基于小波变换和DBN的汽车衡传感器故障诊断

称重传感器作为动态汽车衡的核心部件,一旦发生故障将会对动态称重系统造成严重影响.为了准确地对称重传感器进行故障诊断,提出了一种基于小波变换和深度信念网络(DBN)的故障诊断方法.该方法不仅可以有效地判断出传感器信号是正常波动还是故障,还可以通过将DBN模型的预测值代替实测故障值,保证动态称重系统输出的准确性.通过仿真实验证明:该方法能够对故障传感器进行判别与估计,有效提高了动态称重系统的精度.     

称重设备传感器零漂检测方法研究

数字化改造后的称重设备其称重传感器具有故障诊断的功能,但目前缺少针对称重传感器零漂故障诊断的方法。为此,文章提出了基于滑窗原理和零点采样序列标准差的零漂故障检测方法。首先对称重传感器的零点输出信号采样,然后利用滑窗取出其中的n个连续值,求出这n个值的标准差,最后用此标准差与正常输出标准差的比值作为检测依据。当比值大于设定阈值时,传感器存在故障,否则不存在故障。测试表明该方法能有效检测传感器的零漂故障。     

基于径向基神经网络的称重设备传感器故障检测方法

在对称重设备数字化改造的过程中,有些研究人员提出了对某一特定传感器的故障诊断方法,但对于非指定传感器或者两个传感器同时发生故障的情况却没有检测方法.为此,本文提出了一种基于径向基神经网络预测的任意一个或两个称重传感器的故障检测方法.本文首先建立单个传感器的预测模型和任意两个传感器的预测模型,然后通过这两个预测模型计算出任意一个称重传感器的预测值和任意两个传感器的预测值,根据预测值与实际值之间的差值判断称重传感器故障个数、位置、类型等信息.实验表明,当称重传感器的输出误差大于0.3 t时使用此方法可以准确检测出称重传感器的故障信息.     

基于智能容错算法的多传感器皮带秤

传统的多传感器电子皮带秤不具有故障传感器容错功能。针对上述缺陷,提出一种新方法,该方法根据多路传感器之间的相关性,利用径向基函数神经网络（RBFNN）建立故障传感器的预估网络,并将预估信号与其他几路正常传感器信号组合,完成任一传感器在故障状态下的皮带秤称重。经模拟数据仿真,这种方法可有效避免由于传感器故障而产生的计量误差。     

基于专家系统的汽车衡故障传感器判别

传感器是自动化设备的核心部件,传感器故障检测显得尤为重要。针对目前汽车衡维护、检修的困难,为有效准确判断故障传感器,提出了以径向基函数神经网络（RBFNN）预估值的初始化数据库专家系统判别方法。经现场测试准确率达到96%以上,从而有效简单地判定传感器好坏和识别故障传感器的位置。     

并联传感器系统压力传感器故障诊断系统设计

在工业生产过程中电子皮带秤普遍采用多个称重传感器输出信号的并联方法,而该方法对多个称重传感器无法独立采集同时无法准确判断故障;应用TI公司MSC1210单片机最小系统和信号分离器,设计一种并联传感器系统压力传感器故障诊断系统,实现多个称重传感器并联应用时的独立采集;发明一种皮带秤称重传感器累计量校准方法,将不断变化的累计量转换成定量值实现并联传感器系统压力传感器故障诊断;结果表明该系统较好实现多个称重传感器的独立采集和故障传感器判断,为皮带秤等计量装置并联传感器系统的技术升级提供有效手段。     

一种压力传感器单故障实时诊断方法研究

研究对象为容式变极距型液位压力传感器,分析了传感器测量电路的工作原理与特性;故障诊断首先用压力变化率判断测量电路故障存在性;再得到额定输入信号下电路电桥输出信号和运算放大器输出这两个检测信号的表达式,各表达式均以电路相关元件为自变量,因不同元件发生数值漂移故障时,对检测信号的影响趋势各不相同,使得精确到元件的故障定位得以实现;确定故障位置后将正常元件值代入检测信号表达式联立方程组求解,以实现精确到元件的故障定值;仿真显示该故障诊断方法所求得故障值非常接近真实故障值。     

基于径向基函数神经网络预测器的多传感器故障在线检测和信号恢复的研究

理论研究及大量实践表明:径向基函数神经网络具有较强的函数逼近能力,学习速度优于常用的BP网络.本文利用径向基神经网络构成传感器输出预测器实现了多传感器故障在线检测和信号恢复.文中阐述了预测器的构成及其在线学习算法.通过仿真研究证明:该预测器对传感器输出具有很好的在线预测、跟踪能力.当某传感器发生故障时,在及时准确地发出报警信号的同时,对瞬时故障,能很好地恢复故障期间传感器正常的输出,即消除瞬时故障对系统正常运行的影响;对长期故障,能在故障发生后一定的时间范围内,正确估计出传感器正常输出,以保证系统的正常运行.     

