Experimental investigation of a real-time singularity-based fault diagnosis method for five-phase PMSG-based tidal current applications

This paper presents a novel real-time singularity-based fault diagnosis method for tidal current applications, specifically utilizing a five-phase permanent magnet synchronous generator with trapezoidal back electromotive forces. The proposed method incorporates an innovative orthogonal signal generator through a second-order filter, enabling the extraction of detectable singularity signatures from phase current signals. The principle of the method is elucidated through step-by-step design procedures, outlining the indicator enhancement approach and adaptive thresholds employed for enhanced robustness and adaptability. Fault detection is performed based on the improved fault indicators and an adaptive threshold law, followed by immediate fault localization that is achieved via twice average operations of the phase currents. To demonstrate the effectiveness and efficiency of the proposed method, a comparative study is carried out with a classical mean current vector-based fault diagnosis method. A small-scale experimental platform emulating a tidal current application is established for a comprehensive evaluation of both methods. The experimental results highlight the superior fault diagnosis performance of the proposed method, particularly in detecting single and multiple open circuit faults in phases or switches, while exhibiting enhanced robustness against variations in torque and speed. The simplicity of implementation and rapid detection mechanism are principal merits for the proposed method.

Adaptive Fuzzy Tracking Control for Strict-Feedback Markov Jumping Nonlinear Systems With Actuator Failures and Unmodeled Dynamics

In this paper, an adaptive fuzzy tracking controller is developed for a class of strict-feedback Markovian jumping systems subjected to multisource uncertainties. The unpredictable actuator failures, the unknown nonlinearities, and the unmodeled dynamics are simultaneously taken into consideration, which evolve according to the Markov chain. It is noted that the elements in the transition rate matrix of the Markov chain are not fully available. In virtue of the norm estimation approach, the challenges caused by the complex multiple uncertainties and actuator failures are effectively handled. Furthermore, to compensate for the unavailable switching nonlinearities, the fuzzy logic systems are employed as online approximators. As a result, a novel adaptive fuzzy fault-tolerant tracking control structure is constructed. The sufficient condition is provided to guarantee that the studied system is stochastically stable. Finally, a number of illustrative examples are employed to demonstrate the effectiveness of the proposed methodology.

A bounded-error approach to actuator fault diagnosis and remaining useful life prognosis of Takagi-Sugeno fuzzy systems

The paper presents an actuator fault diagnosis and prognosis scheme for Takagi-Sugeno fuzzy system. It overcomes the drawbacks of the approaches presented in the relevant literature by taking into consideration the problem of the robustness. The proposed approach consists of an actuator fault estimator which provides knowledge about faults with their uncertainty intervals. Thus, the fault detection mechanism relies on an acceptable threshold imposed on these intervals. Once the fault is detected, the remaining useful life of an actuator is predicted what is expressed in the so-called time-to-failure. Similarly as in the case of fault estimation, the remaining useful life is provided as an uncertainty interval. It should be notated that previously reported approaches to Takagi-Sugeno systems cannot either provide the uncertainty of fault estimates for remaining useful life. To tackle the development of the above schemes the theory of quadratic boundedness and feasible parameter set-based estimation are employed. The final part of the paper portrays a case study which clearly exhibits the performance of the proposed approach.

A New Sensor Fault Isolation Method for T-S Fuzzy Systems

This paper is concerned with the fault isolation problem for T-S fuzzy systems with sensor faults. With the help of a set theoretic description of T-S fuzzy models, a new fault isolation scheme is proposed. It consists of a set of fuzzy observers and each of them corresponds to a specified sensor, where the antecedent and consequent parts of the observer are independent on the sensor output. Different from the existing approaches, the premise variables, which do not depend on the specified sensor output but depend on the other sensor outputs, are used in the proposed observer, which has the potential to lead to a better fault isolation performance. In the end, an example is given to show the effectiveness of the fault isolation method.

Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble

For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.

Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems

This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.

An online outlier identification and removal scheme for improving fault detection performance

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

Adaptive approximation for multiple sensor fault detection and isolation of nonlinear uncertain systems

This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults.