自主智能系统(英文)最新文献_第5页

A service-oriented energy assessment system based on BPMN and machine learning 基于BPMN和机器学习的面向服务的能源评估系统

自主智能系统(英文) Pub Date : 2022-08-11 DOI: 10.1007/s43684-022-00036-0

Wei Yan, Xinyi Wang, Qingshan Gong, Xumei Zhang, Hua Zhang, Zhigang Jiang

{"title":"A service-oriented energy assessment system based on BPMN and machine learning","authors":"Wei Yan, Xinyi Wang, Qingshan Gong, Xumei Zhang, Hua Zhang, Zhigang Jiang","doi":"10.1007/s43684-022-00036-0","DOIUrl":"10.1007/s43684-022-00036-0","url":null,"abstract":"<div><p>Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00036-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43424453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging on non-causal reasoning techniques for enhancing the cognitive management of highly automated vehicles 利用非因果推理技术增强高度自动化车辆的认知管理

自主智能系统(英文) Pub Date : 2022-08-10 DOI: 10.1007/s43684-022-00035-1

Ilias Panagiotopoulos, George Dimitrakopoulos

{"title":"Leveraging on non-causal reasoning techniques for enhancing the cognitive management of highly automated vehicles","authors":"Ilias Panagiotopoulos, George Dimitrakopoulos","doi":"10.1007/s43684-022-00035-1","DOIUrl":"10.1007/s43684-022-00035-1","url":null,"abstract":"<div><p>Highly Automated Vehicles (HAVs) are expected to improve the performance of terrestrial transportations by providing safe and efficient travel experience to drivers and passengers. As HAVs will be equipped with different driving automation levels, they should be capable to dynamically adapt their Level of Autonomy (LoA), in order to tackle sudden and recurrent changes in their environment (i.e., inclement weather, complex terrain, unexpected on-road obstacles, etc.). In this respect, HAVs should be able to respond not only on causal reasoning effects, which depend on present and past inputs from the external driving environment, but also on non-causal reasoning situations depending on future states associated with the external driving scene. On the other hand, driver’s personal preferences and profile characteristics should be assessed and managed properly, in order to enhance travel experience. In the light of the above, the present paper aims to tackle these challenges on how cognitive computing enables HAVs to operate each time in the best available LoA by responding quickly to changing environment situations and driver’s preferences. On this basis, an in-vehicle cognitive functionality is introduced, which collects data from various sources (sensor and driver layers), intelligently processing it to the decision-making layer, and finally, selecting the optimal LoA by integrating previous knowledge and experience. The overall approach includes the identification and utilization of a hybrid (data-driven and event-driven) algorithmic process towards reaching intelligent and proactive decisions. An indicative discrete event simulation analysis showcases the efficiency of the developed approach in proactively adapting the vehicle’s LoA.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00035-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45449081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning-based approach for electrical equipment remaining useful life prediction 一种基于深度学习的电气设备剩余使用寿命预测方法

自主智能系统(英文) Pub Date : 2022-07-27 DOI: 10.1007/s43684-022-00034-2

Huibin Fu, Ying Liu

{"title":"A deep learning-based approach for electrical equipment remaining useful life prediction","authors":"Huibin Fu, Ying Liu","doi":"10.1007/s43684-022-00034-2","DOIUrl":"10.1007/s43684-022-00034-2","url":null,"abstract":"<div><p>Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00034-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41769314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning-based approach for product maintenance prediction with reliability information conversion 基于机器学习的可靠性信息转换产品维修预测方法

自主智能系统(英文) Pub Date : 2022-07-14 DOI: 10.1007/s43684-022-00033-3

Hua Zhang, Xue He, Wei Yan, Zhigang Jiang, Shuo Zhu

{"title":"A machine learning-based approach for product maintenance prediction with reliability information conversion","authors":"Hua Zhang, Xue He, Wei Yan, Zhigang Jiang, Shuo Zhu","doi":"10.1007/s43684-022-00033-3","DOIUrl":"10.1007/s43684-022-00033-3","url":null,"abstract":"<div><p>Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00033-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46998657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dynamic customer requirement mining method for continuous product improvement 一种用于产品持续改进的动态客户需求挖掘方法