基于神经网络传感器组改障诊断方法研究

对于系统状态检测与故障诊断,传感器自身故障的及早发现很重要.本文提出了一种基于神经网络和冗余率计算的传感器组故障监测与诊断的新方法.该方法先用RBF神经网络对传感器组中的各个输出进行预测,若预测值与输出值发生较大的偏差,可能是传感器故障或设备故障,运用传感器之间的冗余率,进一步判断传感器是否发生故障,进而采用对应的诊断策略.     

基于硬件自冗余的称重设备传感器故障检测方法

传统的称重设备采用的是模拟称重传感器,不具有故障自检测功能。有研究人员研究了针对称重设备传感器故障检测的方法,但都是从称重传感器的输出进行检测,忽略了产生故障的原因,导致了检测过程复杂、运算量大。为此文章提出了基于硬件自冗余的称重设备传感器故障检测方法,利用称重传感器自身具有的硬件冗余现象对称重传感器的故障进行检测。实验表明,当称重传感器由于自身质量问题而发生故障时,此方法可以有效检测出称重传感器发生了故障。     

基于集群的卡车与无人机联合配送调度研究

基于集群提出卡车与无人机联合配送新模式,来解决农村地区送货上门难的问题。考虑无人机载重和续航能力,以总运营成本最小为目标建立带时间窗的混合整数规划模型,并提出两阶段算法,通过改进后的K-means算法求出卡车停靠点,采用遗传模拟退火算法优化卡车与无人机联合配送路线。将其与传统K-means算法加CPLEX结果对比,可证明算法和模型的可行性与有效性。案例分析选取江苏某农村地区来进行末端物流配送的应用研究,结果表明卡车与无人机联合配送模式与纯卡车运输模式相比可有效减少总运营成本。研究成果可为农村地区末端配送中无人机的应用提供新思路和参考价值。     

Diagnosability of a class of discrete event systems based on observations

The diagnosability of discrete event systems has been a topic of interest to many researchers. The diagnosability conditions for various systems have evolved based on a regularity condition that is imposed on faulty traces with respect to their observable continuations. Improving upon this weak but necessary condition, a new model of diagnosability that is based on sensor outputs, which are called observations, upon a command input is proposed in this paper. Necessary and sufficient conditions are derived for the proposed diagnosability model. The search performance of the proposed diagnosability condition is of linear complexity in terms of the power set of the system events and observations, compared to the exponential complexity of the search with the existing diagnosability regularity condition. Moreover, a system that is not diagnosable according to the existing diagnosability condition may be diagnosable in the proposed diagnosability model, which includes observations.     

Fault tolerant control for joint structure in PEMS high speed maglev train

The PEMS high speed maglev train, which features a permanent magnet inside an electromagnet, is a new kind of maglev train for long distance intercity transportation. The joint structure, which consists of two single levitation sub‐systems, is the fundamental levitation unit. Two kinds of faults are considered and corresponding fault tolerant control strategies are proposed. The first fault condition is when a gap sensor that is part of a single levitation system is faulty. For this kind of fault, a fault tolerant control strategy based on signal reconfiguration is proposed. The second fault condition is when the whole of a single levitation sub‐system is faulty. Under this condition, a faulty model is firstly established, then a fault tolerant control strategy is designed. When this kind of fault is detected, a switch from the normal controller to the fault tolerant controller can make the faulty system stable.     

