水压试验机缓变故障检测、预测及维护方法研究
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摘要
作为一种流体输送管道,钢管是国民经济各个领域不可缺少的重要钢材之一,其质量特别是检验钢管质量的相关技术、设备的革新受到了普遍的关注,并取得了长足的进步。压力试验是钢管生产过程中必需的质量检测环节,对钢管的质量检验具有重要作用。水压试验机正是为进行钢管压力试验而设计制造的检验设备。一方面,由于水压试验是钢管生产过程中的一个非常关键的质量检验工序,水压试验机的运行状态直接影响到钢管的生产效率,另一方面,该设备是一个复杂的高压系统,一旦发生故障,严重影响到生产的安全可靠运行,甚至可能会造成人员和财产的重大损失。因此,如果能够利用适当的过程监测、故障检测及预测方法,实时监测、预测设备运行状态,为企业管理人员制定生产决策提供可靠依据,对提高设备的检验能力、保障生产可靠运行具有重要的实际意义。
本文以宝钢钢管厂1995年从德国DEMAG公司引进的3号线水压试验机作为研究对象,在深入分析其主体液压系统工作机理的基础上,针对该类水压试验机液压系统缓变故障的特点,提出了具有一定实际应用价值的缓变故障检测、预测及维护方法：
(1).分析并研究了水压试验机主体液压系统工作机理,通过简化其增压系统及平衡系统结构,分别给出了相应的数学模型。在此基础上,以AMESim作为软件环境,针对主要子系统(油源系统、增压系统、平衡系统等),着重考虑了各子系统中可能存在的故障,分别搭建相应可进行故障模拟的子模型,最终建立水压试验机主液压系统整体仿真模型,实现了该设备液压系统在正常工况以及常见故障工况下的动态性能仿真。根据该系统的故障仿真结果,可以将液压系统缓变故障划分为两大类,即具有周期性故障征兆的缓变故障和不具有周期性故障征兆的缓变故障。
(2).针对第一类具有周期性故障征兆的缓变故障,如水压试验机液压泵单柱塞松靴故障,选择反映该类故障的特征过程变量,提出了基于混沌振子与滑动窗口符号序列统计相结合的早期故障检测方法。该方法首先根据混沌振子对外界微弱周期信号敏感和对噪声免疫的特点,利用Duffing振子检测液压泵早期松靴故障。由于混沌振子在进行弱信号检测时,是通过视觉观察振子相变来实现的,不便于实现检测算法的自动化。因此,提出一种滑动窗口符号序列统计方法来实现振子状态的实时自动判别,从而实现液压泵早期松靴故障的在线自动检测。
(3).针对第二类不具备周期性故障征兆的缓变故障,仅仅由一个过程变量信息往往无法有效提取出故障信号,本文提出一种基于多变量统计分析的概率故障预测方法。该方法对过程三维数据展开,利用主成分分析方法对展开后的数据矩阵进行分析,对应于批次,提取反映系统运行状态的统计量指标——综合统计量。其次,为了利用该统计量进行系统运行状态的预测,对指标进行滑动窗口累积和运算,降低该指标的随机波动性,增强其规律性。最后利用贝叶斯AR模型对累积后的指标进行预测,获得其概率分布,并计算相应的故障概率以及系统失效时间。
(4).在分析系统或设备退化过程的基础上,针对第一类缓变故障,提出利用Duffing振子在一个周期中处于混沌以及大周期状态的时段比值识别设备退化模式切换点,制定维护决策；针对第二类缓变故障,按照可靠度将系统运行状态划分为正常状态、临界状态和故障状态。由故障概率计算系统的可靠度,通过判断系统所处的退化模式以及可靠度制定维护策略,实现预测维护。
最后,在总结全文的基础上,本文还对缓变故障预测领域未来的研究重点和热点问题进行了展望。
As one type of fluid transporting pipeline, steel tube, which is an essential and vital steel product in various fields of the national economy, has attracted spread interests in its quality, relevant techniques for its quality detection and innovation of the relevant equipments in particular. Great progress has been achieved in these fields. Being a necessary part of quality inspection during the production process of steel tube, pressure test plays an important role in the assessment of steel tube. Hydraulic tube tester, which is especially designed and produced in order to test the pressure resistance ability of steel tube, is a complex system composed of mechanics, hydraulics and electronic control, et al. On one hand, as the hydrostatic testing process is a critical quality inspection procedure in the production of steel tube, the running status of hydraulic tube tester has a direct effect on production efficiency of steel tube. On the other hand, as a complex high-press system, the failure of hydraulic tube tester will not only seriously affect the safety and reliability of process operation, but even result in huge loss of personnel and property. Therefore, it is significant to study feasible real-time monitoring, fault detection and prediction methods to support reliable basis for production management, which will improve the performance of machine detection and guarantee the safety and reliability of production.