自主智能系统(英文) Pub Date : 2022-07-01 DOI: 10.1007/s43684-022-00032-4

Qian Zhao, Wu Zhao, Xin Guo, Kai Zhang, Miao Yu

{"title":"A dynamic customer requirement mining method for continuous product improvement","authors":"Qian Zhao, Wu Zhao, Xin Guo, Kai Zhang, Miao Yu","doi":"10.1007/s43684-022-00032-4","DOIUrl":"10.1007/s43684-022-00032-4","url":null,"abstract":"<div><p>The key to successful product development is better understanding of customer requirements and efficiently identifying the product attributes. In recent years, a growing number of researchers have studied the mining of customer requirements and preferences from online reviews. However, since customer requirements often change dynamically on multi-generation products, most existing studies failed to discover the correlations between customer satisfaction and continuous product improvement. In this work, we propose a novel dynamic customer requirement mining method to analyze the dynamic changes of customer satisfaction of product attributes based on sentiment and attention expressed in online reviews, aiming to better meet customer requirements and provide the direction and content of future product improvement. Specifically, this method is divided into three parts. Firstly, text mining is adopted to collect online review data of multi-generation products and identify product attributes. Secondly, the attention and sentiment scores of product attributes are calculated with a natural language processing tool, and further integrated into the corresponding satisfaction scores. Finally, the improvement direction for next-generation products is determined based on the changing satisfaction scores of multi-generation product attributes. In addition, a case study on multi-generation phone products based on online reviews was conducted to illustrate the effectiveness and practicality of the proposed methodology. Our research completes the field of requirements analysis and provides a new dynamic approach to requirements analysis for continuous improvement of multi-generation products, which can help enterprises to accurately understand customer requirements and improve the effectiveness and efficiency of continuous product improvement.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00032-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49082509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning phase in a LIVE Digital Twin for predictive maintenance 用于预测性维护的实时数字孪生学习阶段

自主智能系统(英文) Pub Date : 2022-06-02 DOI: 10.1007/s43684-022-00028-0

Andrew E. Bondoc, Mohsen Tayefeh, Ahmad Barari

{"title":"Learning phase in a LIVE Digital Twin for predictive maintenance","authors":"Andrew E. Bondoc, Mohsen Tayefeh, Ahmad Barari","doi":"10.1007/s43684-022-00028-0","DOIUrl":"10.1007/s43684-022-00028-0","url":null,"abstract":"<div><p>Digital Twins are essential in establishing intelligent asset management for an asset or machine. They can be described as the bidirectional communication between a cyber representation and a physical asset. Predictive Maintenance is dependent on the existence of three data sets: <i>Fault history</i>, <i>Maintenance</i>/<i>Repair History</i>, and <i>Machine Conditions</i>. Current Digital Twin solutions can fail to simulate the behaviour of a faulty asset. These solutions also prove to be difficult to implement when an asset’s fault history is incomplete. This paper presents the novel methodology, LIVE Digital Twin, to develop Digital Twins with the focus of Predictive Maintenance. The four phases, Learn, Identify, Verify, and Extend are discussed. A case study analyzes the relationship of component stiffness and vibration in detecting the health of various components. The Learning phase is implemented to demonstrate the process of locating a preliminary sensor network and develop the faulty history of a Sand Removal Skid assembly. Future studies will consider fewer simplifying assumptions and expand on the results to implement the proceeding phases.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00028-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48505202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault diagnosis of industrial robot based on dual-module attention convolutional neural network 基于双模注意卷积神经网络的工业机器人故障诊断

