滚动轴承的性能退化特征提取及评估方法研究
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摘要
随着科学技术的进步和工业需求的发展,各类先进生产设备一方面不断向复杂、高速、高效、轻型、微型或大型的方向发展,另一方面却又面临更加苛刻的工作和运行环境。一旦设备的关键部件发生故障,就可能破坏整台设备甚至影响整个生产过程,造成巨大经济损失,还可能导致灾难性的人员伤亡并产生严重的社会影响。因此,如何有效评估设备的运行状态,从而能够及时采取措施以防止灾难性事故的发生是当前迫切需要解决的问题。
一般来说,机械设备在使用过程中总会经历由正常到退化直至失效的过程,而这期间通常都要经过一系列不同的性能退化状态。因此,如果能够在设备性能退化过程中监测到设备性能退化的程度,那么就可以有针对性地组织生产和制定合理的维修计划,做到既能防止设备异常失效的发生,又能实现生产效率的最大化。设备性能退化评估正是基于这一设想而提出的一种主动维护技术,它侧重于对设备全寿命周期中性能衰退程度的度量,而不过多的注重某一时间点的故障类别诊断,因此,与现有的故障诊断技术在理念上和方法上都有很大的不同。本文以滚动轴承为对象,深入开展了设备性能退化评估框架下的特征提取和评估方法的研究,包括以下几个方面的内容:
(1)从理论分析与工程应用的角度出发,阐述了论文的选题背景和研究意义。分析了信号分析与处理技术、模式识别技术以及设备性能退化评估与预测等方面的国内外发展现状,总结了目前研究中需要解决的问题,确立了本论文的研究内容。
(2)介绍了为本文研究提供数据支撑的滚动轴承加速疲劳寿命试验。一方面,从对初始性能退化的敏感性和对全退化过程的一致性角度,分析了常规指标对滚动轴承性能退化的反映能力,结果表明常规指标存在对初始退化不敏感的缺点。另一方面,也检验了试验数据的可信性。
(3)针对滚动轴承所具有的二阶循环平稳特性,提出了谱相关密度切片能量谱分析方法,它把滚动轴承五个特征频率处的谱相关密度切片在循环频率-谱相关能量的二维空间进行表达。对滚动轴承退化过程进行的循环平稳分析表明,该方法既能直观反映性能的退化,又能揭示引起不断退化的主要损伤部位。
(4)研究了针对设备性能退化评估的特征提取方法。滚动轴承正常状态下的振动信号近似为随机分布,而随着故障的不断加深,振动信号的随机成分的比例不断下降,而复杂度可以很好的反映出信号中随机成分比例的变化。因此,以复杂度度量方法为手段,研究了多种复杂度度量方法对滚动轴承性能退化程度的量化反映能力,针对近似熵和样本熵直接从原始时域空间进行度量而无法很好的反映各退化阶段的不足,提出了包络近似熵和包络样本熵,从而实现从包络域对信号进行复杂性的度量。通过理论模型仿真、不同损伤程度实验数据、全寿命周期的实验数据三个方面,对各个复杂度指标进行了对比、总结。结果表明,复杂度指标能够更敏感的捕捉到滚动轴承的初始退化,是对现有常规指标的有益补充。
(5)针对现有性能退化评估方法存在的问题,在提出并分析了基于模糊C均值和基于支持向量数据描述的性能退化评估方法的基础上,提出了二者相结合的评估方法。它利用支持向量数据描述获得正常状态的聚类中心,结合失效状态数据,通过模糊C均值获得退化过程中各时刻隶属于正常状态的隶属度,以此作为退化指标。这种混合评估方法有机融合了两种算法的优点,解决了单独依赖其中一种算法的主要问题,具有对数据完备性要求低、受人为设定参数影响小、评估结果可解释性强的特点。通过对滚动轴承全寿命周期的研究,验证了该方法的有效性。
With the growth of technology and the development of industry requirement, equipments are being developed to the direction of hugeness, distribution, high speed, automation and complexity, and at the same time, their running condition is more and more rigorous. Once the key parts occur fault, the whole equipment may be destroyed even whole production efficiency will be affected and catastrophic accidents will occur. So, the key issue to be resolved is how to assess efficiently equipment’s performance and make proper maintenance strategy. Generally speaking, equipment always experiences the process of from normal state
to failure, and this process is a continuous process. If the equipment performance degradation can be monitored, it would be possible to make proper maintenance strategy, so not only urgent broken can be prevented but also production efficiency can be maximized. Equipment performance degradation assessment is proposed based on the above idea. It emphasized particularly on the performance assessment through the whole lifetime, not on the fault classification at some time. So, it is different from the traditional fault diagnosis technology. Taking rolling element bearing as the research object, this paper has researched the feature extraction and assessment methods for the equipment performance degradation assessment. The main contents are as follows:
(1) From the viewpoint of theoretical analysis and engineering application, this paper’s background and significance of the present study are elucidated. A state of the art review is thoroughly completed, which consists of signal analysis and processing, pattern recognition and performance degradation assessment technologies. The issuses to be resoloved are summerized and the research contents of this paper are established.
