A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

An Online Approximator-Based Fault Detection Framework for Nonlinear Discrete-Time Systems

In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results

A Model Based Fault Detection Scheme for Nonlinear Multivariable Discrete-Time Systems

In this paper, a novel robust scheme is developed for detecting faults in nonlinear discrete time multi-input and multi-output systems in contrast with the available schemes that are developed in continuous-time. Both state and output faults are addressed by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. By using nonlinear estimation techniques, the discrete-time system is monitored online. Once a fault is detected, its dynamics are characterized using an online approximator. A stable parameter update law is developed for the online approximator scheme in discrete-time. The robustness, sensitivity, and performance of the fault detection scheme are demonstrated mathematically. Finally, a Continuous Stir Tank Reactor (CSTR) is used as a simulation example to illustrate the performance of the fault detection scheme

A Model Based Fault Detection and Prognostic Scheme for Uncertain Nonlinear Discrete-Time Systems

A new fault detection and prognostics (FDP) framework is introduced for uncertain nonlinear discrete time system by using a discrete-time nonlinear estimator which consists of an online approximator. A fault is detected by monitoring the deviation of the system output with that of the estimator output. Prior to the occurrence of the fault, this online approximator learns the system uncertainty. In the event of a fault, the online approximator learns both the system uncertainty and the fault dynamics. A stable parameter update law in discrete-time is developed to tune the parameters of the online approximator. This update law is also used to determine time to failure (TTF) for prognostics. Finally a fourth order translational oscillator with rotating actuator (TORA) system is used to demonstrate the fault detection while a mass damper system is used for demonstrating the prognostics scheme

A Neural Network Model based Approach to Detect Seal and Impeller Failures in Centrifugal Pump

With the increased complexity of today\u27s industrial processes, maintaining equipment by preventing unscheduled downtime using monitoring hardware is a key challenge. Industrial statistics indicate that seal and impeller failures are predominant failure modes in centrifugal pumps and they are not adequately addressed in the literature. In this paper, a neural network (NN) based Nonlinear Autoregressive Moving Average with Exogenous input (NARMAX) model is used to develop fault detection scheme for detecting seal and impeller failures in centrifugal pumps. A rigorous methodology of detecting failures at the incipient stage is introduced. First a nonlinear relationship among the monitored parameters (inlet and outlet pressure, outlet flow, inlet and outlet temperature, and acceleration) where the previous values of the indicative parameters are used as inputs to the NARMAX model and the output being the value at the current instance is captured. The NARMAX modeled outputs are compared with the actual measured values in order to generate residuals. By choosing a suitable threshold, we could minimize false and missed alarms. Mathematical procedure for selection of threshold is derived in this paper. Along with the NARMAX model, an online approximator is used in the fault detection scheme for understanding the faults in the system. Experiments on the centrifugal pump seal and impeller failures were conducted by using a laboratory test bed. Experimental results show that the proposed fault detection scheme is able to successfully detect failures