自主智能系统(英文) Pub Date : 2022-06-01 DOI: 10.1007/s43684-022-00031-5

Kaijie Lu, Chong Chen, Tao Wang, Lianglun Cheng, Jian Qin

{"title":"Fault diagnosis of industrial robot based on dual-module attention convolutional neural network","authors":"Kaijie Lu, Chong Chen, Tao Wang, Lianglun Cheng, Jian Qin","doi":"10.1007/s43684-022-00031-5","DOIUrl":"10.1007/s43684-022-00031-5","url":null,"abstract":"<div><p>Fault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00031-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48044082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An attention enhanced dilated CNN approach for cross-axis industrial robotics fault diagnosis 一种用于十字轴工业机器人故障诊断的注意力增强扩张CNN方法

自主智能系统(英文) Pub Date : 2022-05-31 DOI: 10.1007/s43684-022-00030-6

Yuxin Liu, Chong Chen, Tao Wang, Lianglun Cheng

{"title":"An attention enhanced dilated CNN approach for cross-axis industrial robotics fault diagnosis","authors":"Yuxin Liu, Chong Chen, Tao Wang, Lianglun Cheng","doi":"10.1007/s43684-022-00030-6","DOIUrl":"10.1007/s43684-022-00030-6","url":null,"abstract":"<div><p>An industrial robot is a complex mechatronics system, whose failure is hard to diagnose based on monitoring data. Previous studies have reported various methods with deep network models to improve the accuracy of fault diagnosis, which can get an accurate prediction model when the amount of data sample is sufficient. However, the failure data is hard to obtain, which leads to the few-shot issue and the bad generalization ability of the model. Therefore, this paper proposes an attention enhanced dilated convolutional neural network (D-CNN) approach for the cross-axis industrial robotics fault diagnosis method. Firstly, key feature extraction and sliding window are adopted to pre-process the monitoring data of industrial robots before D-CNN is introduced to extract data features. And self-attention is used to enhance feature attention capability. Finally, the pre-trained model is used for transfer learning, and a small number of the dataset from another axis of the multi-axis industrial robot are used for fine-tuning experiments. The experimental results show that the proposed method can reach satisfactory fault diagnosis accuracy in both the source domain and target domain.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00030-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46725199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed constrained aggregative games of uncertain Euler-Lagrange systems under unbalanced digraphs 不平衡有向图下不确定欧拉-拉格朗日系统的分布约束聚合对策

自主智能系统(英文) Pub Date : 2022-05-27 DOI: 10.1007/s43684-022-00027-1

Yanqiong Zhang, Chaoqun Liu, Yu-Ping Tian

引用次数: 0

Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning 基于深度强化学习的顺序合作任务多智能体协调行为两阶段奖励衰减分配

自主智能系统(英文) Pub Date : 2022-05-27 DOI: 10.1007/s43684-022-00029-z

Yuki Miyashita, Toshiharu Sugawara

{"title":"Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning","authors":"Yuki Miyashita, Toshiharu Sugawara","doi":"10.1007/s43684-022-00029-z","DOIUrl":"10.1007/s43684-022-00029-z","url":null,"abstract":"<div><p>We propose a two-stage reward allocation method with decay using an extension of replay memory to adapt this rewarding method for deep reinforcement learning (DRL), to generate coordinated behaviors for tasks that can be completed by executing a few subtasks sequentially by heterogeneous agents. An independent learner in cooperative multi-agent systems needs to learn its policies for effective execution of its own responsible subtask, as well as for coordinated behaviors under a certain coordination structure. Although the reward scheme is an issue for DRL, it is difficult to design it to learn both policies. Our proposed method attempts to generate these different behaviors in multi-agent DRL by dividing the timing of rewards into two stages and varying the ratio between them over time. By introducing the coordinated delivery and execution problem with an expiration time, where a task can be executed sequentially by two heterogeneous agents, we experimentally analyze the effect of using various ratios of the reward division in the two-stage allocations on the generated behaviors. The results demonstrate that the proposed method could improve the overall performance relative to those with the conventional one-time or fixed reward and can establish robust coordinated behavior.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00029-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45075022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0