(2) Rolling element bearing accelerated life test, which has provided data for this research, is presented. On the on hand, eight general indices’reflection capabilities of rolling element bearing performance degradation are analyzed from the sensitivity and consistency, the results show that these indices are not sensitive to initial degradation. On the other hand, the data’s credibility is proved.
(3) Spectral correlation density slice power spectrum is proposed, which is based on the rolling element bearing’s cyclostationary characteristic. Bearings’degradation is researched using this method, and the results show that it not only can intuitionistic reflect performance degradation, but also can reveal the dominant defect position.
(4) Feature extraction methods for equipment performance degradation assessment are researched. The vibration signal of normal bearing is close to random distribution, while with the defect’s deterioration, the proportion of random in vibration signal will decline. Complexity just right can reflect this change. In this paper, the complexity measurement is used as a means, multi-methods’capabilities of reflecting bearing’s degradation are researched, and envelope approximate entropy and envelope sample entropy are proposed to resolve the problem of approximate entropy and sample entropy, which measure signal’s complexity in the time-domain directly, can’t reflect bearings’degradation well. These indices are compared through theoretical model, different defect degree experiment and whole life time experiment. The results show that complexity can reflect initial degradation more sensitive than general indices.
(5) The performance degradation assessment methods respectively based on fuzzy c-means and support vector data description are proposed aiming for the problem of existing methods, and through analyzing these two methods, a new method combining them is proposed. It utilizes support vector data description to obtain the clustering center of normal state, combining failure data, the subjection to normal state is computed using fuzzy c-means, which is used as the degradation index. This hybrid method combines the merits of the two algorithms and resolves the main problems of relying on one algorithm solely. It holds some specialties such as low requirement for data’s maturity, unsusceptible to parameters and results with excellent interpretability. The validity is shown by applying it to bearings’whole lifetime.
一般来说,机械设备在使用过程中总会经历由正常到退化直至失效的过程,而这期间通常都要经过一系列不同的性能退化状态。因此,如果能够在设备性能退化过程中监测到设备性能退化的程度,那么就可以有针对性地组织生产和制定合理的维修计划,做到既能防止设备异常失效的发生,又能实现生产效率的最大化。设备性能退化评估正是基于这一设想而提出的一种主动维护技术,它侧重于对设备全寿命周期中性能衰退程度的度量,而不过多的注重某一时间点的故障类别诊断,因此,与现有的故障诊断技术在理念上和方法上都有很大的不同。本文以滚动轴承为对象,深入开展了设备性能退化评估框架下的特征提取和评估方法的研究,包括以下几个方面的内容:
(1)从理论分析与工程应用的角度出发,阐述了论文的选题背景和研究意义。分析了信号分析与处理技术、模式识别技术以及设备性能退化评估与预测等方面的国内外发展现状,总结了目前研究中需要解决的问题,确立了本论文的研究内容。
(2)介绍了为本文研究提供数据支撑的滚动轴承加速疲劳寿命试验。一方面,从对初始性能退化的敏感性和对全退化过程的一致性角度,分析了常规指标对滚动轴承性能退化的反映能力,结果表明常规指标存在对初始退化不敏感的缺点。另一方面,也检验了试验数据的可信性。
(3)针对滚动轴承所具有的二阶循环平稳特性,提出了谱相关密度切片能量谱分析方法,它把滚动轴承五个特征频率处的谱相关密度切片在循环频率-谱相关能量的二维空间进行表达。对滚动轴承退化过程进行的循环平稳分析表明,该方法既能直观反映性能的退化,又能揭示引起不断退化的主要损伤部位。
(4)研究了针对设备性能退化评估的特征提取方法。滚动轴承正常状态下的振动信号近似为随机分布,而随着故障的不断加深,振动信号的随机成分的比例不断下降,而复杂度可以很好的反映出信号中随机成分比例的变化。因此,以复杂度度量方法为手段,研究了多种复杂度度量方法对滚动轴承性能退化程度的量化反映能力,针对近似熵和样本熵直接从原始时域空间进行度量而无法很好的反映各退化阶段的不足,提出了包络近似熵和包络样本熵,从而实现从包络域对信号进行复杂性的度量。通过理论模型仿真、不同损伤程度实验数据、全寿命周期的实验数据三个方面,对各个复杂度指标进行了对比、总结。结果表明,复杂度指标能够更敏感的捕捉到滚动轴承的初始退化,是对现有常规指标的有益补充。
(5)针对现有性能退化评估方法存在的问题,在提出并分析了基于模糊C均值和基于支持向量数据描述的性能退化评估方法的基础上,提出了二者相结合的评估方法。它利用支持向量数据描述获得正常状态的聚类中心,结合失效状态数据,通过模糊C均值获得退化过程中各时刻隶属于正常状态的隶属度,以此作为退化指标。这种混合评估方法有机融合了两种算法的优点,解决了单独依赖其中一种算法的主要问题,具有对数据完备性要求低、受人为设定参数影响小、评估结果可解释性强的特点。通过对滚动轴承全寿命周期的研究,验证了该方法的有效性。
With the growth of technology and the development of industry requirement, equipments are being developed to the direction of hugeness, distribution, high speed, automation and complexity, and at the same time, their running condition is more and more rigorous. Once the key parts occur fault, the whole equipment may be destroyed even whole production efficiency will be affected and catastrophic accidents will occur. So, the key issue to be resolved is how to assess efficiently equipment’s performance and make proper maintenance strategy. Generally speaking, equipment always experiences the process of from normal state
