Feed-Forward Neural Network Based Petroleum Wells Equipment Failure Prediction

In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver with 1/4 rate reconfigurable 4-tap FFE and half-rate slicer in a 28-nm CMOS

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver(TRx)designed in a 28-nm complementary metal-oxide-semiconduc-tor(CMOS)process is presented in this article.A voltage-mode(VM)driver featuring a 4-tap reconfigurable feed-forward equal-izer(FFE)is employed in the quarter-rate transmitter(TX).The half-rate receiver(RX)incorporates a continuous-time linear equal-izer(CTLE),a 3-stage high-speed slicer with multi-clock-phase sampling,and a clock and data recovery(CDR).The experimen-tal results show that the TRx operates at a maximum speed of 56 Gb/s with chip-on board(COB)assembly.The 28 Gb/s NRZ eye diagram shows a far-end vertical eye opening of 210 mV with an output amplitude of 351 mV single-ended and the 56 Gb/s PAM-4 eye diagram exhibits far-end eye opening of 33 mV(upper-eye),31 mV(mid-eye),and 28 mV(lower-eye)with an output amplitude of 353 mV single-ended.The recovered 14 GHz clock from the RX exhibits random jitter(RJ)of 469 fs and deterministic jitter(DJ)of 8.76 ps.The 875 Mb/s de-multiplexed data features 593 ps horizontal eye opening with 32.02 ps RJ,at bit-error rate(BER)of 10-5(0.53 UI).The power dissipation of TX and RX are 125 and 181.4 mW,respectively,from a 0.9-V sup-ply.

基于LSTM算法的前馈控制系统扰动预测研究

为了提高机械自动化系统前馈控制精度,设计了一种基于长短期记忆LSTM循环神经网络的前馈控制系统扰动预测模型,并开展实验测试分析。研究结果表明:预测结果中较大间隔下形成的扰动信号序列均方根误差更低,具备更高精度;小间隔时间尺度下形成了更低峭度的扰动信号序列。经过组合预测序列结果更加趋近高占比的序列,获得更小负峭度与序列波动,降低均方根误差,达到更精确的效果。

双控制方式下带缆水下机器人轨迹跟踪与水动力响应分析

本文首先根据已有的脐带缆运动控制方程,通过引入与工作船和水下机器人连接点处边界条件和速度耦合关系,与水下机器人运动方程一同构成完整的水动力数学模型;其次提出收放脐带缆的前馈-反馈控制方法和调节螺旋桨转速的增量式PID(Proportion Integration Differentiation)算法,建立完整的带缆水下机器人系统水动力与控制模型;最后对所提出的模型进行数值模拟实验验证与运动控制操纵下的水动力分析。数值计算结果表明:本文所提出的带缆水下机器人系统水动力与控制模型是有效可靠的,垂向运动控制过程中,水下机器人纵摇角、横摇角和沉深的模拟值与实验值最大误差分别为2°、1°和-50 mm;对于PID算法,调节螺旋桨转速控制水下机器人对给定位置信息的跟踪响应效果,模拟值与实验值相差均不大;轨迹跟踪模拟计算沿水平(X轴)方向和竖直(Z轴)方向最大相对误差分别为10%和15%;水下机器人竖向(沿Z轴)运动主要由调节脐带缆长度的前馈-反馈策略决定,沿水平(X轴)方向的运动主要由调节螺旋桨转速的PID算法控制;机器人水动力载荷受其周围流场变化的影响,而流场的变化主要由机器人运动速度的改变和螺旋桨转速变化二者共同决定。

基于Maxwell模型的压电驱动系统前馈控制方法

为了提高外腔可调谐半导体激光器的测量精度,基于Maxwell模型提出了用于消除其压电驱动系统迟滞非线性的前馈控制方法.基于经典Maxwell力学模型,建立了描述非凸、非对称迟滞环的静态迟滞模型;根据频率与位移幅值的关系建立了率相关系数矩阵,与静态迟滞模型耦合得到率相关迟滞模型,实现了不同频率下压电驱动系统迟滞非线性的精准预测;构建了迟滞逆模型,对压电驱动系统进行前馈补偿控制.实验结果表明,率相关迟滞模型具有较高的精度,1 Hz下模型均方根误差为0.049μm, 50 Hz下模型均方根误差为0.252μm.基于迟滞逆模型的前馈控制方法能够明显提高系统输出位移的线性度,增大线性度范围,线性相关系数R2由0.971 58提高到了0.999 77.

