Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH-SC method

Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.

A Hybrid Time-delay Prediction Method for Networked Control System

This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.

Time-delay Compensation Method for Networked Control System Based on Time-delay Prediction and Implicit PIGPC

A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller(PIGPC) is proposed. The least squares support vector machine(LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization(PSO) algorithm,and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller(GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based(PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified.