利用加速度信号测量位移是油田抽油井光杆位移测量的主要方法 而加速度信号的随机噪声和趋势项是影响

测量精度的主要因素 本文提出了一种基于学习的实时消噪和剔除趋势项方法 学习时先获取一段时间的加速度信

号 再通过时间序列分析技术得出 ARIMA 模型及其参数 最后基于 FFT 变换的 Rife-Jane 频率估计方法求出加速度

信号的周期 在线实时消噪和剔除趋势项方法是基于学习阶段所得模型参数 运用卡尔曼滤波技术消除加速度信号

随机噪声 按周期两次积分得到光杆位移 用加窗递推最小二乘法在线消除趋势项 通过抽油机半实物仿真平台测试

和分析加速度信号 结果表明 该方法有效地去除了加速度信号中的噪声和趋势项 极大地提高了位移的测量精度-Acceleration signal based approach is a main approach used to measure the displacement of polish rod in the

oilfield pumping wells. In this study a learning based real time noise immunization and trend term elimination approach is

proposed. During the learning acceleration signal within a period of time are firstly acquired then corresponding ARIMA

model and its parameters are derived finally the period of the acceleration signal is computed by using FFT transformation

and Rife Jane frequency estimation. The proposed approach bases the model obtained parameters uses Kalman filtering

techniques to remove random noise computes the polish rod displacement by a quadratic integration of the period and

eliminates the trend term by windowed recursive least squares method. Eventually an experiment over a pumping unit

hardware in the loop plant is carried out which indicates that the proposed approach can effectively eliminate noise and trend

term and obviously improve the measuring pre