Detection of gross errors using mixed integer optimization approach in process industry

A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

An NT-MT Combined Method for Gross Error Detection and Data Reconciliation

An NT-MT combined method based on nodal test (NT) and measurement test (MT) is developed for gross error detection and data reconciliation for industrial application. The NT-MT combined method makes use of both NT and MT tests and this combination helps to overcome the defects in the respective methods. It also avoids any artificial manipulation and eliminates the huge combinatorial problem that is created in the combined method based on the nodal test in the case of more than one gross error for a large process system. Serial compensation strategy is also used to avoid the decrease of the coefficient matrix rank during the computation of the proposed method. Simulation results show that the proposed method is very effective and possesses good performance.

Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates

Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.

MHSS ARAIM Algorithm Combined with Gross Error Detection

Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets with the larger computational load,a method combining MHSS ARAIM with gross error detection is proposed in this paper.The gross error detection method is used to identify and eliminate the gross data in the original data first,then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection.Therefore,this makes up for the weakness of the MHSS ARAIM algorithm.With the data processing and analysis from several international GNSS service(IGS)and international GNSS monitoring and assessment system(iGMAS)stations,the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service(LPV-200)when using GPS and BDS measure data.Under the assumption of a single-faulty satellite,the effective monitoring threshold(EMT)is improved about 22.47%and 9.63%,and the vertical protection level(VPL)is improved about 32.28%and 12.98%for GPS and BDS observations,respectively.Moreover,under the assumption of double-faulty satellites,the EMT is improved about 80.85%and 29.88%,and the VPL is improved about 49.66%and 18.24%for GPS and BDS observations,respectively.

自动确定边角混合网独立闭合差的增量算法

为评定外业观测数据质量和发现粗差,提出了一种闭合差搜索的通用算法。首先,针对高速铁路隧道中的各类边角混合网,基于平差理论,计算由边角混合网中测站数、非测站数和观测数确定其独立闭合差的数目,给出最简闭合差构成原则,求出余弦和正弦闭合差,推导闭合差限差计算模型,建立由闭合差探测观测值粗差的方法;进而导出闭合差增量与测站递增产生的重叠观测点数之间的数学关系式,设计数据结构实现测站与结点间双向多对多的拓扑映射关系,并基于分治思想,提出队列-测站双循环的增量算法来自动搜索最简且独立的闭合差;最后,以西南某高速铁路隧道构建的边角混合网为例进行算法验证。结果表明:增量算法能够在混合网中自动确定100%数量的最简独立闭合差,对观测值中存在的粗差100%予以探测,验证了采用该算法搜索闭合差及检核粗差的有效性。

高速铁路三维控制网粗差实时判别方法研究

高速铁路三维控制网外业观测易受不利环境因素影响,难免存在测量粗差,为了尽早识别测量粗差,提高外业数据质量及后续平差结果的精度,需进行三维控制网粗差实时判别方法的研究。根据三维控制网网形及其特点,详细分析三维控制网闭合环的类型,采用三维控制网(Ⅰ类、Ⅱ类、Ⅲ类)22种闭合环进行闭合差计算;同时,基于协方差传播定律构建闭合环校核边中误差的数学模型,以2倍闭合环相对中误差为限差,判别三维控制网外业数据质量。研究表明,三维控制网网形具有较强的规律性和对称性,在完成归整后,可使用1/7000,1/13000和1/23000为对应闭合环的极限误差进行粗差判别。研究结果表明,在三维控制网外业数据观测过程中,采用该方法能够实时判别并发现测量粗差,有利于以最小代价进行现场补测,从而提高三维控制网数据可靠性和数据质量。

A new method of identifying and locating gross errors——Quasi-accurate detection

A new idea and a distinctive method have been proposed, which concern real errors and their estimators. By using the idea of 'Quasi-Stable Adjustment' created by Prof. Zhou Jiangwen for reference, the rank-deficient equations on real errors are resolved by adding the conditions under which the minimum of the norm of the real errors of the quasi-accurate observations is restrained.

