International Journal of Prognostics and Health Management最新文献_第4页

Predictive Diagnosis in Axial Piston Pumps 轴向柱塞泵的预测诊断

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-03-25 DOI: 10.36001/ijphm.2023.v14i1.3393

Oliver Gnepper, Hannes Hitzer, Olaf Enge-Rosenblatt

{"title":"Predictive Diagnosis in Axial Piston Pumps","authors":"Oliver Gnepper, Hannes Hitzer, Olaf Enge-Rosenblatt","doi":"10.36001/ijphm.2023.v14i1.3393","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i1.3393","url":null,"abstract":"Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48605687","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

Validation of a Physics-based Prognostic Model with Incomplete Data 不完全数据下基于物理的预后模型的验证

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-03-11 DOI: 10.36001/ijphm.2023.v14i1.3283

A. Meghoe, R. Loendersloot, T. Tinga

引用次数: 0

An Enhanced Joint Indicator for Starter Failure Diagnostics in Auxiliary Power Unit 辅助动力装置起动器故障诊断的增强型联合指示器

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-25 DOI: 10.22215/jphm.v3i1.4154

Yu Zhang, Jie Liu, Houman Hanachi, Chunsheng Yang

引用次数: 0

Anomaly Detection for Early Failure Identification on Automotive Field Data 面向汽车现场数据早期故障识别的异常检测

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3123

Aditya Jain, Piyush Tarey

{"title":"Anomaly Detection for Early Failure Identification on Automotive Field Data","authors":"Aditya Jain, Piyush Tarey","doi":"10.36001/ijphm.2023.v14i3.3123","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i3.3123","url":null,"abstract":"The automotive industry is witnessing its next phase of transformation. The vehicles are getting defined by software, becoming intelligent, connected and more complex to design, develop and analyze. For these complex vehicles, prognostics and proactive maintenance has become ever more critical than before.OEMs and suppliers analyze probable failures that a vehicle component is likely to encounter, define fault codes to identify those failures, and provide procedure or guided steps to resolve them. For smarter vehicles, it is required that vehicles be capable to catch potential problems as soon as the component’s condition starts to deteriorate and becomes a failure. These failures could be known (defined) or new (undefined). Given the vehicle development timelines and increasing complexity, many problems are not analyzed at design stage and remain undetected before production. Hence, no fault code or test case exist for them. Diagnosing such problems become very difficult, postproduction.The aim of this paper is to propose a Machine Learning (ML) based framework which utilizes minimally labelled or unlabeled sensor data generated from a vehicle system at a given frequency. The framework utilizes an ML model to identify any anomalous behavior or aberration, and flag it for further review. This framework can be adopted on large amount of real time or time series data to identify known as well as undefined failures early. These models could be deployed on cloud or on edge (on vehicles) for analyzing real-time sensor data for a given system/component and flag any anomaly. It could further be utilized to create a part specific Predictive Maintenance (PM) model to provide proactive warnings and prevent downtime.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70086164","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

Ground Fault Diagnostics for Automotive Electronic Control Units 汽车电子控制单元的接地故障诊断

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3128

Xinyu Du, Shengbing Jiang, D. Zhou, Alaeddin Bani Milhim, Hossein Sadjadi

引用次数: 1

Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks 电动汽车电池充电系统和网络的智能维护

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3130

Yuan-Ming Hsu, Dai-Yan Ji, Marcella Miller, Xiaodong Jia, J. Lee

{"title":"Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks","authors":"Yuan-Ming Hsu, Dai-Yan Ji, Marcella Miller, Xiaodong Jia, J. Lee","doi":"10.36001/ijphm.2023.v14i3.3130","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i3.3130","url":null,"abstract":"Electric Vehicles (EVs) have become a trending topic in recent years due to the industry’s race for competitive pricing as well as environmental awareness. These concerns have led to increased research into the development of both affordable and environmentally friendly EV technology. This paper aims to review EV-related issues beginning with the component level, through the system level, based on intelligent maintenance aspects. The paper will also clarify the existing gaps in practical applications and highlight the potential opportunities related to the current issues in EVs for the EV industry moving forward. More specifically, we will briefly start with an overview of the fast-growing EV market, showing the urgent demand for Prognostics and Health Management (PHM) applications in the EV industry. At the component level, the issues of the major components such as the motor, battery, and charging system in EVs are elaborated, and the relevant PHM research of these components is surveyed to show the development in the era of EV expansion. Moreover, the impact of an increasing number of EVs at the system level such as power distribution systems and power grid are explored to uncover possible research in the future.\u0000The combination of existing PHM techniques and robust measurement or feature extraction methods can provide better solutions to address the motor, battery, or transformer issues at the component level. A comprehensive optimization and cybersecurity strategy will help to address the issues of the whole network at a system level. Four aspects of vision in the overall charging network – battery innovation, charging optimization, infrastructure evolution, and sustainability – that cover the demands of research in new battery materials, innovative charging techniques, new architectures of the charging network, and reliable waste treatment mechanisms are outlined. A conclusion is reached in this paper by summarizing the opportunities for future EV research and development.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41855643","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: 2

Special Issue on Advanced Diagnostics and Prognostics for Automotive Systems 汽车系统高级诊断和预测特刊

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3438

Yilu Zhang, R. Salehi, Shiyu Zhou, Xiaodong Jia, Jason A. Siegel

引用次数: 0

Machine Learning Based Approach for EVAP System Early Anomaly Detection Using Connected Vehicle Data 基于机器学习的车联网EVAP系统早期异常检测方法

