Research on static characteristic of double redundance double nozzle flapper valve based on AMESim

The feedback spring rod of the armature assembly is cancelled in the double redundance double nozzle flapper valve(DRDNFV),and the difficulty of valve core displacement control is increased.Therefore,this paper intends to study the static characteristic of DRDNFV through the AMESet and AMESim simulation.It is explored under the circumstance of the fixed orifices being clogged and experimentally verified on the test bench.The results show that the pressure gain increases and the flow gain decreases with the increasing clogged degree of the fixed orifices on both sides.The zero bias increases synchronously with the increasing clogged degree of the unilateral fixed orifice.The experimental results are basically consistent with the theoretical curves and the theoretical correctness of the simulation model is effectively verified.The results can provide the theoretical reference for design,debugging,maintenance and fault diagnosis of DRDNFV.

Fault Detection and Diagnosis of Pneumatic Control Valve Based on a Hybrid Deep Learning Model

As the growing requirements for the stability and safety of process industries,the fault detection and diagnosis of pneumatic control valves have crucial practical significance.Many of the approaches were presented in the literature to diagnose faults through the comparison of residual sequences with thresholds.In this study,a novel hybrid neural network model has been developed to address the issue of pneumatic control valve fault diagnosis.First,the feature extractor automatically extracts in-depth features of the signals through multi-scale convolutional neural networks with different kernel sizes,which not only adequately explores the local distinguishable features,but also takes into account the global features.The extracted features are then fused by the feature fusion layer to reduce redundant features.Finally,the long short-term memory for fault identification and the dense layer for fault classification.Experimental results demonstrate that the average test accuracy is above 94%and 16 out of the 19 conditions can be successfully detected in the simulated actual industrial environment.The effectiveness and practicability of the proposed method have been verified through a comparative analysis with existing intelligent fault diagnosis methods,and the results suggest that the developed model has better robustness.

Research on Inter-turn Short-circuit Fault Diagnosis Method Based on High Frequency Voltage Residual for PMSM

Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.

Fault Diagnosis of Valve Clearance in Diesel Engine Based on BP Neural Network and Support Vector Machine

Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square(RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network(GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.

Fault Diagnosis of Reciprocating Compressors Valve Based on Cyclostationary Method

The relationship between second-order cyclostationary method and time-frequency distribution is studied,and cyclic autocorrelation(CA) function is indicated to be one sort of special time-frequency distribution method.Furthermore,a fault diagnosis method for reciprocating compressors based on empirical mode decomposition(EMD) and CA function is proposed,and then it is applied to the fault diagnosis of reciprocating compressor valve.Firstly,the vibration signal of reciprocating compressor valve is decomposed by using EMD method,and several intrinsic mode functions(IMFs) are obtained.Secondly,the IMFs are evaluated by some denoising criterions to remove the noise and interfering ones.Finally,the CA functions of the remained IMFs are calculated,which will be used to reconstruct the CA function of the original vibration signal.Engineering application indicates that this method can sufficiently inhibit the cross-interference items of CA function.Therefore,more explicit working conditions of reciprocating compressor components can be achieved.

Fault Diagnosis of Overflow Valve Based on Trispectrum

The high-order spectrum can effectively remove Gaussian noise. The three-spectrum and its slices represent random signals from a higher probability structure. It can not only qualitatively describe the linearity and nonlinearity of vibration signals closely related to mechanical failures, Gaussian and non-Gaussian Performance, and can greatly improve the accuracy of mechanical fault diagnosis. The two-dimensional slices of trispectrum in normal and fault states show different peak characteristics. 2-D wavelet multi-level decomposition can effectively compress 2-D array information. Least squares support vector machine can obtain the global optimum under limited samples, thus avoiding the local optimum problem, and has the advantage of reducing computational complexity. In this paper, 2-D wavelet multi-level decomposition is used to extract features of trispectrum 2-D slices, and input LSSVM to diagnose the fault of the pressure reducing valve, which has achieved good results.

Research and Development of Electro‑hydraulic Control Valves Oriented to Industry 4.0:A Review

Electro-hydraulic control valves are key hydraulic components for industrial applications and aerospace,which controls electro-hydraulic motion.With the development of automation,digital technology,and communication technology,electro-hydraulic control valves are becoming more digital,integrated,and intelligent in order to meet the requirements of Industry 4.0.This paper reviews the state of the art development for electro-hydraulic control valves and their related technologies.This review paper considers three aspects of state acquisition through sensors or indirect acquisition technologies,control strategies along with digital controllers and novel valves,and online maintenance through data interaction and fault diagnosis.The main features and development trends of electro-hydraulic control valves oriented to Industry 4.0 are discussed.

