基于KPCA算法的环网柜故障检测方法

为保证供电系统的安全运行,针对智能电网中环网柜故障检测模型精度低,非线性变量泛化能力差等问题,提出了一种基于KPCA算法的环网柜故障检测建模方法。PCA算法是故障检测的常用方法,为了解决非线性问题,使用核函数建立核主元模型提取环网柜系统的非线性冗余信息,通过非线性映射将输入空间映射到特征空间,再计算特征值问题。将构建的KPCA模型应用于环网柜系统故障检测,采集环网柜内的多变量信息和环境变量信息,将变量数据空间分解为2个正交互补子空间,分别在特征空间构造T~2统计量和残差空间构造Q统计量进行监控,实现环网柜系统的故障报警。经过对正常数据和故障数据的仿真实验结果表明,该KPCA算法在准确检测故障的前提下,能够有效降低模型的故障误报率,改善了环网柜故障检测效果。     

基于传递熵与KELM的炼油化工过程风险传播路径分析方法

基于炼油化工过程复杂，设备众多，某一设备的监测变量发生扰动可能会传播至其相邻设备引发出一系列故障链。现有方法多是针对某一设备进行监测与诊断，以期降低事故后果，而忽视了对过程风险传播路径的预测以防止事故的发生。因此，提出一种基于传递熵与核极限学习机的炼油化工过程风险传播路径分析方法，该方法针对某一工艺扰动，分析其在风险发展过程中的扰动传播过程，基于传递熵分析法建立炼油化工过程风险传播推绎模型；并提出一种基于KELM的风险传播搜索方法，预测风险传播路径；将该方法应用于分馏塔冲塔过程。研究结果表明:该方法可辨识出未来一段时间内风险的可能传播路径，以便操作人员及时采取预防措施，保证过程安全及产品质量。     

化工过程自适应多模型故障监测方法研究

为提高化工过程故障监测的准确性,针对先验知识不足情况下的多模态化工过程模态数未知问题,开发自适应多模型的故障监测方法。基于奇异值分解(SVD)求解模态数,利用模糊C均值(FCM)算法对训练数据进行模态划分,并利用主成分分析(PCA)方法,针对不同的过程模态建立相应的监测模型,实现故障监测。将该方法应用于丙烯计量罐装置,进行实例分析。结果表明,与基于聚类有效性指标的方法相比,该方法在正常和故障2种情况下,故障监测的误报率分别降低了3.54%和5.59%。     

化工生产事故预防信息系统研究

为指导操作人员正确地处理化工生产过程中的问题,防范人为误操作导致的事故发生,开发了以化工生产的危险与可操作性分析结果和典型事故原因分析结果为知识库的事故预防信息系统.系统实时在线监测化工生产过程中的关键变量,通过判定变量间的影响关系,实现对化工生产过程潜在危险的辨识、预警并给予实时操作指导,以确保生产安全,提高装置的生产效率.最后,以丙烯聚合工艺为例,在多功能过程试验控制平台上进行了验证,探讨了事故预防信息系统的应用方法.     

LS-SVM在地下空间可燃气体分类中的应用研究

在城市燃气管线相邻地下空间可燃气体监测过程中,由微生物产生的沼气会对可燃气体监测预警的准确性造成影响。为了能够准确地对地下空间中可燃气体的来源进行判别,提出了一种基于最小二乘法支持向量机(LS-SVM)的可燃气体分类预测模型。选取了典型的燃气及沼气体积分数时空演化数据,分析得到了两类可燃气体数据的基本特征。基于最小二乘法支持向量机分类算法,选取多项式核函数和径向基核函数分别构建可燃气体分类预测模型。通过样本数据对两种分类器分别进行测试,结果显示两种分类器的准确度相差不大,其中采用径向积核函数的分类模型测试准确度达到81%。最小二乘法支持向量机对于可燃气体监测分类具有一定的应用价值,有助于提高燃气监测运维过程的准确性和高效性。     

基于mini-1D-CNN模型的TE过程故障诊断

为提升石化企业过程监测与故障诊断系统性能,满足化工过程故障诊断实时性、时效性的要求,提出一种基于过程历史数据驱动的最小一维卷积神经网络(mini-1D-CNN)的故障诊断模型。首先,通过一维卷积核学习和识别不同故障类型的数据特征,自动提取优势特征并进行故障分类;其次,通过逐步向后回归选择重要特征参数,优化模型结构。利用可实时获取的31个过程变量与操作参数,输入一维卷积神经网络(1D-CNN),监测与诊断田纳西-伊斯曼(TE)过程的主要故障。结果表明：相对于其他故障诊断模型,mini-1D-CNN模型在测试集上故障诊断率(FDR)较高,可达到96.50%;同时,mini-1D-CNN模型关注于TE过程故障诊断的重要特征参数,在降低参数量及降低训练和测试时间上具有显著优势。     

