Knowledge mining technique based fault diagnosis of shape control system in a rolling process

2010 ◽  
Author(s):  
Zhi Zhou ◽  
Ningyun Lu ◽  
Bin Jiang
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Shape Control ◽  
Rolling Process ◽  
Knowledge Mining ◽  
Mining Technique

Fault Diagnosis Method for a Network Control System with Long-Time Delay and Data Packet Loss Based on H∞ Filtering

2013 ◽  
Vol 846-847 ◽  
pp. 795-798
Author(s):  
Jiao Meng ◽  
Qi Hua Xu ◽  
Xiao Xiao
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Time Delay ◽  
Packet Loss ◽  
Estimation Error ◽  
Network Control ◽  
Data Packet ◽  
Practical Significance ◽  
Network Control System ◽  
Long Time

Improving network control system---NCS reliability and safety has important practical significance because NCS is a hot research subject in these years. Fault diagnosis methods are researched in this paper according to NCS with long-time delay and data packet loss. Firstly, given a NCS with long-time delay, a state observer is structured. Secondly, make the state estimation error equation equivalent to an asynchronous dynamical system having event incidence constraint according to whether the system having data packets loss. The problem of fault diagnosis is converted to filtering problem through structuring filtering residual system based on the observer, then giving a corresponding filter designing algorithm. The designed fault diagnosis filter system not only make sure the stability of the closed loop system but also make the residual systems norm less than given reduction level. Finally, the simulation results prove that the algorithm can diagnose faults effectively.

FAULT DIAGNOSIS IN A PROCESS EXPERIMENTAL CONTROL SYSTEM WITH UNKNOWN FACTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 151-155 ◽  
Author(s):  
AKIRA INOUE ◽  
MINGCONG DENG
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Experimental System ◽  
Detection Problem ◽  
Experimental Control ◽  
Blind System Identification ◽  
Experimental Process ◽  
Identification Approach ◽  
Simulation Results ◽  
Detecting Method

A fault detection problem in a process control experimental system with unknown factors is presented in this paper. The fault detecting method is based on blind system identification approach. The experimental system actuator output includes unknown dynamics and unknown fault signal. By using the fault detecting method, the fault signal is detected. Simulation results for the experimental process are presented to show the effectiveness.

Fault Diagnosis of New Certain Mine Sweeping Plough’s Electrical Control System Based on Data Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 539-543
Author(s):  
Yong Zhao ◽  
Xiao Qiang Yang ◽  
Yin Hua Xu ◽  
Jian Bin Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Complex Structure ◽  
Evidence Theory ◽  
System Level ◽  
Sensor Failure ◽  
Advanced Technique ◽  
Electrical Control

The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

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