丝杠进给系统运动控制研究

丝杠进给系统在运行过程中具有非线性、强耦合和参数时变等特点,由于传统PID控制器的参数固定,无法很好地适应系统变化,从而导致系统响应速度缓慢及跟踪性能较差。针对此类问题,将复杂的丝杠进给系统简化处理并建立二质量系统模型;对传统的模糊规则加以优化和改进,设计了一种模糊PI控制与加速度前馈补偿相结合的复合控制器。模糊PI控制器根据实际系统特性调整模糊规则,使得控制器参数自适应变化,提高控制效果;加速度前馈补偿控制器根据输入信号预先补偿系统,提升了系统带宽。在Simulink中搭建系统模型,在阶跃输入和干扰输入下对比观察2种控制器作用的系统响应曲线。经仿真验证,丝杠进给系统的动态特性、稳态特性和抗干扰能力都得到了明显地提升。

管道电流测绘交流恒流源双环控制策略研究

为解决埋地输油管道阻容性负载对恒流源的影响,结合交流恒流源模型,针对传统电压电流双闭环控制在应对阻容性负载时,出现的输出波形失真问题展开研究,提出一种管道电流测绘交流恒流源双环控制策略。在内环控制方面,采用电感电流瞬时反馈和负载电流前馈的PI控制策略;对于外环控制,采用负载电压反馈的PI控制策略。为进一步完善系统性能,运用极点配置法对控制器参数进行设计,以实现更加精准的控制,并通过Matlab进行频域特性分析和Simulink仿真验证。结果表明:经过优化的控制策略能够有效抑制阻容性负载条件下的电压波形畸变,同时在阻性负载和阻容性负载条件下的输出电流均显示出良好的正弦性,总谐波失真率分别为2.26%和3.19%,具备良好的动态性能和鲁棒性,可为埋地输油管道输油系统的检测提供坚实的技术基础。

Real-time Feed-forward Force Compensation for Active Magnetic Bearings System Based on H∞ Controller

There are two kinds of unbalance vibrations—force vibration and displacement vibration due to the existence of unbalance excitation in active magnetic bearings（AMB） system. And two unbalance compensation methods—closed-loop feedback and open loop feed-forward are presented to reduce the force vibration. The transfer function order of the control system directly influencing the system stability will be increased when the closed-loop method is adopted, which makes the real-time compensation not easily achieved. While the open loop method would not increase the primary transfer function order, it provides conditions for real-time compensation. But the real-time compensation signals are not easy to be obtained in the open loop method. To implement real-time force compensation, a new method is proposed to reduce the force vibration caused by the rotor unbalance on the basis of AMB active control. The method realizes real-time and on-line force auto-compensation based on H∞ controller and one novel feed-forward compensation controller, which makes the rotor rotate around its inertia axis. The time-variable feed-forward compensatory signal is provided by a modified adaptive variable step-size least mean square（VSLMS） algorithm. And the relevant least mean square（LMS） algorithm parameters are used to solve the H∞ controller weighting functions. The simulation of the new method to compensate some frequency-variable and sinusoidal signals is completed by MATLAB programming, and real-time compensation is implemented in the actual AMB experimental system. The simulation and experiment results show that the compensation scheme can improve the robust stability and the anti-interference ability of the whole AMB system by using H∞ controller to achieve close-loop control, and then real-time force unbalance compensation is implemented. The proposed research provides a new control strategy containing real-time algorithm and H∞ controller for the force compensation of AMB system. And the stability of the control system is finally improved.

