利用振动数据的 Mel-Frequency Cepstral 系数进行风力涡轮机齿轮箱早期故障检测

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Cristian Velandia-Cardenas, Yolanda Vidal, Francesc Pozo
{"title":"利用振动数据的 Mel-Frequency Cepstral 系数进行风力涡轮机齿轮箱早期故障检测","authors":"Cristian Velandia-Cardenas,&nbsp;Yolanda Vidal,&nbsp;Francesc Pozo","doi":"10.1155/2024/7733730","DOIUrl":null,"url":null,"abstract":"<div>\n <p>A methodology utilizing vibration data and Mel-frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost-effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time-consuming and labor-intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration-based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost-effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three-stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.</p>\n </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7733730","citationCount":"0","resultStr":"{\"title\":\"Wind Turbine Gearbox Early Fault Detection Using Mel-Frequency Cepstral Coefficients of Vibration Data\",\"authors\":\"Cristian Velandia-Cardenas,&nbsp;Yolanda Vidal,&nbsp;Francesc Pozo\",\"doi\":\"10.1155/2024/7733730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>A methodology utilizing vibration data and Mel-frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost-effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time-consuming and labor-intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration-based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost-effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three-stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.</p>\\n </div>\",\"PeriodicalId\":49471,\"journal\":{\"name\":\"Structural Control & Health Monitoring\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7733730\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Control & Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/7733730\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/7733730","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

本研究开发了一种利用振动数据和梅尔频率倒频谱系数(MFCC)进行风力涡轮机状态监测的方法,用于检测风力涡轮机齿轮箱中的初期故障。与依赖物理检测的传统状态监测技术相比,这种方法提供了一种更高效、更具成本效益的解决方案,因为物理检测可能会耗费大量时间和人力。使用振动数据可以识别风机运行状况的细微变化,提供潜在问题的早期预警信号。在对振动数据进行分析时,可以检测到频率和振幅的变化,这表明存在正在发展中的故障。基于振动的状态监测系统(CMS)已广泛应用于风能行业(主要是新型涡轮机)。这些系统利用基本的标准功能,在时域或频域工作,并没有针对非稳态信号进行优化。相比之下,这项工作的重点是同时在时域和频域工作的 MFCC,从而能够从非稳态信号中提取足够的信息。MFCC 从振动数据信号中提取,为更有效的分析提供了一种紧凑的表示方法。与已知的健康状况相比,这些系数可创建风力涡轮机运行状况的指纹,从而识别异常情况。为了强调这项研究的实用价值,有必要强调其对风能行业的重大意义。所开发的方法为齿轮箱故障的早期检测提供了先进的预测工具,而这正是优化风力涡轮机性能和使用寿命的关键所在。通过实现更早、更准确的故障检测,所提出的方法大大降低了发生灾难性故障和大面积停机的可能性。这不仅提高了风能系统的可靠性和成本效益,还通过优化资源利用和降低维护成本,促进了可持续能源实践。研究结果强烈表明,所提出的方法在检测风力涡轮机齿轮箱的初期故障方面非常有效。通过提供损坏预警,操作人员可以在发生重大停机或损坏之前解决问题。使用 MFCC 还能带来更多好处,因为数据可以远程采集,无需实际检查。分析可以更快地进行,甚至可以实时进行,从而可以更频繁地进行监测。这样就能更全面、更准确地了解系统的健康状况。该方法在 EISLAB 数据集中进行了测试,该数据集涉及瑞典北部六台风力涡轮机的振动信号,全部采用三级齿轮箱。所有测量数据都与每个风机输出轴轴承座中加速度计的轴向相对应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Wind Turbine Gearbox Early Fault Detection Using Mel-Frequency Cepstral Coefficients of Vibration Data

Wind Turbine Gearbox Early Fault Detection Using Mel-Frequency Cepstral Coefficients of Vibration Data

A methodology utilizing vibration data and Mel-frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost-effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time-consuming and labor-intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration-based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost-effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three-stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信