Based on the aforementioned consideration, by setting the3#hydraulic tube tester in the Baosteel Tube Branch, imported from the DEMAG Compony, Germany in1995, as the research object, this dissertation further studies its work mechanism and thus developes a series of incipient fault detection, prediction and maintenance methods focusing on the characteristics of incipient fault occurred in the hydraulic system of this type of hydraulic tube tester, which shows important practical values.
(1). The boost system and balance system are simplified to construct their corresponding mathematical models after the operation mechanism of the main hydraulic system in the hydraulic tube tester has been summarized and analyzed. Based on this, some submodels concerning fault simulation are developed for the main subsystems, such as oil supply system, boost system, balance system and so on. Finally, the whole simulation model is developed for the main hydraulic system of hydraulic tube tester based on the AMEsim software, which realizes the dynamlic simulation of this hydraulic system under normal and commonly faulty conditions, respectively. According to the simulation results of this hydraulic system, the incipient faults can be divided into two categories, i.e. incipient fault with and without periodic fault symptom.
(2). For the first type of incipient fault with periodic fault symptom, taking single piston loose shoes fault of hydraulic pump in the hydraulic tube tester for example, by selecting a characteristic variable, an early fault detection algorithm is proposed by a combination of Chaos oscillator and sliding window symbol sequence statistic methods. The proposed method firstly chooses Chaos oscillator to diagnose this type of incipient fault based on the insight that the phase transition of Duffing oscillator is very sensitive to a periodic weak signal and immune against to other noises. Then, considering that it is not easy for computer to discriminate the intermittent chaos phenomenon, sliding window symbol sequence statistics method is developed to realize online real-time diagnosis, and critical parameter configuration are analyzed for window size and depth of symbol tree.(3). For the second type of incipient fault without periodic fault symptom, which is not
realistic to effectively detect the connotative fault symptom through a single process variable, a probabilistic fault prediction method is developed based on multivariate statistical analysis technique. The proposed method firstly unfolds three-way batch process data into a two dimensional matrix, applies principle component analysis (PCA) to this two dimensional matrix to extract corresponding statitistical index reflecting the status of the running system, i.e. the combined index. Then, sliding window cumulative sum mentod is proposed to decrease the stochastic volatility, and increase the regularity buried in each statistical time-series. After that, Bayesian AR model is used to predict the transformed index to get its probability distribution at future time, and calculates its corresponding fault probability and time to failure.(4). Based on the analysis of system's or equipment's deterioration process, for the first type
of incipient fault, a predictive maintenance method is developed by identifying the mode change point according to the ratio of two time spans, which are the duration of chaos motion and large periodic motion of the Duffing oscillator in a cycle. For the second type of incipient fault, the proposed method partitions the system statuses into3statuses, i.e. normal status, critical statuse and fault status. Then, it calculates the reliability according to the fault probability, and realizes predictive maintenance by determinining the deterioration mode and reliability of current system.
Finally, the potential further research direction in the area of incipient fault detection and prediction is discussed after summarizing the whole work in this thesis.
本文以宝钢钢管厂1995年从德国DEMAG公司引进的3号线水压试验机作为研究对象,在深入分析其主体液压系统工作机理的基础上,针对该类水压试验机液压系统缓变故障的特点,提出了具有一定实际应用价值的缓变故障检测、预测及维护方法：
(1).分析并研究了水压试验机主体液压系统工作机理,通过简化其增压系统及平衡系统结构,分别给出了相应的数学模型。在此基础上,以AMESim作为软件环境,针对主要子系统(油源系统、增压系统、平衡系统等),着重考虑了各子系统中可能存在的故障,分别搭建相应可进行故障模拟的子模型,最终建立水压试验机主液压系统整体仿真模型,实现了该设备液压系统在正常工况以及常见故障工况下的动态性能仿真。根据该系统的故障仿真结果,可以将液压系统缓变故障划分为两大类,即具有周期性故障征兆的缓变故障和不具有周期性故障征兆的缓变故障。
(2).针对第一类具有周期性故障征兆的缓变故障,如水压试验机液压泵单柱塞松靴故障,选择反映该类故障的特征过程变量,提出了基于混沌振子与滑动窗口符号序列统计相结合的早期故障检测方法。该方法首先根据混沌振子对外界微弱周期信号敏感和对噪声免疫的特点,利用Duffing振子检测液压泵早期松靴故障。由于混沌振子在进行弱信号检测时,是通过视觉观察振子相变来实现的,不便于实现检测算法的自动化。因此,提出一种滑动窗口符号序列统计方法来实现振子状态的实时自动判别,从而实现液压泵早期松靴故障的在线自动检测。
(3).针对第二类不具备周期性故障征兆的缓变故障,仅仅由一个过程变量信息往往无法有效提取出故障信号,本文提出一种基于多变量统计分析的概率故障预测方法。该方法对过程三维数据展开,利用主成分分析方法对展开后的数据矩阵进行分析,对应于批次,提取反映系统运行状态的统计量指标——综合统计量。其次,为了利用该统计量进行系统运行状态的预测,对指标进行滑动窗口累积和运算,降低该指标的随机波动性,增强其规律性。最后利用贝叶斯AR模型对累积后的指标进行预测,获得其概率分布,并计算相应的故障概率以及系统失效时间。
(4).在分析系统或设备退化过程的基础上,针对第一类缓变故障,提出利用Duffing振子在一个周期中处于混沌以及大周期状态的时段比值识别设备退化模式切换点,制定维护决策；针对第二类缓变故障,按照可靠度将系统运行状态划分为正常状态、临界状态和故障状态。由故障概率计算系统的可靠度,通过判断系统所处的退化模式以及可靠度制定维护策略,实现预测维护。
最后,在总结全文的基础上,本文还对缓变故障预测领域未来的研究重点和热点问题进行了展望。