Analysis on the fault characteristics of three-phase short-circuit for half-wavelength AC transmission lines

Half-wavelength AC transmission(HWACT) is an ultra-long distance AC transmission technology, whose electrical distance is close to half-wavelength at the system power frequency. It is very important for the construction and operation of HWACT to analyze its fault features and corresponding protection technology. In this paper, the steady-state voltage and current characteristics of the bus bar and fault point and the steady-state overvoltage distribution along the line will be analyzed when a three-phase symmetrical short-circuit fault occurs on an HWACT line. On this basis, the threephase fault characteristics for longer transmission lines are also studied.

Remedial Phase-Angle Control of a Five-Phase Fault-Tolerant Permanent- Magnet Vernier Machine With Short-Circuit Fault

A fault-tolerant permanent-magnet vernier(FT-PMV)machine incorporates the merits of high fault-tolerant capability and high torque density.In this paper,a remedial phase-angle control(RPAC)strategy is proposed for a five-phase FT-PMV machine with short-circuit fault.Firstly,the proposed strategy can reduce the amount of unknown quantities by structuring the phase-angles of the normal phases.It can simplify the calculation of the remedial currents.Then,in order to obtain the desired torque,only the amplitudes of the remedial currents need to be calculated.Based on the principle of instantaneous electrical input power and mechanical output power balance condition,the real components are used to maintain the torque capability,while the reactive components are limited zero to minimize the torque ripple.Both simulations and experiments are presented to verify the proposed RPAC strategy.

ValveLink软件在天然气调节阀故障诊断方面的应用

介绍了ValveLink软件在川东北分公司高含硫天然气净化装置对调节阀故障进行智能诊断的应用情况。采用ValveLink软件可以在不解体调节阀的情况下,对在线运行的阀部件进行损坏情况预判,有助于维修工程师提前制定准确的大修维修方案、购买备品备件,有效减少阀门的维修时间和备件的购买成本,实现对控制阀的预防性维护和全生命周期管理,更好地提升调节阀的使用效果,为生产装置平稳运行提供有力保障。

基于小波包分析和优化KNN的电动开度阀故障检测方法

针对以微控制器MCU为控制核心的电动开度阀控制系统难以集成高效且计算量小的故障检测子系统的问题,基于小波包变换和优化K近邻(K-Nearest Neighbor,KNN)算法提出了一种电动开度阀故障检测方法。对阀门振动信号进行小波包变换,计算小波包节点的能量值与其重构信号的时域特征参数。根据Pearson系数筛选出两种与能量强相关的故障特征参数:峰峰值与均方根,并将两者作为KNN算法的样本评价指标;通过对评价指标进行加权优化了KNN算法的距离计算公式,分别在MATLAB和实验样机中进行故障检测测试,对应最高分类准确率分别为92.5%与86.7%。结果表明:实验测试与仿真分析具有较好的一致性,该故障检测方法的优势在于计算量小、故障识别率较高,并能有效地应用于以MCU为核心的电动开度阀控制系统。

博世共轨压力泄压阀打开故障解析

文章主要对泄压阀的结构原理、故障发生机理进行系统介绍,并结合故障诊断与排除思路进行说明,以期能够为维修人员提供一些启发和帮助。

基于数据驱动的主燃油计量装置故障诊断

为建立主燃油计量装置的故障诊断方法,提出一种基于快速存取记录器(QAR)数据的燃油计量参数估计模型建模方法,并结合液压机械控制装置工作原理,给出了基于燃油计量参数残差设计的主燃油计量装置故障诊断方案。设计基于系统辨识的燃油计量活门位置(FMVP)估计模型,采取降阶处理策略以提高模型的估计精度和动态响应速度,在此基础上给出基于多项式拟合的燃油流量(FF)估计模型。结果表明:所建立的FMVP模型估计残差不超过±2%,FF估计模型残差不超过±5%;该方案能够有效地诊断出燃油计量活门故障、线性可变差动位移传感器故障和热式流量计故障。该方案结构简单,不需要大量的调试技术,提高了故障检测精度,具有工程应用前景。

SMA驱动尿道阀可靠性的蒙特卡罗仿真分析

形状记忆合金(SMA)驱动尿道阀是应对尿道功能障碍设计的机电装置,若临床应用一旦发生失效,将被迫取出更换。为研究尿道阀的可靠性,依据尿道阀组成原理建立了尿道阀故障树和可靠性数学模型,提出了一种基于蒙特卡罗法的尿道阀仿真算法,仿真分析了尿道阀的可靠性指标。实验结果表明:该仿真算法简单有效,尿道阀平均寿命达10000次的失效概率为0.012。该仿真算法也可为克服膀胱功能障碍的SMA驱动人工逼尿肌系统可靠性设计提供理论指导。

