2022 4th International Conference on System Reliability and Safety Engineering (SRSE)最新文献

Capacity and RUL Prediction of Retired Batteries Using Machine Learning Features 使用机器学习特征的退役电池容量和RUL预测

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067595

Qingcheng Yang, Yanhua Chen, X. Ye, Tianpei Liu, Yuyi Tan, Wei Peng

{"title":"Capacity and RUL Prediction of Retired Batteries Using Machine Learning Features","authors":"Qingcheng Yang, Yanhua Chen, X. Ye, Tianpei Liu, Yuyi Tan, Wei Peng","doi":"10.1109/SRSE56746.2022.10067595","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067595","url":null,"abstract":"With the widespread application of lithium batteries, estimation of the capacity and remaining life of retired batteries has become an important issue. Traditional battery calibration tests bring high cost, and cannot estimate health status of batteries in time. For life prediction problems, retired batteries have not drawn much attention. Existing studies often require many parameters and cannot predict battery capacity and remaining life at the same time. Therefore, we propose a method that can jointly predict capacity and remaining life of retired lithium batteries using less parameters and data. Our method contains two steps: (1) capacity prediction using features extracted from parameters, (2) remaining useful life prediction using coefficient related to cycle index and former extracted features. Regression models are selected to complete two steps. In this article, basic definitions of parameters are given and datasets are introduced first. Then, battery health features containing mean value, skewness and kurtosis are extracted, as well as curve features with the concept of Frechet and Hausdorff Distance. Experiments are conducted for each type of feature by constructing Random Forest regressor, AdaBoost regressor and other 3 models on datasets, whose performances are measured by RMSE, MAE, MAPE and R2 score. The optimal model pair are selected as the prediction model.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440086","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}

引用次数: 0

Overview of the Application of Knowledge Graph in Anomaly Detection and Fault Diagnosis 知识图谱在异常检测与故障诊断中的应用综述

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067308

Peizheng Huang, Shulin Liu, Kuan Zhang, Tao Xu, X. Yi

引用次数: 1

The Study of Activation Energy of Electromigration at Different Temperature 不同温度下电迁移活化能的研究

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067678

Ning Li, Zhijian Chen, Xiaowen Zhang

{"title":"The Study of Activation Energy of Electromigration at Different Temperature","authors":"Ning Li, Zhijian Chen, Xiaowen Zhang","doi":"10.1109/SRSE56746.2022.10067678","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067678","url":null,"abstract":"In this paper, electromigration (EM) activation energy versus temperature in advanced damascene copper lines has been studied. EM experiments have been carried out on fully back-end processed samples under the condition of different temperatures and current densities. The metal line test structures used have the four-terminal Kelvin configuration and the lines length is 800µm with the minimum design rule width. The specific location of failure was observed by scanning electron microscope (SEM) and the failure mechanism was analyzed. The mechanical stress of the test structure was measured by Raman spectroscopy and the effect of mechanical stress on the reliability of metal electromigration was studied. It is found that the activation energy decreases slowly with the decrease of the test temperature. When the activation energy acquired under high temperature environment was extrapolated to operation condition by quadratic polynomial fitting, the activation energy under the environment temperature of 300°C is different from the value under the operation condition. Meanwhile, when predicting the EM lifetime, the results is that the EM lifetime under the operation condition decreased to 18% compared to that under the environment temperature of 300°C.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116813104","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}

引用次数: 0

A Novel Hybrid Neural Network with Attentive Feature Selection for Degradation Status Identification of Aircraft Self-locking Nuts 一种新颖的带有注意特征选择的混合神经网络用于飞机自锁螺母退化状态识别

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067512

Zhang Wenjing, Ma Yulin, Xu Yanwei, Liang Xinfu, Qi Le, Yang Jun, Li Lei

{"title":"A Novel Hybrid Neural Network with Attentive Feature Selection for Degradation Status Identification of Aircraft Self-locking Nuts","authors":"Zhang Wenjing, Ma Yulin, Xu Yanwei, Liang Xinfu, Qi Le, Yang Jun, Li Lei","doi":"10.1109/SRSE56746.2022.10067512","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067512","url":null,"abstract":"The self-locking nuts are widely used to connect structures in aerospace assembly lines. As these parts are usually installed in regions that suffered from heavy shocks and vibrations, the status of such essential parts closely relates to the safety and reliability of the aircraft. To enable precise sensing of these nuts and improve the system reliability, this paper proposes a hybrid neural network with a novel feature selection module for the identification of its degradation status. Specifically, the temporal tendency information and spatial fault patterns of monitored degradation torques are captured through a long- short-term memory (LSTM) network and a stacked Convolutional neural network (CNN) respectively. Besides, to effectively integrate these dual networks, a novel attention module absorbing the temporal features is proposed to reweight the spatial convolutional features. In particular, to explore fault information in the presence of multiple monitored torques, a regularized multi-task classifier is introduced to learn diverse representations. Experiments based on an industrial self-locking dataset proved that the proposed method possesses an accurate identification capability of degradation status than conventional neural networks.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121182622","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}

引用次数: 0

Human-Machine Trust and Calibration Based on Human-in-the-Loop Experiment 基于人在环实验的人机信任与标定