As one type of fluid transporting pipeline, steel tube, which is an essential and vital steel product in various fields of the national economy, has attracted spread interests in its quality, relevant techniques for its quality detection and innovation of the relevant equipments in particular. Great progress has been achieved in these fields. Being a necessary part of quality inspection during the production process of steel tube, pressure test plays an important role in the assessment of steel tube. Hydraulic tube tester, which is especially designed and produced in order to test the pressure resistance ability of steel tube, is a complex system composed of mechanics, hydraulics and electronic control, et al. On one hand, as the hydrostatic testing process is a critical quality inspection procedure in the production of steel tube, the running status of hydraulic tube tester has a direct effect on production efficiency of steel tube. On the other hand, as a complex high-press system, the failure of hydraulic tube tester will not only seriously affect the safety and reliability of process operation, but even result in huge loss of personnel and property. Therefore, it is significant to study feasible real-time monitoring, fault detection and prediction methods to support reliable basis for production management, which will improve the performance of machine detection and guarantee the safety and reliability of production.
Based on the aforementioned consideration, by setting the3#hydraulic tube tester in the Baosteel Tube Branch, imported from the DEMAG Compony, Germany in1995, as the research object, this dissertation further studies its work mechanism and thus developes a series of incipient fault detection, prediction and maintenance methods focusing on the characteristics of incipient fault occurred in the hydraulic system of this type of hydraulic tube tester, which shows important practical values.
(1). The boost system and balance system are simplified to construct their corresponding mathematical models after the operation mechanism of the main hydraulic system in the hydraulic tube tester has been summarized and analyzed. Based on this, some submodels concerning fault simulation are developed for the main subsystems, such as oil supply system, boost system, balance system and so on. Finally, the whole simulation model is developed for the main hydraulic system of hydraulic tube tester based on the AMEsim software, which realizes the dynamlic simulation of this hydraulic system under normal and commonly faulty conditions, respectively. According to the simulation results of this hydraulic system, the incipient faults can be divided into two categories, i.e. incipient fault with and without periodic fault symptom.
(2). For the first type of incipient fault with periodic fault symptom, taking single piston loose shoes fault of hydraulic pump in the hydraulic tube tester for example, by selecting a characteristic variable, an early fault detection algorithm is proposed by a combination of Chaos oscillator and sliding window symbol sequence statistic methods. The proposed method firstly chooses Chaos oscillator to diagnose this type of incipient fault based on the insight that the phase transition of Duffing oscillator is very sensitive to a periodic weak signal and immune against to other noises. Then, considering that it is not easy for computer to discriminate the intermittent chaos phenomenon, sliding window symbol sequence statistics method is developed to realize online real-time diagnosis, and critical parameter configuration are analyzed for window size and depth of symbol tree.(3). For the second type of incipient fault without periodic fault symptom, which is not
realistic to effectively detect the connotative fault symptom through a single process variable, a probabilistic fault prediction method is developed based on multivariate statistical analysis technique. The proposed method firstly unfolds three-way batch process data into a two dimensional matrix, applies principle component analysis (PCA) to this two dimensional matrix to extract corresponding statitistical index reflecting the status of the running system, i.e. the combined index. Then, sliding window cumulative sum mentod is proposed to decrease the stochastic volatility, and increase the regularity buried in each statistical time-series. After that, Bayesian AR model is used to predict the transformed index to get its probability distribution at future time, and calculates its corresponding fault probability and time to failure.(4). Based on the analysis of system's or equipment's deterioration process, for the first type
of incipient fault, a predictive maintenance method is developed by identifying the mode change point according to the ratio of two time spans, which are the duration of chaos motion and large periodic motion of the Duffing oscillator in a cycle. For the second type of incipient fault, the proposed method partitions the system statuses into3statuses, i.e. normal status, critical statuse and fault status. Then, it calculates the reliability according to the fault probability, and realizes predictive maintenance by determinining the deterioration mode and reliability of current system.
Finally, the potential further research direction in the area of incipient fault detection and prediction is discussed after summarizing the whole work in this thesis.
引文
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