Observer-based Fault Detection Approach Research Articles

Observer-Based Fault Detection With Fuzzy Variable Gains and Its Application to Industrial Servo System

In this paper, an adaptive high-accurate observer-based fault detection approach for industrial applications is proposed. The proposed fault detection algorithm employs a fuzzy logic-based approach with the objective of finding the appropriate observer gains that could cope with the different working conditions. The flexibility and adaptability represent the main objectives of the proposed observer. This work is interested in proposing an observer with fuzzy variable gains for a general nonlinear system. Furthermore, a linear model has been built to facilitate the accomplishment of the fault detection of the industrial servo system by using the proposed observer. In order to evaluate the proposed approach, eleven realistic sensor fault scenarios are created under varying conditions: fault parameters (e.g., multiple fault profiles, location, and magnitudes), unknown inputs (e.g., disturbers and sensor noises) for performance testing. Also, a scoring algorithm has been implemented, to evaluate the classification ability of the algorithm and the early fault detection ability. The experimental results demonstrate the effectiveness of the proposed observer approach in detecting sensor faults in the industrial servo systems, with 88.8% classification accuracy. Furthermore, the obtained results confirm the proposed algorithm superiority when compared to classical Luenberger observer with constant gains.

A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations

This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi–Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

Weighted Fuzzy Observer-Based Fault Detection Approach for Discrete-Time Nonlinear Systems via Piecewise-Fuzzy Lyapunov Functions

The main focus of this paper is on the analysis and integrated design of $\mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into this FD framework, the existence condition is introduced first. Then, an integrated design of $\mathcal {L}_2$ observer-based FD approach is realized by solving the proposed existence condition with the aid of Takagi–Sugeno fuzzy dynamic modeling technique and piecewise-fuzzy Lyapunov functions. Most importantly, a weighted piecewise-fuzzy observer-based residual generator is proposed, aiming at achieving an optimal integration of residual evaluation and threshold computation into FD systems. The core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors. In comparison with the standard norm-based fuzzy observer-based FD methods, the proposed scheme may lead to a significant improvement of the FD performance. In the end, the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control systems firstly, and fault detection and diagnosis (FDD) is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems (LNCSs), but nonlinear networked control systems (NNCSs) are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

Takagi-Sugeno Sliding Mode Observer Design for Fault Diagnosis in Pitch Control Systems of Wind Turbines1

The performance and reliability of the pitch control systems significantly influences the functional safety of wind turbines. In this paper an observer-based fault detection approach for a class of nonlinear systems, which can be modeled by Takagi-Sugeno (TS) fuzzy models, is presented and applied to electro mechanical pitch drive systems of wind turbines. To achieve robustness we propose a TS fuzzy observers combined with sliding mode that deals with unmeasurable premise variables, bounded uncertainties in the plant and allows fault estimation based on an equivalent output error injection approach. The stability of the error dynamics is ensured by linear matrix inequality (LMI) conditions. Based on a derived physical model of the pitch drive system and the unknown but bounded wind loads the robust observer is systematically designed using the TS system form of the plant. Simulation results demonstrate the effectiveness of the proposed scheme.

Fault detection for stator inter-turn short circuit in doubly fed induction generators using adaptive observers

This paper presents an observer-based fault detection approach for inter-turn stator short circuit fault in doubly fed induction generators (DFIG). A state-space model is developed, which can represent both the healthy and faulty conditions. Based on this model, two different adaptive observers: asymptotic adaptive observer and exponential adaptive observer are constructed, which are both able to estimate the fraction of short circuited winding online. The case when the rotor speed is immeasurable is also considered in this paper. The proposed exponential adaptive observer is reformulated into a new observer, which is capable of estimating the fault level and the rotor speed simultaneously. The simulation results demonstrate that this approach has effectiveness on fault detection under both steady state operation and transient state operation. It has strong robustness against the speed and load variations. This advantage has significance on fault detection of wind turbine DFIGs, since the DFIGs are predominantly under varying speed operation.

Fault Detection of Networked Control Systems with Packet Dropout

Fault detection of networked control systems with packet dropouts in both sensor-to-controller link and controller-to-actuator link is discussed in this paper. Two types of packet dropouts are considered. One is packet dropout characterized by a Bernoulli process, the other is characterized by a Markov chain. According to the system configuration, packet dropout in the sensor-to-controller link is known to the fault detection system, therefore a time varying parity space based residual generator which is fully decoupled from this influence is designed. Robustness to packet dropout in the controller-to-actuator link which is unknown is achieved by an adaptive threshold. Due to the known probability properties of the residual signal in parity space approach, upper bounds of false alarm rates of the designed thresholds are also given, which are very difficult to obtain in existent observer based fault detection approaches of networked control systems with packet dropout. It is verified that the proposed fault detection approach can also be realized by diagnostic observers by employing the relationship between parity vectors and observers.

Comparison of Observer-Based Fault Detection Approaches

The objective of this work is to gain an insight into interconnections of different observer-based fault detection and isolation (FDI) approaches and to compare them from different points of view. Some useful remarks on system models, residual generator construction, solution and computation of FDI problems are made.