2023 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第3页

Experimental Setups for Linear Feed Drive Predictive Maintenance: A Review 线性进给传动预见性维修实验装置综述

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194225

Brett S. Sicard, Quade Butler, Youssef Ziada, S. Gadsden

{"title":"Experimental Setups for Linear Feed Drive Predictive Maintenance: A Review","authors":"Brett S. Sicard, Quade Butler, Youssef Ziada, S. Gadsden","doi":"10.1109/ICPHM57936.2023.10194225","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194225","url":null,"abstract":"The manufacturing world has advanced to the fourth industrial revolution (4IR). Machine tools, especially computer numerical control (CNC) machine tools are an essential part of manufacturing. An important part of the 4IR is predictive maintenance (PM). PM is key in ensuring the availability and high quality of parts produced by machine tools. An important part of CNC machine tools is their feed drives. It is essential to implement PM to keep these components in good working order. Often PM methods will need to be developed and tested on experimental setups before they can be implemented in production. This work examines the literature on experimental setups for feed drive condition monitoring, fault detection and PM and seeks to disseminate and organize information about methods and equipment used in these setups. Three primary factors were analyzed from these papers: the methods used to implement wear and faults, the external loading methods, and which sensors were used and where the sensors were installed. This work seeks to aid others who wish to create their own experimental setup to easily access information about the experimental setups of previous works on linear feed drive PM. A few trends were observed after examining the literature. A large quantity of experimental setups studied faults in ball screws, specifically preload in ball screws. A wide variety of sensors were used, the most popular being accelerometers. There was a lack of methods to implement external loading, with most papers using adjustable worktable weights or magnetic brakes.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130291764","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

Using Digital Twins for CBM+ and RAMS Decision Support 利用数字孪生进行CBM+和RAMS决策支持

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194013

Vincent Mendoza, John V. Morgan, Marlene Haag

引用次数: 0

Data-driven Health Monitoring and Anomaly Detection in Aircraft Shock Absorbers 数据驱动的飞机减震器健康监测与异常检测

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194159

Josí Joaquín Mendoza Lopetegui, Gianluca Papa, M. Tanelli

引用次数: 0

Gradient feature-based method for Defect Detection of Carbon Fiber Reinforced Polymer Materials 基于梯度特征的碳纤维增强聚合物材料缺陷检测方法

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194165

Yamini Kotriwar, Obaid Elshafiey, Lei Peng, Zi Li, Vijay Srinivasan, Eric Davis, Y. Deng

{"title":"Gradient feature-based method for Defect Detection of Carbon Fiber Reinforced Polymer Materials","authors":"Yamini Kotriwar, Obaid Elshafiey, Lei Peng, Zi Li, Vijay Srinivasan, Eric Davis, Y. Deng","doi":"10.1109/ICPHM57936.2023.10194165","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194165","url":null,"abstract":"The structural and material aging of the energy and transportation infrastructure requires the development of faster, better, and more efficient non-destructive evaluation (NDE) techniques to assess remaining life and structural health for prognostics and structural health management. Composite materials such as carbon fiber reinforced polymer (CFRP) have beneficial properties such as corrosion resistance, durability, and lightweight, which reduce maintenance requirements and extend service life. Their an-isotropic dielectric and mechanical properties make it challenging for NDE techniques to detect and locate material defects. A miniaturized capacitive imaging system was developed to detect multiple types of defects in CFRP materials. However, algorithms to convert the raw imaging data into defect detection, classification, sizing, and location is not currently available. This paper presents a defect localization algorithm using a gradient response feature-based method to reduce the noise in the imaging data. The algorithm calculates the co-occurrence matrix of the image. From this matrix, the local features such as contrast, homogeneity, energy, and correlation are extracted. A combination of these features is selected to define a defect area. The features extracted from the image processing are classified using a support vector machine (SVM) algorithm. The location of the defects identified through the algorithm is compared with the ground truth to achieve a probability of detection of 82%.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126255759","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

Fault diagnosis of rolling bearing using a transfer ensemble deep reinforcement learning method 基于传递集成深度强化学习方法的滚动轴承故障诊断

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194014

Zhenning Li, Hongkai Jiang, Shaowei Liu, Ruixin Wang

{"title":"Fault diagnosis of rolling bearing using a transfer ensemble deep reinforcement learning method","authors":"Zhenning Li, Hongkai Jiang, Shaowei Liu, Ruixin Wang","doi":"10.1109/ICPHM57936.2023.10194014","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194014","url":null,"abstract":"The reliable operation of rolling bearings is related to machinery safety. However, fault signals encountered in practical engineering applications are often characterized by high-dimensionality, complexity, and volume, which restricts the application of deep neural networks in fault diagnosis. Additionally, conventional diagnostic methods are limited by their reliance on manual feature extraction and a significant quantity of labeled samples, which can be time-consuming and resource-intensive. To address these limitations and improve the performance of fault diagnosis in the absence of labeled samples, an intelligent diagnostic agent (TERL-Agent) that combines transfer learning, ensemble learning and reinforcement learning is proposed. Firstly, an intelligent diagnostic agent is constructed by ensemble learning, which combines multiple reinforcement learning agents based on the Deep Q Network structure and has interactive learning capability to learn and classify fault data in the source domain environment. Secondly, transfer learning is used to transfer the feature extraction ability of the source domain intelligent diagnostic agent to the target intelligent diagnostic agent. Finally, the obtained target intelligent diagnostic agent is evaluated on fault data in the target domain and compared with other methods. The results indicate that the proposed method exhibits remarkable advantages and has great potential for practical application in fault diagnosis.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129206576","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