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3122

Ala E. Omrani, Pankaj Kumar, A. Dudar, Michael Casedy, Steven Szwabowski, Brandon M. Dawson

{"title":"Machine Learning Based Approach for EVAP System Early Anomaly Detection Using Connected Vehicle Data","authors":"Ala E. Omrani, Pankaj Kumar, A. Dudar, Michael Casedy, Steven Szwabowski, Brandon M. Dawson","doi":"10.36001/ijphm.2023.v14i3.3122","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i3.3122","url":null,"abstract":"From automobile manufacturers’ perspective, reduction of warranty cost leads to less expenditures, which then yields higher profits. Hence, it is crucial to leverage the different methods and available tools to achieve such outcome. Connected vehicle data is one critical resource that can be a gamechanger, reducing the associated costs and improving the business profitability. This project uses Mode06 (On-Board diagnostics reported tests results) connected vehicle data along with contextual data to early detect EVAP and purge monitors’ anomalies. Early detection allows fixing the issue through software (SW) and/or hardware (HW) upgrades before it turns into a failure (preventive maintenance), yielding then system quality improvement. Root cause analysis, which can be developed based on the anomaly detection outcomes and which is not within the scope of this paper, allows diagnostics of HW and/or SW related issues in a timely manner and eventually be prepared ahead of time for system failures. In this paper, statistics-based early anomaly detection models, based on vehicle data and fleet data, are developed. The proposed solution is a generic tool that does not make assumptions on data distribution and can be adapted to other systems by tweaking mainly the data cleaning process. It also incorporates specific system definitions of abnormal behavior, which makes it more accurate compared to conventional anomaly detection tools, which are mainly affected by the imbalanced data and the EVAP and purge definition of an anomaly. When deployed with field data, the algorithm showed higher performance, compared to popular anomaly detection techniques, and proved that failures can be prevented through detection of the anomalies several weeks/miles before the actual fail.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41576911","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

Explainable Models for Multivariate Time-series Defect Classification of Arc Stud Welding 电弧螺柱焊多元时间序列缺陷分类的可解释模型

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3125

Sadra Naddaf-sh, M.-Mahdi Naddaf-Sh, Maxim Dalton, Soodabeh Ramezani, Amir R. Kashani, H. Zargarzadeh

{"title":"Explainable Models for Multivariate Time-series Defect Classification of Arc Stud Welding","authors":"Sadra Naddaf-sh, M.-Mahdi Naddaf-Sh, Maxim Dalton, Soodabeh Ramezani, Amir R. Kashani, H. Zargarzadeh","doi":"10.36001/ijphm.2023.v14i3.3125","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i3.3125","url":null,"abstract":"Arc Stud Welding (ASW) is widely used in many industries such as automotive and shipbuilding and is employed in building and jointing large-scale structures. While defective or imperfect welds rarely occur in production, even a single low-quality stud weld is the reason for scrapping the entire structure, financial loss and wasting time. Preventive machine learning-based solutions can be leveraged to minimize the loss. However, these approaches only provide predictions rather than demonstrating insights for characterizing defects and root cause analysis. In this work, an investigation on defect detection and classification to diagnose the possible leading causes of low-quality defects is proposed. Moreover, an explainable model to describe network predictions is explored. Initially, a dataset of multi-variate time-series of ASW utilizing measurement sensors in an experimental environment is generated. Next, a set of pre-possessing techniques are assessed. Finally, classification models are optimized by Bayesian black-box optimization methods to maximize their performance. Our best approach reaches an F1-score of 0.84 on the test set. Furthermore, an explainable model is employed to provide interpretations on per class feature attention of the model to extract sensor measurement contribution in detecting defects as well as its time attention.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43319839","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

Self-Adaptive Air-path Health Management for a Heavy Duty-Diesel Engine 重型柴油机的自适应气道健康管理

IF 2.1

International Journal of Prognostics and Health Management Pub Date : 2023-02-13 DOI: 10.36001/ijphm.2023.v14i3.3118

Tomas Poloni, P. Dickinson, Jianrui Zhang, Peng Zhou

{"title":"Self-Adaptive Air-path Health Management for a Heavy Duty-Diesel Engine","authors":"Tomas Poloni, P. Dickinson, Jianrui Zhang, Peng Zhou","doi":"10.36001/ijphm.2023.v14i3.3118","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i3.3118","url":null,"abstract":"This paper presents the air-path health management strategy with the ability to estimate the mass-flows and mitigate (adapt to) the air-path faults in the exhaust system of a heavy-duty diesel combustion engine equipped with a twin-scroll turbine. Based on the engine component models applied in the quasi-steady-state mass-balancing approach, two main engine mass-flow quantities are estimated: the Air mass-flow (AMF) and the Exhaust gas recirculation (EGR) mass-flow. The health management system is monitoring for three kinds of air-path faults that can occur through the combustion engine operation, related either to the after-treatment system, EGR valve, or to the turbine balance valve hardware. For each fault, a fault-mitigation strategy based on in-observer-reconfigurable mass-balance equations with excluded faulty component model and utilized exhaust pressure sensor is proposed. The applied observer is using the iterated Kalman filter (IKF) as the core fault mitigating solver for the quasi-steady-state mass-balancing problem. It is further demonstrated how the individual faults are robustly isolated using the Sequential Probability Ratio Test (SPRT). The strategy and results are validated using the test cycle driving data.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49314425","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