脉冲电磁阀的集成分类器故障诊断方法

脉冲电磁阀因具有结构简单、成本低、动作快速等优点,被广泛应用于布袋除尘领域,故其元件的健康状态对系统运行安全具有重大的意义。针对脉冲电磁阀常见故障的诊断方法展开深入研究,利用接近开关检测主阀芯开启滞后时间和保持时间作为特征后,构建Bagging-DT(RF)分类器模型进行故障诊断,通过对多种模拟故障的实验结果和多种模型的对比,结果表明,构建的Bagging-DT模型的精确率、召回率、F1值均优于其他模型,且其准确率达到86%,实现了对脉冲电磁阀5种及以上故障更加准确的预测与判断,解决了实际工作中工作人员依靠主观经验,诊断效果可信度较低的问题。

基于监督对比学习和混合注意力残差网络的隔膜泵单向阀故障诊断

由于工业生产环境中的强噪声和其他环境激励,隔膜泵单向阀不同故障的特征呈现一定的相似性,导致传统深度学习方法对单向阀的故障状态难以准确识别。为解决这一问题,提出了一种结合监督对比学习和混合注意力残差神经网络(HA-ResNet)的隔膜泵单向阀故障诊断方法。首先,将注意力机制引入了残差神经网络以提升网络的学习能力,自适应调节了重要但微弱特征权重,并以恒等变换减少了有效信息被抑制现象;其次,提出了加权“监督对比损失(SCL)+交叉熵(CE)损失”,调节单向阀不同故障状态数据之间的距离,明确了单向阀不同故障状态的分类边界与降低噪声或环境激励的干扰;最后,通过工程实测数据,对监督对比学习和HA-ResNet融合方法的有效性和稳定性进行了验证。研究结果表明:监督对比学习和HA-ResNet融合方法在隔膜泵单向阀验证集上的平均准确率达到了99.3%;与其他故障诊断方法相比,其在诊断精度和稳定性上都具有一定的优势,验证了该方法在噪声干扰条件下故障诊断的可靠性。

农机用先导式减压阀建模及故障仿真研究

阐述农机常用先导式减压阀工作原理,利用AMESim软件建立阀的仿真模型,仿真分析主阀阻尼孔堵塞、主阀阀芯卡滞、先导阀弹簧疲劳等常见故障工况下先导式减压阀工作时的动态特性。仿真结果表明:应用AMESim软件仿真能够真实地模拟先导式减压阀实际工作时的各类故障响应,为快速、准确地诊断先导式减压阀及类似液压元件故障提供一种新的方法与依据,大大降低了故障诊断时间,提高了整机工作效率。

基于Simscape的燃气轮机燃料控制阀建模及故障分析

目前国内对燃气设备的研究中,针对控制阀等阀门设备的研究仍有欠缺,其模型大多停留在单个的液压阀门或液压缸研究。对此,利用Simulink/Simscape组件针对从PI控制器到燃气管道的整个燃气轮机燃料控制阀进行建模,并针对固定节流孔堵塞、滑阀阀芯磨损、液压油污染等故障进行模拟分析,探讨其故障形式及原因。结果表明:喷嘴挡板阀单侧固定节流孔堵塞、滑阀阀芯的磨损以及液压油中的杂质均会不同程度地造成阀门的响应迟缓,甚至堵塞卡滞。最后,针对当前电厂对阀门数据的监测提出如下建议:可以根据实际运行状况使用电反馈伺服阀引入伺服阀阀芯位移信号及液压缸活塞位移等参数的监控以便更好地判断阀门健康状态。

基于SSA-VMD的气阀信号故障特征提取方法研究

为了解决VMD算法因人为预设分解层数K及惩罚因子α两个参数而导致的信号过分解和欠分解的问题,提出了一种基于SSA-VMD的隔膜压缩机气阀故障特征提取方法。该方法以各分量的最小包络熵作为适应度函数,对VMD进行参数优化。将SSA-VMD方法与原始VMD方法进行对比分析,分别计算两种分解方法各分量的多尺度散布熵(MDE)值,选取合适的熵值作为特征向量,利用随机森林算法进行故障的分类识别,从而实现对隔膜压缩机气阀各类故障的诊断。研究结果表明:使用原始VMD方法分解后的数据,故障识别的平均准确率为90.63%,而利用SSA-VMD方法故障识别的平均准确率高达99.32%。该结果表明使用SSA-VMD方法分解信号,故障诊断的准确率明显提高。

基于深度卷积神经网络的单向阀泄漏模式识别

以SV10PB1-30B液控单向阀为研究对象,利用传感器采集3种不同泄漏模式下10个阀芯的振动信号,设计深度卷积模型,开展不同测点(单向阀的上表面和阀座)、不同信号特征提取方式(原始信号、特征值、特征图)下的模式识别研究。结果表明:基于轴向冲击信号特征值和深度卷积神经网络的模型能有效识别故障类型,验证集上的识别准确率高达88.293%,是基于特征图的7.79倍,是基于原始时域冲击信号的1.16倍;训练步数以100的较优,同时该模型对正常阀芯和不同损伤阀芯的分类效果明显。