The computations are intuitive to understand, and the classification results can be visualized for better interpretability and plausibility analysis.
{"title":"Anomaly Detection for Hydraulic Systems under Test","authors":"Deniz Neufeld, Ute Schmid","doi":"10.1109/ETFA45728.2021.9613265","DOIUrl":"https://doi.org/10.1109/ETFA45728.2021.9613265","url":null,"abstract":"This work focuses on computationally efficient difference metrics of time series and compares two different unsupervised methods for anomaly classification. It takes place in the domain of hardware systems testing for reliability, where several structurally identical devices are tested at the same time with a load expected in their lifetime use. The devices perform different maneuvers in predefined testing cycles. It is possible that rare, unexpected system defects appear. They often show up in the measured data signals of the system, for example as a decrease in the output pressure of a pump. Due to the intended aging of the parts under load, the measured data also exhibits a concept drift, i.e. a shift in the data distribution. It is of interest to detect anomalous behavior as early as possible to reduce cost, save time and enable accurate root-cause-analysis. We formulate this problem as an anomaly detection task on periodic multivariate time series data. Experiments are evaluated using an open access hydraulic test bench data set by Helwig et al. [1]. The method's performance under concept drift is tested by simulating an aging system using the same data set. We find that Mean Squared Error towards the median in combination with the Modified z-Score is the most robust method for this use case. The solution can be applied from the beginning of a hardware testing cycle. The computations are intuitive to understand, and the classification results can be visualized for better interpretability and plausibility analysis.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289156","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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