一种新的基于图模型的间歇过程故障监测方法

目前多元统计方法被广泛用于间歇过程故障监测并已经取得了比较好的效果。但是,统计模型的可解释性能比较差,很难直接利用操作人员积累的安全经验。为应对这些不足,提出了一种基于图模型的间歇过程故障监测方法。利用提出的定性建模方法,过程机理及操作人员的安全经验能够方便地表达。利用在线的过程变量数据和正向推理算法推断生产应处于的状态,利用安全知识及时地发现生产异常。当推断的结论与在线测得的结果矛盾或过程变量超过设定的安全限时,给出解释性输出。通过一个间歇反应案例,验证了提出的方法在模型的可解释性和利用安全经验方面的优势。     

大型火电机组故障处理策略与方法

为提高大型火力发电机组生产过程中故障处理的正确性和准确性,揭示火力发电生产过程故障的趋势、规律和特征,量化分析故障趋势的关键点、诱发因子和临界点,提出树形结构的故障应对策略,针对不同故障源和故障分类,制定相应的处理方法,提炼火力发电生产故障处理的注意事项。研究表明:针对大型火电机组构建树形结构故障处理策略和方法,有助于火电机组操作人员提升处理能力。从故障源类型、设备类型、机组状态等方面采取针对性的故障处理方法,有助于处理大型火力发电机组的生产故障,减少人因失误。     

基于运行场次用时误差的载人设备故障预警可视化研究

针对国内游乐设施行业载人设备运行状态监测不够完善、故障诊断多数情况下需要依赖技术人员经验的现状,分析了基于机理模型、基于知识和基于数据驱动的载人设备故障预警方法,研究了载人设备故障预测的多元线性回归算法,并利用矿山车的时间系数、人数系数、温度系数,建立了矿山车载人设备的多元线性回归模型。通过矿山车15个同步点区间用时预测案例,在可视化平台上显示预测和实际运行曲线,实现了对矿山车或过山车类载人设备运行故障的有效预警。     

时序数据驱动的化工过程风险动态预警研究

对化工过程进行在线监测与动态风险预警是降低事故发生的有效途径。提出了一种基于深度学习时序预测与模糊数学定量风险评估相结合的预警方法。针对化工过程数据的动态性、时序性、非线性强，且预测周期短等问题，将卷积神经网络(Convolutional Neural Networks, CNN)与长短期记忆网络(Long Short-Term Memory, LSTM)模型结合形成深度学习时序预测模型，实现过程参数108 min的超前预测。将该方法应用于合成氨过程，对温度、压力、流量、氢氮比等6个风险参数进行预测。结果表明，该预测方法具有较高的预测精度，其线性回归相关系数及均方根误差表明所提出的方法具有非常高的精度。同时利用三角模糊数对时序预测结果进行风险评估，得到时序风险变化曲线，实现了化工过程风险预警。研究对使用人工智能和大数据实现过程控制和风险预警进行了有益探索，为实现化工过程的超前预警提供参考。     

基于重构相空间充填体变形规律的灰色预测研究

尾砂胶结充填体是非线性力学介质,其变形是能量耗散的复杂过程,必须研究其内在变形规律,才能正确预测采矿过程中充填体的稳定性.对不同配比的尾砂胶结充填体进行力学试验,得出了其应力-应变规律,对安庆铜矿高阶段充填体变形进行了监测.采用自适应滤波原理,研究基于重构相空间的测量数据去噪处理方法.用灰色理论研究充填体变形在相空间中相点距离的演变规律,建立了重构相空间的灰色预测模型.为减小预测误差,对预测结果采用残差模型修正.应用建立的模型,对安庆铜矿高阶段充填体变形进行分析,确定了采场合理回采周期.结果表明,充填体变形具有非线性混沌特性,不同配比的充填体表现出不同的非线性动力学行为,重构相空间能充分展示充填体变形的内在规律.     

