Soft computing and signal processing based active fault tolerant control for benchmark process

Abstract

Ideally, when faults happen, the closed-loop system should be capable of maintaining its present operation. This leads to the recently studied area of fault-tolerant control (FTC). This paper addresses soft computing and signal processing based active FTC for benchmark process. Design of FTC has three levels: Level 1 comprises a traditional control loop with sensor and actuator interface and the controller. Level 2 comprises the functions of online fault detection and identification. Level 3 comprises the supervisor functionality. Online fault detection and identification has signal processing module, feature extraction module, feature cluster module and fault decision module. Wavelet analysis has been used for signal processing module. In the feature extraction module, feature vector of the sensor faults has been constructed using wavelet analysis, sliding window, absolute maximum value changing ratio and variance changing ratio as a statistical analysis. For the feature cluster module, the self-organizing map (SOM), which is a subtype of artificial neural network has been applied as a classifier of the feature vector. As a benchmark process three-tank system has been used. Control of the three-tank system is provided by fuzzy logic controller. Faults are applied to three level sensors. Sensor faults represent incorrect reading from the sensors that the system is equipped with. When a particular fault occurs in the system, a suitable control scheme has been selected on-line by supervisor functionality to maintain the closed-loop performance of the system. Active FTC has been achieved by switch mode control using fuzzy logic controller. Simulation results show that benchmark process has maintained acceptable performance with FTC for the sensor faults. As a result, when the system has sensor faults soft computing and signal processing based FTC helps for the best performance of the system.

Isolating Non-predefined Sensor Faults by Using Farthest First Traversal Algorithm

“…The aim is to keep the level of tank-2 ( H 2 ) stable by controlling the levels of tank-1 ( H 1 ) and tank-3 ( H 3 ), which interact with tank-2. Using the balance equations for the three-tank system, the model setup is as follows: normald H 1 normald t = prefix− k 1 S 1 g H 1 ( t ) − k 12 S 1 nobreak0em0.25em⁡ sgn ( H 1 false( t false) − H 2 false( t false) ) × g | H 1 false( t false) − H 2 false( t false) | + Q in , 1 S 1 normald H 2 normald t = prefix− k 2 S 2 g H 2 ( t ) + …”

“…Isolation of Predefined Sensor Faults. In the threetank experiment, the system has been working offline for normal and seven predefined faulty conditions 15 to create a knowledge base. Different types of artificial faults were applied on the system; these are additive, multiplicative, and stuck type sensor faults as given in Table 1.…”