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067635

Yifan Wang, Jianbin Guo, S. Zeng, Qirui Mao, Zhenping Lu, Zengkai Wang

{"title":"Human-Machine Trust and Calibration Based on Human-in-the-Loop Experiment","authors":"Yifan Wang, Jianbin Guo, S. Zeng, Qirui Mao, Zhenping Lu, Zengkai Wang","doi":"10.1109/SRSE56746.2022.10067635","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067635","url":null,"abstract":"While the automation system brings efficiency improvements, people's trust in the automation system has become an important factor affecting the safety of the human-machine system. The operator's unsuitable trust in the automation system (such as undertrust and overtrust) makes the human-automation system not always well matched. In this paper, we took the aircraft engine fire alarm system as the research scene, carried out the human-in-the-loop simulation experiment by injecting aircraft engine fire alarms, and used the subjective report method to measure the trust level of the subject. Then, based on the experimental data, we studied the laws of human-machine trust, including the law of trust anchoring (that is, in the case of anchoring with a known false alarm rate, the subject's trust fluctuation range is smaller than that of the unknown false alarm rate), trust elasticity, and primacy effect. A human-machine trust calibration method was proposed to prevent undertrust and overtrust in the process of human-machine interaction, and different forms of calibration methods were verified. It was found that reminding the subjects when the human error probability (HEP) ≥ 0.3 and at the same time declaring whether the source of human error is overtrust or undertrust is a more effective calibration method, which can generally reduce the human error probability.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124121259","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}

引用次数: 0

Some Extended Geometric Processes and Their Estimation Methods 一些扩展几何过程及其估计方法

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067726

Jiaqi Yin, Shaomin Wu

引用次数: 0

Research on State Safety Programme Maturity Assessment Model of Civil Aviation 民航国家安全规划成熟度评价模型研究

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067701

Lin Diao, Mingliang Chen, Yanqiu Chen

引用次数: 0

An XOR-Based Pico-Physically Unclonable Function for Securing IoT Devices 用于保护物联网设备的基于xor的微物理不可克隆功能

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067307

Junjun Wang, Jinhui Liu, Zhao Huang, Quan Wang

{"title":"An XOR-Based Pico-Physically Unclonable Function for Securing IoT Devices","authors":"Junjun Wang, Jinhui Liu, Zhao Huang, Quan Wang","doi":"10.1109/SRSE56746.2022.10067307","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067307","url":null,"abstract":"Physically unclonable function (PUF) can be applied as a lightweight way to improve the security of Internet of Thing (IoT) devices. In the existing PUF studies, reconfigurable Pico-PUF (RPPUF) is an effective solution with good uniqueness and reliability. However, it still has a limited key space and requires extra hardware resources to generate more challenge-response pairs (CRPs). Therefore, this paper improves the RPPUF and proposes a lightweight XOR-based Pico-PUF, namely XORPPUF. By replacing each NOT gate in the configurable logic with an XOR gate, the key space is effectively expanded while preserving the PUF performance. We have implemented and verified the proposed XORPPUF on Xilinx Spartan-6 XC6SLX25 microboards. The experimental results show that XORPPUF achieves 40.06% uniqueness and 99.49% temperature reliability. Compared with the RPPUF, our work improves temperature reliability by 0.26%, expands key space by 2n, and reduces hardware resources overhead by 11.3% when generating a 128-bit PUF response. In addition, the prediction rate of our XORPPUF against Decision Tree (DT) and Random Forest (RF)-based modeling attacks is 34.17% and 39.40% lower than RPPUF, respectively. This means XORPPUF performs better resistant than RPPUF in Machine Learning (ML) attack. Thus, it is more suitable for securing the IoT devices with limited resources.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902457","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}

引用次数: 1

An Application of Cognitive Workload and Situational Awareness Assessment in Human Safety Demonstration of NPP 认知负荷与态势感知评估在核电厂人身安全论证中的应用

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067543

Zhihui Xu, Hang Liu, Can Zhou, Yunqing Gao, Jipu Wang, Yu-xin Zhang, Jun Yang, Ming Yang

引用次数: 0

Resilience Modeling of Interdependent Supply Chain Networks with Company Collaboration Against Ripple Effects 基于公司协作的相互依赖供应链网络抗涟漪效应弹性模型

2022 4th International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2022-12-15 DOI: 10.1109/SRSE56746.2022.10067730

Lei Zhang, Jian Zhou, D. Coit, Yizhong Ma

{"title":"Resilience Modeling of Interdependent Supply Chain Networks with Company Collaboration Against Ripple Effects","authors":"Lei Zhang, Jian Zhou, D. Coit, Yizhong Ma","doi":"10.1109/SRSE56746.2022.10067730","DOIUrl":"https://doi.org/10.1109/SRSE56746.2022.10067730","url":null,"abstract":"This study investigates interdependent supply chain network resilience (ISCNR) in the presence of ripple effects, i.e., the phenomenon that the disruptions at some companies also impact the operation of other companies in the same supply chain network (SCN), and even collapse a large part of the SCN. Some researchers have studied ISCNR considering the ripple effect. However, few of them explore the phenomenon taking into account the influences of network structure and company cooperation. In practice, when a company is at risk of failure, its partner companies in the same ISCN may help it to shed the risk through proper cooperation. In this research, a new model for ISCNR against ripple effects considering company collaboration is proposed. A multi-dimension quantitative framework is also developed to measure ISCNR, which considers three resilience dimensions based on three network performance indicators. Then, with a new ISCN model, we investigate the ISCNR considering company collaboration under random and malicious attacks. The simulation results show that company collaboration can enhance ISCNR against ripple effects, while in some cases, company collaboration may adversely impact ISCNR. This research provides insights on improving supply chain network resilience considering the impact of real-world company collaboration to mitigate the intensity of ripple effects.","PeriodicalId":147308,"journal":{"name":"2022 4th International Conference on System Reliability and Safety Engineering (SRSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323198","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}

引用次数: 1