基于复杂阴影测量方法的建筑物高度反演

阴影法能以较低的成本测量大范围的城市建筑物高度,但其中的阴影长度测量方法在复杂阴影场景中存在测量效率低、精度差、鲁棒性低等问题。对此,该文提出了一种复杂建筑阴影测量方法。该方法首先通过渔网法和多种约束条件相结合实现对阴影的测量与划分;其次,统计所有划分区域的阴影长度值,利用四分位法和双向逼近策略确定阴影最优值;然后,综合评估所有区域最优值确定阴影长度;最后,利用立体交会的建筑高度对应用该阴影测量方法计算的建筑物高度进行验证。结果表明:该方法计算建筑物高度有90.6%以上绝对误差在0~5 m之间。因此可以得出该文方法计算的阴影长度有着较高的精度,能够满足不同种类的复杂阴影测量,提高了阴影法反演建筑物高度的精度,为城市建筑物高度的反演提供新的技术支撑。

跨海桥梁首级GPS控制网数据处理方法及应用

跨海桥梁具有跨海距离长、工程环境恶劣、施工周期长、精度要求高等显著特点,对工程施工控制网测量提出了较高要求。以平潭海峡公铁大桥首级GPS控制网为例,研究并提出适合于跨海桥梁首级GPS控制网的高精度大规模工程控制网数据处理方法,并采用实测数据进行分析论证。结果表明,使用国际公认的高精度数据处理软件GAMIT,采用合适的模型和参数对多时段长时间的同步观测数据进行精细处理后,可获得高质量的基线向量解。在网平差阶段,引入一种基于相关分析的粗差探测及处理方法,对大于三倍基线中误差的小粗差基线进行探测和分析处理,可进一步提高首级GPS控制网的成果精度。

基于物联网的无人机智能巡线路径规划方法

为在输电线路环境不确定因素较多的情况下,确保智能巡线路径规划效果,提高障碍物勘测安全性及目标搜索准确率,提出基于物联网的无人机智能巡线路径规划方法。基于物联网平台特点,结合输电线路周围环境特征,利用视距测量方法测试安全距离;采用加权距离公式,计算地面对飞行点间距离差,锁定地面差值与飞行距离差值间的加权关系,保证无人机稳定飞行;通过拉格朗日插值法计算输电线路周围节点,对噪声数据进行粗差剔除,补偿差值向量;采用强化学习算法中启发式回报函数,计算样本障碍物间路径,划分最优航线行驶路径,规划无人机智能巡线路径。实验结果表明:所提方法的无人机与障碍物之间的距离差值均未小于200 m,目标搜索个数可达33,数据剔除率高达98%,其智能巡线路径规划和数据粗差剔除效果较好,能够有效提高障碍物勘测安全性和目标搜索准确率。

基于Kalman滤波思想的接触线高度检测数据粗差修正算法

接触线高度平顺性是评价接触网设备动态运行质量的重要指标。针对接触线高度检测数据中的粗差,基于Kalman滤波思想提出相应的粗差辨识与修正算法。将接触线高度视作一个连续平稳系统,利用矩形窗函数定义系统状态值,实现检测数据的分段线性化;依据系统状态值的回归预测方法和状态差异的量化方案,构建粗差辨识与修正算法,并引入适用于锚段关节处双支接触线采集特征的回溯机制;基于大量实测数据,采用显著性检验等统计方法确定算法参数,并从回溯、修正、运用和实时性等方面,对算法有效性和可行性进行分析验证,结果表明:该算法能够完成各类接触线高度检测粗差的高速实时辨识与修正。

基于抗差估计的CPⅢ平面控制网粗差分步探测

针对CPⅢ平面控制网数据处理中粗差探测和严密定权相互影响的问题,提出基于抗差估计的CPⅢ平面控制网粗差分步探测法。将方向观测值和距离观测值分别构网,形成单一类型观测值控制网,再依次针对测边网、方向网采用抗差估计进行粗差探测。实验表明,与传统闭合差检验法相比,基于抗差估计的粗差分步探测法可使CPⅢ点位中误差提高0.15 mm,相邻点位中误差提高0.25 mm,避免了粗差观测值对严密定权的影响,具有更好的粗差探测能力。