ADAPTIVE FEED-FORWARD COMPENSATOR FOR HARMONIC CANCELLATION IN ELECTRO-HYDRAULIC SERVO SYSTEM

Since the dead zone phenomenon occurs in electro-hydraulic servo system, the output of the system corresponding to a sinusoidal input contains higher harmonic besides the fundamental input, which causes harmonic distortion of the output signal. The method for harmonic cancellation based on adaptive filter is proposed. The task is accomplished by generating reference signals with frequency that should be eliminated from the output. The reference inputs are weighted by the adaptive filter in such a way that it closely matches the harmonic. The output of the adaptive filter is a harmonic replica and is injected to the fundamental signal such that the output harmonic is cancelled leaving the desired signal alone, and the total harmonic distortion （THD） is greatly reduced. The weights of filter are adjusted on-line according to the control error by using least-mean-square （LMS） algorithm. Simulation results performed with a hydraulic system demonstrate the efficiency and validity of the proposed adaptive feed-forward compensator （AFC） control scheme

Dynamic Velocity Feed-Forward Compensation Control with RBF-NN System Identification for Industrial Robots

A dynamic velocity feed-forward compensation （RBF-NN） dynamic model identification was presented for control （DVFCC） approach with RBF neural network the adaptive trajectory tracking of industrial robots. The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model. The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accu- racy. The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can con- vert torque signal into velocity in the standard industrial controller. Then, the need for a torque control interface was avoided in the real-time dynamic control of industrial robot. The simulations and experiments were carried out on a gas cutting manipulator. The results show that the proposed control approach can reduce steady-state error, suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space, especially under high- speed condition.

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

Load Shedding Strategy Based on Combined Feed-Forward Plus Feedback Control over Data Streams

In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.

Noise decomposition algorithm and propagation mechanism in feed-forward gene transcriptional regulatory loop

Feed-forward gene transcriptional regulatory networks, as a set of common signal motifs, are widely distributed in the biological systems. In this paper, the noise characteristics and propagation mechanism of various feed-forward gene transcriptional regulatory loops are investigated, including （i） coherent feed-forward loops with AND-gate, （ii） coherent feed-forward loops with OR-gate logic, and （iii） incoherent feed-forward loops with AND-gate logic. By introducing logarithmic gain coefficient and using linear noise approximation, the theoretical formulas of noise decomposition are derived and the theoretical results are verified by Gillespie simulation. From the theoretical and numerical results of noise decomposition algorithm, three general characteristics about noise transmission in these different kinds of feed-forward loops are observed, i） The two-step noise propagation of upstream factor is negative in the incoherent feed-forward loops with AND-gate logic, that is, upstream factor can indirectly suppress the noise of downstream factors, ii） The one-step propagation noise of upstream factor is non-monotonic in the coherent feed-forward loops with OR-gate logic, iii） When the branch of the feed-forward loop is negatively controlled, the total noise of the downstream factor monotonically increases for each of all feed-forward loops. These findings are robust to variations of model parameters. These observations reveal the universal rules of noise propagation in the feed-forward loops, and may contribute to our understanding of design principle of gene circuits.

A double closed loop APFC control with feed-forward

In order to improve the steady state performance,dynamic response and power factor of traditional power factor correction(PFC)digital control method and reduce the harmonic distortion of input current,a double closed loop active power factorcorrection(APFC)control method with feed-forward is proposed.Firstly,the small signal model of Boost PFC control systemis built and the system transfer function is deduced,and then the parameters of the main device with Boost topology is estimated.By means of the feed-forward,the system can quickly respond to the change in input voltage.Furthermore,the use ofvoltage loop and current loop can achieve input current and output voltage regulation Simulink modeling shows that this methodcan effectively control the output voltage in case of input voltage largely fluctuating,improve the system dynamic response abilityand input power factor,and reduce the input current harmonic distortion

Feed-Forward-Like Decoupling Control in Coagulation Bath of Carbon Fiber Precursor

The coagulation bath system of carbon fiber precursor is a complicated and multivariable coupling system. Based on the model of industrial production,the full dynamic decoupling control of the coagulation bath system of carbon fiber precursor is achieved in combination with multivariable feed-forward-like decoupling and proportional-integral-differential( PID) control. Compared with the conventional PID decoupling control,the experiment results show that the proposed method has a better control effect. The use of the controller can achieve complete decoupling of three parameters from coagulation bath system. The method should have great applications.