SCS系列多台面模块化汽车衡秤台的有限元分析

传统的汽车衡台面结构和刚度分析方法是把汽车衡秤台简化成一简支梁，因其模型过于简化，存在计算结果可靠性差、无法进行局部应力及应变分析等缺点。本文利用ANSYS有限元分析软件，对SCS系列汽车衡秤台进行分析。在实体建模的基础上对刚度进行校核，重点对汽车衡超载时进行极限承载校核．得出超载时强度指标也应成为主要校核指标的结论．并给出合理的改进建议，为汽车衡的设计与生产提供有价值的参考。     

Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA

In many manufacturing processes, some key process parameters (i.e., system inputs) have very strong relationship with the categories (e.g., normal or various faulty products) of finished products (i.e., system outputs). The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, a knowledge-based artificial neural network (KBANN) is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. In addition, a genetic algorithm (GA)-based rule extraction approach named GARule is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for diagnosis of the manufacturing process, provide guidelines on improving the product quality, and are used to construct KBANN. Therefore, the seamless integration of GARule and KBANN provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to a japing-line, which improves the product quality and saves manufacturing cost.     

Partial-DNA cyclic memory for bio-inspired electronic cell

Genome memory is an important aspect of electronic cells. Here, a novel genome memory structure called partial-DNA cyclic memory is proposed, in which cells only store a portion of the system’s entire DNA. The stored gene number is independent of the scale of embryonic array and of the target circuit, and can be set according to actual demand in the design process. Genes can be transferred in the cell and the embryonics array through intracellular and intercellular gene cyclic and non-cyclic shifts, and based on this process the embryonic array’s functional differentiation and self-repair can be achieved. In particular, lost genes caused by faulty cells can be recovered through gene updating based on the remaining normal neighbor cells during the self-repair process. A reliability model of the proposed memory structure is built considering the gene updating method, and depending on the implementations of the memory, the hardware overhead is modeled. Based on the reliability model and hardware overhead model, we can find that the memory can achieve high reliability with relatively few gene backups and with low hardware overhead. Theoretical analysis and a simulation experiment show that the new genome memory structure not only achieves functional differentiation and self-repair of the embryonics array, but also ensures system reliability while reducing hardware overhead. This has significant value in engineering applications, allowing the proposed genome memory structure to be used to design larger scale self-repair chips.     

Fault detection and measurements correction for multiple sensors using a modified autoassociative neural network

Periodic manual calibrations ensure that an instrument will operate correctly for a given period of time, but they do not assure that a faulty instrument will remain calibrated for other periods. In addition, sometimes such calibrations are even unnecessary. In industrial plants, the analysis of signals provided by process monitoring sensors is a difficult task due to the high dimensionality of the data. A strategy for online monitoring and correction of multiple sensors measurements is therefore required. Thus, this work proposes the use of autoassociative neural networks, trained with a modified robust method, in an online monitoring system for fault detection and self-correction of measurements generated by a large number of sensors. Unlike the existing models, the proposed system aims at using only one neural network to reconstruct faulty sensor signals. The model is evaluated with the use of a database containing measurements collected by industrial sensors that monitor and are used in the control of an internal combustion engine installed in a mining truck. Results show that the proposed model is able to map and correct faulty sensor signals and achieve low error rates.     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

On GID-Testable Two-Dimensional Iterative Arrays

A new approach is presented for easily testable two-dimensional iterative arrays.It is an improvement of GI-testability (Group Identical testability)and is referred to as GID-testability (Group Identical and Different testability).In a GID-testable two-dimensional array,the primary x and y outputs are organized into groups and every group has more than one output.This is similar to the GI-testable arrays.However,GID-testability not only ensures that identical test responses can be obtained from every output in the same group when an array is fault free,but also ensures that at least one output has different test responses (from the other outputs in a group)when a cell in the array is faulty.Therefore,all faults can be detected under the assumption of a single faulty cell model.It is proved that an arbitrary two-dimensional iterative array is GID-testable if seven x-states and seven y-states are added to the original flow table of the basic cell of the array.GID-testability simplifies the response verification of built-in-self-testing in a way similar to PI-and GI-testability^[6-9].Therefore,it is suitable for BIST design.     

基于小波变换的动态称重信号处理方法

针对目前动态汽车衡因货车拖磅称重而导致称重信号异常及称重不准确问题,提出采用小波变换极大值信号重构算法对拖磅称重信号进行处理:首先将拖磅称重信号逐层分解到不同频域和时域,在保持频率不变的条件下,对称重信号逐级求极大值点,得出信号逐级变化趋势;然后将多级称重信号按原离散逼近系数重构成新的称重信号,进而得到车辆称重信息。现场实测表明,采用小波变换极大值信号重构算法处理后的货车拖磅称重数据与正常过磅时称重数据的误差小于1%。