DEM粗差权值衰减迭代探测算法研究

为剔除经滤波处理后的机载激光雷达(LIDAR)地面点云数据中的残留非地面点,提高由其制作生产的数字高程模型(DEM)的精度,提出一种基于LIDAR点云数据所生成的不规则分布数据DEM粗差权值衰减迭代探测算法。该算法将滤波后残留的非地面点视为DEM粗差,基于地理学局部地形相似性原理,根据数据点密度及地形起伏变化程度确定局部窗口并进行二次曲面拟合,求解局部窗口内各数据点高程残差值,从而构建粗差相关检验量来定位粗差点。同时,该算法借鉴选权迭代粗差定位法思想,通过迭代,不断衰减含粗差观测点的权值,从而不断减小其对局部窗口二次曲面拟合产生的不良影响,降低地面点被误判为粗差点的概率。通过实验验证,该算法能有效提升DEM粗差探测率并极大降低粗差误判概率,粗差剔除率为98.02%,粗差误判率为1.71%。

基于TOF模式的UWB定位原理与精度验证

通过分析基于飞行时间(time of flight,TOF)的超宽带(ultra wide band,UWB)距离测量的特性,利用卡尔曼滤波及其变式处理原始TOF测距值,剔除TOF测距粗差并对距离观测值进行降噪处理以减小观测误差的影响;同时利用已知长度分析多路径效应和非视距环境等对TOF测距系统误差的影响规律。基于该实验数据的定位结果表明,在较好地改正TOF测距系统误差的情况下,UWB静态定位精度能达到10 cm以内,动态定位精度优于0.2m。

电梯安全性能定量测试分析仪的设计及应用

本文梳理了TSG T7001—2023《电梯监督检验和定期检验规则》和GB/T 7588.1—2020《电梯制造与安装安全规范第1部分:乘客电梯和载货电梯》中对几个重要检验项目的技术要求,这些检验项目需要对电梯的加速度值进行定量测试分析。传统的测试方法存在一定局限性,检验效率和检验质量不高。本文研发了一种电梯安全性能定量测试分析仪,能够对电梯三轴加速度进行测量,对数据进行分析,得到轿厢运行的振动峰峰值、运行速度、行程、制停距离、减速度和制停时间等数值。可以实现对电梯轿厢意外移动保护装置安全性能的检测,具备自动触发和人工触发2种模式,可实现累积误差消除,自动判断轿厢启停,对制动触发点自动进行捕捉,有效地提升检验效率和检验精度。

Real-time filter based on the quasi-accurate detection of gross errors

Because of the sensitivity of the Kalman framework to gross errors, proper techniques for detection of gross errors are necessary. By integrating the selection of quasi-accurate observations and the Kalman framework, a new filter called the quasi-accurate filter (QUAF) is developed. The expansibility and implementation scheme of the new algorithm are then discussed in detail, and the reliability matrix for the Kalman filter is proposed to analyze the reliability of the filters with different detection technologies. Finally, the experimental results from a real world case study are used to validate the conclusions. The QUAF carries out the preliminary selection of the quasi-accurate observations (QAOs) using the innovation of the Kalman filter, and use the check QAOs to determine reasonable observations. This causes the QUAF to handle more easily and possess wider expansibility. QUAF can be reformulated to the special cases of several common detection methods, such as the innovation method, robust estimation and quasi-accurate detection (QUAD). Since only reasonable observations are used, the QUAF has better detection accuracy and stronger avoidance of gross errors than the innovation method and robust estimation. Meanwhile, compared with QUAD methods, QUAF introduces the state-predicted model, requiring fewer quasi-accurate observations and making it more suitable for systems with complicated observation structures or sparse observations.