Adaptive Residual Generator Research Articles

Model-Free Fault Detection Based on Performance Residual for Feedback Control Systems

This brief proposes a control performance-oriented fault detection (PFD) strategy for closed-loop linear time-invariant systems. Firstly, the performance index with the summation of quadratic form over the infinite time interval is investigated in virtue of a data-driven residual-centered closed-loop model, which can be constructed utilizing input and output data instead of system models. Then the adaptive residual generator is presented to update the parameters of the data-driven model in case of system changes caused by anomalies, serving the information of the faulty plant for PFD. Further, the performance residual, which represents the performance degradation, is defined based on the Bellman equation. By embedding the predesigned controller parameters in the performance residual generator, the performance degradation caused by system faults and controller mismatches can be detected. Additionally, the proposed scheme can be applied in various operating points without revising the detector parameters. Finally, a randomized algorithm-aided threshold design is developed to handle unknown input. Case studies on a circuit model are conducted to show the superiority of PFD.

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

Abstract. It is a great challenge to apply a diagnostic system for sensor fault detection to engine test beds. The main problem is that such test beds involve frequent configuration changes or a change in the entire test engine. Therefore, the diagnostic system must be highly adaptable to different types of test engines. This paper presents a diagnostic method consisting of the following steps: residual generation, fault detection and fault isolation. As adaptability can be achieved with residual generation, the focus is on this step. The modular toolbox-based approach combines physics-based and data-driven modeling concepts and, thus, enables highly flexible application to various types of engine test beds. Adaptability and fault detection quality are validated using measurement data from a single-cylinder research engine and a multicylinder diesel engine.

Projection-aided Adaptive Residual Generator for Disturbance-decoupled Process Monitoring

Adaptive residual generator is a powerful technique which delivers a simultaneous co-estimation of the state variables and unknown parameters with stability and convergence guarantee. This paper reports our recent study on a projection-aided adaptive residual generator design for disturbance-decoupled process monitoring. The core of the study is the implementation of a projection on the orthogonal complement of disturbance subspace, which results in a robust state and parameter estimation against unknown disturbance, furthermore, accurate process monitoring can be achieved.

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

In this paper, two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on the data-driven design of the proposed fault-tolerant architecture whose core is an observer/residual generator based realization of the Youla parameterization of all stabilization controllers, FTC is achieved by an adaptive residual generator for the online identification of the fault diagnosis relevant vectors, and an iterative optimization method for system performance enhancement. The performance and effectiveness of the proposed schemes are demonstrated through the TE benchmark model.

Data‐Driven Adaptive Observer for Fault Diagnosis

This paper presents an approach for data‐driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory‐scale three‐tank system is finally utilized. It shows that the proposed data‐driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large‐scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

An approach to data-driven adaptive residual generator design and implementation

This paper addresses data-driven design and implementation of adaptive observer based residual generators for discrete-time systems. The basic idea behind this study is the application of an one-to-one mapping between a parity vector and the solution of Luenberger equations and the data-driven identification of parity space. For the realization of the adaptive residual generation, standard adaptive technique is applied. The proposed approach is demonstrated on the laboratory three-tank-system.

Adaptive fault tolerant control with adaptive residual generation

This paper presents an adaptive predictive fault tolerant controller and an adaptive residual generator in an integrated scheme applied to a non-linear process. A case study on the steam super-heating subsystem of a boiler, represented by a detailed non-linear physical based model, is described. The faults considered are additive, step like and occur on the actuator. The controller is a GPC, rendered adaptive by a RLS algorithm, and is designed as in the "classical" way, with the addition of the actuator fault estimate information. The adaptive residual generator is based on the parity space method, rendered adaptive through on-line estimation of the model parameters allowing residuals to stay near zero for different amplitudes of the set-point. Simulation results of this integrated scheme are presented highlighting the performance advantages of the adaptive residuals vs. the standard residuals and discussing the proposed modified GPC controller vs. the "classical" GPC.

FDI with Adaptive Residual Generation Applied to a DC-Servo

When parity equations are designed ad hoc., the low frequency gain of the fault-to-residual transfer function can become low or even zero which is highly undesirable. As a solution, a systematic design procedure, utilizing the freedom available in residual generator design, is proposed. The method is called residual shaping and guarantees reasonable performance. It is used together with RLS to form an adaptive residual generator, which is important to handle e.g. aging. The RLS algorithm also makes it possible to differentiate between additive and multiplicative faults. The proposed FDI system is evaluated on a DC-servo where the strengths of the system is illustrated.