to failure, and this process is a continuous process. If the equipment performance degradation can be monitored, it would be possible to make proper maintenance strategy, so not only urgent broken can be prevented but also production efficiency can be maximized. Equipment performance degradation assessment is proposed based on the above idea. It emphasized particularly on the performance assessment through the whole lifetime, not on the fault classification at some time. So, it is different from the traditional fault diagnosis technology. Taking rolling element bearing as the research object, this paper has researched the feature extraction and assessment methods for the equipment performance degradation assessment. The main contents are as follows:
(1) From the viewpoint of theoretical analysis and engineering application, this paper’s background and significance of the present study are elucidated. A state of the art review is thoroughly completed, which consists of signal analysis and processing, pattern recognition and performance degradation assessment technologies. The issuses to be resoloved are summerized and the research contents of this paper are established.
(2) Rolling element bearing accelerated life test, which has provided data for this research, is presented. On the on hand, eight general indices’reflection capabilities of rolling element bearing performance degradation are analyzed from the sensitivity and consistency, the results show that these indices are not sensitive to initial degradation. On the other hand, the data’s credibility is proved.
(3) Spectral correlation density slice power spectrum is proposed, which is based on the rolling element bearing’s cyclostationary characteristic. Bearings’degradation is researched using this method, and the results show that it not only can intuitionistic reflect performance degradation, but also can reveal the dominant defect position.
(4) Feature extraction methods for equipment performance degradation assessment are researched. The vibration signal of normal bearing is close to random distribution, while with the defect’s deterioration, the proportion of random in vibration signal will decline. Complexity just right can reflect this change. In this paper, the complexity measurement is used as a means, multi-methods’capabilities of reflecting bearing’s degradation are researched, and envelope approximate entropy and envelope sample entropy are proposed to resolve the problem of approximate entropy and sample entropy, which measure signal’s complexity in the time-domain directly, can’t reflect bearings’degradation well. These indices are compared through theoretical model, different defect degree experiment and whole life time experiment. The results show that complexity can reflect initial degradation more sensitive than general indices.
(5) The performance degradation assessment methods respectively based on fuzzy c-means and support vector data description are proposed aiming for the problem of existing methods, and through analyzing these two methods, a new method combining them is proposed. It utilizes support vector data description to obtain the clustering center of normal state, combining failure data, the subjection to normal state is computed using fuzzy c-means, which is used as the degradation index. This hybrid method combines the merits of the two algorithms and resolves the main problems of relying on one algorithm solely. It holds some specialties such as low requirement for data’s maturity, unsusceptible to parameters and results with excellent interpretability. The validity is shown by applying it to bearings’whole lifetime.
引文
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