Haris M. Khalid

Expectation maximization approach to data-based fault diagnostics

The data-based fault detection and isolation (DBFDI) process becomes more potentially challenging if the faulty component of the system causes partial loss of data. In this paper, we present an iterative approach to DBFDI that is capable of recovering the model and detecting the fault pertaining to that particular cause of the model loss. The developed method is an expectation-maximization (EM) based on forward-backward Kalman filtering. We test the method on a rotational drive-based electro-hydraulic system using various fault scenarios. It is established that the developed method retrieves the critical information about presence or absence of a fault from partial data-model with minimum time-delay and provides accurate unfolding-in-time of the finer details of the fault, thereby completing the picture of fault detection and estimation of the system under test. This in turn is completed by the fault diagnostic model for fault isolation. The obtained experimental results indicate that the developed method is capable to correctly identify various faults, and then estimating the lost information.

Model prediction-based approach to fault-tolerant control with applications

Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions.

Robust Control of a Closed-Loop Identified System with Parametric/Model Uncertainties and External Disturbances

This paper deals with the closed-loop identification of a two-tank process used in industry. The identified model is then utilized to develop robust controllers i.e. H E; and sliding mode controllers. It is shown that these controllers guarantee a satisfactory performance in the face of both model/parametric uncertainties and external disturbances. The designed controllers have been successfully tested through extensive simulation. In addition, this paper shows that the designed robust controllers far outperform traditional controllers such as P, PI, and PID, in the face of parametric model uncertainties and the effects of external disturbances. The successful use of the designed robust controllers encourages their extension to other physical systems.

Model order selection criterion with application to physical systems

In this paper, it is shown that poles of the discrete-time equivalent of continuous system will lie in the right-half of the complex plane if the sampling rate is chosen to be more than twice the Nyquist rate. This new criterion allows for a quick and reliable separation between the systems poles and any extraneous poles emanating from a variety of artifacts such as high frequency noise and nonlinearities in the system. The system is identified for different model orders and only those for which the poles lie in the right-half plane are chosen. Then using a conventional scheme such as Akaike Information Criterion (AIC), the correct order is chosen from the selected model set. The proposed model order selection criterion has been evaluated on a physical system.

Non-linear constrained optimal control problem: A hybrid PSO-GA-Based discrete augmented lagrangian approach

This work deals with the optimal control problem which has been proposed to solve using the discrete augmented lagrangian based non-linear programming approach. It is shown that this technique guarantee a satisfactory performance in the face of both optimality by minimizing the energy and maximizing the output. Later on, the optimization has been more effective by using PSO-GA-Based Optimization to achieve the optimal value of Lagrange Multipliers and required dynamic parameters and optimally controlling the dynamics. The designed scheme has been successfully tested through extensive simulation. The successful use of the proposed scheme encourages their extension to other physical systems. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupled-tank system.

Fault detection and classification using Kalman filter and genetic neuro-fuzzy systems

In this paper, an efficient scheme to detect the unprecedented changes in system reliability and find the failed component state by classifying the faults is proposed using kalman filter and hybrid neuro-fuzzy computing techniques. A fault is detected whenever the moving average of the Kalman filter residual exceeds a threshold value. The fault classification has been made effective by implementing a hybrid Genetic Adaptive Neuro-Fuzzy Inference System (GANFIS). By doing so, the critical information about the presence or absence of a fault is gained in the shortest possible time, with not only confirmation of the findings but also an accurate unfolding-in-time of the finer details of the fault, thus completing the overall fault diagnosis picture of the system under test. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupledtank system.

Data-driven fault detection filter design for time-delay systems

In this paper, robust fault detection (FD) problems for time-delay LTI systems with unknown inputs are studied. This paper is proposed to evaluate the robustness as well as sensitivity of residual signals to the unknown inputs as well as to the faults in terms of L2. First, the weighting matrix is selected for an appropriate design of filter, then fault detection filter design with Lyapunov-Krasovskii function (LKF) is designed with time delay. The main results include the detailed derivation of these steps followed by its implementation on an open-loop time-delay system for chemical reactor example.

A novel scheme to estimate the model order of physical systems

A method to estimate the model order of physical systems is proposed. It is assumed that the region where the poles of a physical system are located is known. This a priori knowledge is generally obtained from the physical laws governing the physical system. Various model orders are chosen using the conventional model selection criteria. For each order, a model of the physical system is identified. If the poles of the identified model are not located in this region, this model is assumed to include extraneous poles and is rejected. This process is continued till the estimated model does not include extraneous poles. The proposed scheme is extensively evaluated on physical systems and the results are compared with those of the conventional schemes such as Akaike Information Criterion (AIC).

Fusion of model-based and model-free approaches to leakage diagnosis

A diagnosis scheme for incipient leakage faults is proposed using a combination of two entirely different approaches, namely model-free and model-based ones, ensuring thereby that critical information about the presence or absence of leakage is monitored in the shortest possible time and the complete status regarding the leakage is unfolded in time. Model-free approaches include limit checks & knowledge-based analysis, while model-based approaches include the extended Kaiman filter and a parameter identification scheme. The knowledge-based analysis indicates quickly a possible onset of leakage, the Kaiman filter detects the presence/absence and finally the identification scheme isolates the detected fault. Further, the whole combined helps in designing an effective preventive maintenance strategy. The proposed scheme is evaluated on a physical fluid system exemplified by a bench-marked two-tank system.