Exploring the Use of PHM for Software System Security and Resilience 探索PHM在软件系统安全性和弹性中的应用

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10193932

Rajesh Murthy

引用次数: 0

Optimizing Data Training Quantity for Bearing Condition Monitoring 优化轴承状态监测数据训练量

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10193864

Ethan Wescoat, Vinita Jansari, L. Mears

{"title":"Optimizing Data Training Quantity for Bearing Condition Monitoring","authors":"Ethan Wescoat, Vinita Jansari, L. Mears","doi":"10.1109/ICPHM57936.2023.10193864","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10193864","url":null,"abstract":"Prognostics Health Management (PHM) in manu-facturing seeks to reduce the amount of unexpected downtime that inhibits manufacturing competitiveness. However, a common challenge for the manufacturing industry is the lack of known failure data to train a predictive classifier. This work optimizes the necessary quantity of required failure training data and healthy data for three different exemplar datasets by assessing classifier performance. Particle swarm optimization with penalty factors associated with the training data amount were used to identify the required training data amount for fault classification. Two separate analysis cases are considered: a binary classification and multi-class classification case termed the progressive case. In both analysis cases, the optimal training data depended on how separable the bearing data were between the different baseline and defect stages. In those instances where the differences in the data classes were apparent, the bearing data optimal training data amount was lower than in those instances where the data class differences were not present. Future work focuses on the investigation of these overlap cases to determine the best means for classifying progressive damage for remaining useful life calculations.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034300","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

Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation 电化学阻抗谱(EIS)和基于机器学习的电池健康状态(SoH)估计

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194065

Masuda A. Tonima, Austin DeHart, Deniz Tabakci, Piramon Tisapramotkul, Andrew Munro-West, Aarushi Mehra, T. Shoa

{"title":"Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation","authors":"Masuda A. Tonima, Austin DeHart, Deniz Tabakci, Piramon Tisapramotkul, Andrew Munro-West, Aarushi Mehra, T. Shoa","doi":"10.1109/ICPHM57936.2023.10194065","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194065","url":null,"abstract":"While Li-ion batteries have proven long lifetimes, an accurate assessment of the battery ca-pacity and its remaining life cannot yet be made using current Battery Management Systems (BMS) devices. Battery sensors used in BMS typically mon-itor voltage, current and temperature of the battery, in order to predict the state of health (SoH) of the battery. SoH is a measure that indicates the remaining capacity that had been affected by degradation. Information obtained by monitoring voltage, current and temperature are often not sufficient to predict SoH. In this study we captured extra information from interfacial layers of the battery through applying Electrochemical Impedance Spectroscopy (EIS) and employed a XGBoost-based machine learning approach to train our models. The results show that SoH of batteries can be predicted with 90% accuracy, 95% confidence and 82% reliability. Additionally, it was shown that accuracy could be maintained with little to no change even when the number of features was dramatically reduced and the sample size was minimal, thus making this method very practical for embedded EIS/AI based solutions.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131268963","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

Robust Contrastive Learning and Multi-shot Voting for High-dimensional Multivariate Data-driven Prognostics 高维多元数据驱动预测的鲁棒对比学习和多镜头投票

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194050

Kaiji Sun, S. Magnússon, O. Steinert, Tony Lindgren

引用次数: 0

A semi-supervised RUL prediction with likelihood-based pseudo labeling for suspension histories 基于似然的悬架历史伪标记的半监督规则学习预测

2023 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2023-06-05 DOI: 10.1109/ICPHM57936.2023.10194226

Ryosuke Takayama, Masanao Natsumeda, T. Yairi

{"title":"A semi-supervised RUL prediction with likelihood-based pseudo labeling for suspension histories","authors":"Ryosuke Takayama, Masanao Natsumeda, T. Yairi","doi":"10.1109/ICPHM57936.2023.10194226","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194226","url":null,"abstract":"Accurate remaining useful life (RUL) prediction is an essential for efficient maintenance. In recent years, with the rapid development of industrial big data, many data-driven methods for RUL prediction have made significant progress, especially using deep learning. However, most of the proposed deep learning models only utilize labeled data and require a large amount of labeled data. In practice, the component of equipment is often replaced with a new one before it fails by preventive maintenance, resulting in a small number of failure histories and a large number of suspension histories. In other words, we have a small amount of labeled data and a large amount of unlabeled data. This paper proposes a new semi-supervised RUL prediction method using pseudo labels with flexibility in model architecture and low computational cost. For each suspension history, optimal pseudo labels are estimated using a likelihood-based method that takes into account important constraints, which enables more effective use of the information in both failure and suspension histories. The experiments on the C-MAPSS dataset validate the prediction accuracy of the proposed approach and provide several insights.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130770407","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