Fault detection approach for systems involving soft sensors

This paper develops a new approach for fault detection which involves soft sensors for process monitoring. Unlike existing approaches, which compare current measurements, or linear combinations thereof, to values of these measurements representing normal operations, the methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the effect of abnormal process conditions on the state variables can be directly observed and that it is possible to include nonlinear relationships between measurements and states. At the same time, this type of approach has the drawback that the variances of the unmeasured states are not equal to the variances of the actual process variables due to the use of a soft sensor. However, for many popular soft sensor techniques, such as Kalman filters and related approaches, it is possible to compute variances of the predicted states that correspond to normal operating conditions. This paper presents a general framework for using soft sensors for process monitoring, i.e., soft sensor design and computation of the statistics that represent normal operating conditions, and illustrates this framework in three specific applications. It should be pointed out that the contribution of this work does not lie with the soft sensor design or the computation of the statistics itself as either part has individually already been addressed in the existing literature. However, the authors are not aware of any studies where both tasks are combined for process monitoring, which forms the contribution of this work.     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

基于风量-风压复合特征的通风系统阻变型故障诊断

为了避免风量单一特征进行故障位置诊断的不适定性，提出基于风量-风压复合特征的故障位置诊断方法，实现特征信息的多维互补，提高故障位置诊断的准确度。利用蒙特卡洛方法生成大致满足实际故障风阻值分布的故障仿真样本，为了避免不同变量之间不同量纲、不同数量级造成的数据损失，对原始风量、风压数据进行标准化处理，并分别以风量单一特征、风压单一特征、风量-风压复合特征作为支持向量机（SVM）的输入，构建通风系统阻变型故障位置诊断模型。通过故障模拟实验研究表明:风量、风压单一特征进行故障位置诊断的准确度分别为89.80%，90.34%，风量-风压复合特征进行故障位置诊断的准确度为98.23%，说明风量-风压复合特征进行故障诊断可以消除风量、风压单一特征进行故障诊断的不适定性，提高故障诊断的准确度。     

Kullback-Leibler distance-based enhanced detection of incipient anomalies

Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters.     

基于覆岩空间结构理论的矿压特征分析及应用

基于覆岩空间结构理论,一面采空、两面采空边界条件下覆岩结构称为“O”型和“S”型,结合矿压知识及工程背景的特殊条件,将二者转换过程中覆岩结构定义为“O-S”型。在系统分析三种结构构成、运移规律和应力分布特征的基础上,对矿山动力事件进行分析与防治;现场侧向支承压力监测结果显示基于覆岩空间结构理论的结构构成及相关计算相似,现场顶板岩层离层监测结果显示基于覆岩空间结构理论的覆岩运移规律阐述可靠,工作面矿压特征监测结果显示基于覆岩空间结构理论的特厚煤层矿压特征分析与实际发生情况基本一致,研究可为类似条件下矿压特征分析与岩层控制提供一定借鉴。     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

基于自记忆模型的煤与瓦斯突出电磁辐射预测研究

利用实验测定的电磁辐射信号时间序列,用双向差分原理反导出一个非线性常微分方程;以其为微分动力核,运用动力系统数据机理自记忆模式构造自记忆方程并求出自记忆系数;利用该方程预测未来电磁辐射信号的变化,并与现场测定对比分析,用误差分析和距平分析法验证该模型正确性和预测准确率。实例表明:该自记忆模型预测与实测结果是一致的,相对误差均在6.7852%左右,距平符合率为90%;自记忆方法能有效应用于煤与瓦斯突出电磁辐射动态预测中;该模型与电磁辐射预测方法的有机结合能有效地提高预测准确性,从而为煤与瓦斯突出电磁辐射预测技术提供了一种新的研究途径。     

基于支持向量机(SVM)的回采工作面瓦斯涌出混沌预测方法研究

针对瓦斯涌出传统的线性预测方法存在的问题,根据瓦斯涌出时间序列固有的确定性和非线性,利用混沌动力系统的相空间延迟坐标重构理论,结合基于机器学习理论的支持向量机(SVM),建立基于SVM理论的瓦斯涌出混沌时间序列预测模型。经Ⅱ1024回采工作面瓦斯涌出时间序列仿真计算,仿真结果显示该预测模型具有比传统的回归方法更好的泛化能力,预测方法具有很高的预测精度。同时,该模型具有以往传统机器学习的瓦斯涌出预测模型建立简便、训练速度快等优点。由于充分考虑瓦斯涌出时间序列的混沌性,并利用SVM预测的优良特性,使得预测更科学。