自主智能系统(英文)最新文献_第7页

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":"2 1","pages":""},"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":"2 1","pages":""},"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":"2 1","pages":""},"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":"2 1","pages":""},"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":"2 1","pages":""},"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":"2 1","pages":""},"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":"2 1","pages":""},"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

Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure 机械系统和民用基础设施的机器人和自主检测的机器学习技术

自主智能系统(英文) Pub Date : 2022-04-29 DOI: 10.1007/s43684-022-00025-3

Michael O. Macaulay, Mahmood Shafiee

{"title":"Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure","authors":"Michael O. Macaulay, Mahmood Shafiee","doi":"10.1007/s43684-022-00025-3","DOIUrl":"10.1007/s43684-022-00025-3","url":null,"abstract":"<div><p>Machine learning and in particular <i>deep learning</i> techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00025-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42816843","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

Nash equilibrium seeking over directed graphs 有向图上的纳什均衡寻求

自主智能系统(英文) Pub Date : 2022-04-18 DOI: 10.1007/s43684-022-00026-2

Yutao Tang, Peng Yi, Yanqiong Zhang, Dawei Liu

引用次数: 0