A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms

In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimisation problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of WF fault diagnosis

A Comparison of Inverse Simulation-Based Fault Detection in a Simple Robotic Rover with a Traditional Model-Based Method

Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing \textit{input} residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances

A Fault Tolerant System for an Integrated Avionics Sensor Configuration

An aircraft sensor fault tolerant system methodology for the Transport Systems Research Vehicle in a Microwave Landing System (MLS) environment is described. The fault tolerant system provides reliable estimates in the presence of possible failures both in ground-based navigation aids, and in on-board flight control and inertial sensors. Sensor failures are identified by utilizing the analytic relationships between the various sensors arising from the aircraft point mass equations of motion. The estimation and failure detection performance of the software implementation (called FINDS) of the developed system was analyzed on a nonlinear digital simulation of the research aircraft. Simulation results showing the detection performance of FINDS, using a dual redundant sensor compliment, are presented for bias, hardover, null, ramp, increased noise and scale factor failures. In general, the results show that FINDS can distinguish between normal operating sensor errors and failures while providing an excellent detection speed for bias failures in the MLS, indicated airspeed, attitude and radar altimeter sensors

Observer-based Anomaly Diagnosis and Mitigation for Cyber-Physical Systems

Cyber-Physical Systems (CPS) seamlessly integrate computational devices, communication networks, and physical processes. The performance and functionality of many critical infrastructures such as power, traffic, and health-care networks and smart cities rely on advances in CPS. However, higher connectivity increases the vulnerability of CPS because it exposes them to threats from both the cyber domain and the physical domain. An attack or a fault within the cyber or physical domain can subsequently affect the cyber domain, the physical domain, or both, resulting in anomalies. An attack or a fault on CPS can have serious or even lethal consequences. Traditional anomaly diagnosis techniques mainly focus on cyber-to-cyber or physical-to-physical interactions. However, in practice they can often be subverted in the face of cross-domain attacks or faults. In summary, the safety and reliability of CPS become more and more crucial every day and existing techniques to diagnose or mitigate CPS attacks and faults are not sufficient to eliminate vulnerability. The motivation of this dissertation is to enhance anomaly diagnosis and mitigation for CPS, covering physical-to-physical and cyber-to-physical attacks or faults. With the advantage of dealing with system uncertainties and providing system state estimation, observer-based anomaly diagnosis is of great interest. The first task is to design a multiple observers framework to diagnose sensor anomalies for continuous systems. Since CPS contain both continuous and discrete variables, CPS are modeled as hybrid systems. Utilizing the relationship between the continuous and discrete variables, a conflict-driven hybrid observer-based anomaly detection method is proposed, which checks for conflicts between the continuous and discrete variables to detect anomalies. Lastly, the observer design for hybrid systems is improved to enable observer-based anomaly diagnosis for a wider class of hybrid systems. The novel observer-based anomaly diagnosis and mitigation approaches introduced in this dissertation can not only diagnose anomalies caused by traditional faults, but also anomalies caused by sophisticated attacks. This research work can benefit the overall security of critical infrastructures, preventing disastrous consequences and reducing economic loss. The effectiveness of the proposed approaches is demonstrated mathematically and illustrated through applications to various simulated systems, including a suspension system, the Positive Train Control system and a microgrid system.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147576/1/zhengwa_1.pd

Electromechanical actuator bearing fault detection using empirically extracted features

Model parameter estimation when coupled with Principal Component Analysis (PCA) and Bayesian classification techniques form a potentially effective fault detection scheme for Electromechanical Actuators (EMAs). This work uses parameter estimation algorithms based on linear system identification methods, derives a novel feature extraction algorithm based on PCA and analyzes its performance through simulations and experiments. A Bayesian classifier is used to create well defined EMA health classes from the extracted features. Research contributions on fault detection in EMAs are significant because EMA faults and their detection are not yet well understood. Potential future applications - such as in primary flight control actuation in aircraft - require that quality fault detection systems be in place. Therefore, fault detection of EMAs is a vast area of ongoing research where highly capable solutions are gradually becoming available. Prior work in parameter estimation methods for feature extraction in DC motor drives - which includes EMAs - are amongst those available. While PCA is a popular feature extraction solution in a number of frequency-based fault detection approaches, the use of PCA for feature extraction from model parameters for detecting bearing faults in EMAs has not been previously reported. In this work, a linear difference model is applied to the EMA system data such that fault information is distributed amongst the estimated model parameters. A direct comparison of the parameter estimates from healthy and degraded systems offers little insight into health conditions because of the weak effects of faults on the signal data. However, the application of PCA to uncorrelate the linearly correlated model parameters while minimizing the loss of variance information from the data effectively brings out fault information. The present algorithm is successfully applied to data collected from a Moog MaxForce EMA. The results are consistent and display effective fault detection characteristics, making the developed approach a suitable starting point for future work

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\ud 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\ud 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

On Reachable Sets of Hidden CPS Sensor Attacks

For given system dynamics, observer structure, and observer-based fault/attack detection procedure, we provide mathematical tools -- in terms of Linear Matrix Inequalities (LMIs) -- for computing outer ellipsoidal bounds on the set of estimation errors that attacks can induce while maintaining the alarm rate of the detector equal to its attack-free false alarm rate. We refer to these sets to as hidden reachable sets. The obtained ellipsoidal bounds on hidden reachable sets quantify the attacker's potential impact when it is constrained to stay hidden from the detector. We provide tools for minimizing the volume of these ellipsoidal bounds (minimizing thus the reachable sets) by redesigning the observer gains. Simulation results are presented to illustrate the performance of our tools