基于前馈神经网络的分数阶事件触发控制器实现及应用研究

为了提高事件触发控制算法的普适性,降低现有控制算法对具体数学表达式的依赖,提出了一种基于前馈神经网络的事件触发控制方法,实现了更具一般化的分数阶事件触发控制器设计。该方法充分发挥前馈神经网络可以任意逼近平方可积函数的优势,对传统事件触发控制过程进行学习,有效解决了传统方法对具体数学模型的依赖问题,且可进一步降低事件触发控制的次数。另外,相较于传统的整数阶控制器,分数阶控制器拓宽了事件触发控制算法的实际应用范围。以具体应用为背景进行仿真分析,结果表明,该算法不但能够保持传统事件触发控制算法在节省控制资源方面的优势,而且在可移植性方面优于传统的事件触发控制算法。

水泥粉磨优化控制系统的设计和实现

采用广义预测控制算法克服大滞后问题,引入前馈解决强干扰问题,设计并实现了一套水泥粉磨优化控制系统。该系统已经在某水泥厂长期投运,有效改善了水泥粉磨控制品质。

六相串联三相双PMSM驱动系统低速区转子位置角高精度解耦观测研究

静止坐标系下通过控制不同平面的电压矢量可以实现六相串联三相PMSM系统中两台电机的独立解耦控制。基于此,在静止坐标系下六相平面和三相平面同时注入旋转高频电压,通过解调对应平面高频电流的负序分量,解耦获得两台电机的转子位置角初步观测值。为了补偿定子电阻、转速和滤波器对观测带来的误差,利用高频电流正序分量中误差角信息与转速前馈相结合的补偿方法,对转子位置角观测值进行补偿。实验结果表明,六相和三相电机稳态转子位置角观测误差绝对值的平均值为0.146和0.106 rad,动态下最大误差为0.2 rad;基于观测的转子位置角构建的无位置传感器直接转矩控制系统低速各种运行工况性能优越。

弱电网下改进LADRC抑制直驱风机次同步振荡研究

针对在弱电网下直驱风电机组引起的次同步振荡(subsynchronousoscillation,SSO)现象,提出基于一阶总扰动偏差控制的微分前馈线性自抗扰控制器(linear active disturbance rejection control,LADRC),采用全改进LADRC控制策略抑制SSO现象(“全”是指电压外环、电流内环以及PLL锁相环3个环节都采用相应的控制)。首先,建立直驱风电机组并网数学模型;其次,结合风电机组并网系统对改进LADRC控制器进行设计并对其进行特性分析,该控制器相较于传统LADRC,不仅减小系统的跟踪误差且抗干扰性能更强;最后,通过PSCAD/EMTDC仿真软件将本文策略与全PI、全传统LADRC进行仿真对比。结果表明:相较于全传统LADRC,本文方法在降低1.62%超调量的同时,缩短0.129 s系统调节时间,有效抑制SSO现象并且具有较好的适应性。

热力站机组供暖自动控制系统的设计与仿真验证

为解决热力站供暖系统中的供水分系统和补水分系统采用传统PID控制算法对应的控制精度低、稳定性差等问题,在对热力站供暖系统基本组成和供水、补水系统分析的基础上,分别以供水系统和补水系统为研究对象,对其当前控制系统存在的问题进行分析,提出了以自适应模糊PID控制对供水系统进行优化控制,以前馈-串级控制器对补水系统进行优化控制。通过仿真计算验证了优化控制的效果。