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

Rapid Sensor Fault Diagnosis for a Class of Nonlinear Systems via Deterministic Learning

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

Fault Identification for a Class of Nonlinear Systems of Canonical Form via Deterministic Learning

In this article, through a combination of the deterministic learning (DL) method and the adaptive high gain observer (AHGO) technology, a fault identification approach for a class of nonlinear systems in canonical form is proposed. By using the DL method, the partial persistent excitation condition of the identification system is satisfied, and then, the AHGO technology is exploited to estimate the states and the neural network weights simultaneously. To analyze the convergence of the proposed method, we first analyze the uniformed completely observability (UCO) property of the linear part of the nonlinear identification system. Then, by using the Lipschitz property of the nonlinear item and the Bellman-Gronwall lemma, we show that the UCO property of the nonlinear identification system is depended on the UCO property of the linear part when the observer gain is chosen large. Therefore, by using the UCO property of the nonlinear identification system and the Lyapunov stability theorem, the convergence of the proposed learning observer is proven. The attraction of this article is based on the analysis of the UCO property of the identification system, and the convergence of the proposed learning observer can be directly proven. The simulation example is given to demonstrate the effectiveness of the proposed method.

A Scalable Algorithm for Identifying Multiple-Sensor Faults Using Disentangled RNNs

The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant, and sustainable operations of modern industrial processing systems. The increasing complexity of such systems brings, however, new challenges for sensor fault detection and sensor fault isolation (SFD-SFI). One of the key enablers for any SFD-SFI method is analytical redundancy, which is provided by an analytical model of sensor observations derived from first principles or identified from historical data. As defective sensors generate measurements that are inconsistent with their expected behavior as defined by the model, SFD amounts to the generation and monitoring of residuals between sensor observations and model predictions. In this article, we introduce a disentangled recurrent neural network (RNN) with the objective to cope with the smearing-out effect, i.e., where the propagation of a sensor fault to nonfaulty sensor results in large and misleading residuals. The introduction of a probabilistic model for the residual generation allows us to develop a novel procedure for the identification of the faulty sensors. The computational complexity of the proposed algorithm is linear in the number of sensors as opposed to the combinatorial nature of the SFI problem. Finally, we empirically verify the performance of the proposed SFD-SFI architecture using a real data set collected at a petrochemical plant.

Facilitating Autonomous Systems with AI-Based Fault Tolerance and Computational Resource Economy

Proposed is the facilitation of fault-tolerant capability in autonomous systems with particular consideration of low computational complexity and system interface devices (sensor/actuator) performance. Traditionally model-based fault-tolerant/detection units for multiple sensor faults in automation require a bank of estimators, normally Kalman-based ones. An AI-based control framework enabling low computational power fault tolerance is presented. Contrary to the bank-of-estimators approach, the proposed framework exhibits a single unit for multiple actuator/sensor fault detection. The efficacy of the proposed scheme is shown via rigorous analysis for several sensor fault scenarios for an electro-magnetic suspension testbed.

Nonlinear robust fault diagnosis of power plant gas turbine using Monte Carlo-based adaptive threshold approach

This paper addresses the robust fault diagnosis of power plant gas turbine as an uncertain nonlinear system using a new adaptive threshold method. In order to determine the bounds of the adaptive threshold and to identify neural network thresholds modelling, an approach based on Monte Carlo simulation is employed. To evaluate the performance of the proposed fault detection method, a fault sensitivity analysis is provided. In addition, the neural network-based estimators are considered to estimate the magnitude of faults according to the values of residuals. The proposed fault diagnosis system is evaluated during different scenarios. The obtained results indicate the high sensitivity, accuracy, and robustness of the proposed method for fault detection and isolation in the nonlinear uncertain systems, even in dealing with small faults.

Adaptive Reliable $H_\infty $ Static Output Feedback Control Against Markovian Jumping Sensor Failures

This paper investigates the adaptive static output feedback (SOF) control problem for continuous-time linear systems with stochastic sensor failures. A multi-Markovian variable is introduced to denote the failure scaling factors for each sensor. Different from the existing results, the failure parameters are stochastically jumping and their bounds of are unknown. An adaptive reliable SOF control method is proposed, where the controller parameters are updated automatically to compensate for the failure effects on systems. A novel cubic absolute Lyapunov function is proposed to design adaptive laws only using measured output with sensor failures, and the convergence of jumping adaptive parameters is ensured by a trajectory initialization approach. The resultant designs can guarantee the asymptotic stability with an adaptive performance of closed-loop systems regardless of sensor failures. Finally, the simulation results on the "Raptor-90" helicopter are given to show the effectiveness of the proposed approaches.

Sensor Compromise Detection in Multiple-Target Tracking Systems

Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision. Other concerns include the introduction of false targets, which would result in a waste of computational and material resources, depending on the application. In this work, we study the problem of detecting compromised or faulty sensors in a multiple-target tracker, starting with the single-sensor case and then considering the multiple-sensor scenario. We propose an algorithm to detect a variety of attacks in the multiple-sensor case, via the application of finite set statistics (FISST), one-class classifiers and hypothesis testing using nonparametric techniques.

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.

An Integrated Learning and Filtering Approach for Fault Diagnosis of a Class of Nonlinear Dynamical Systems

This paper develops an integrated filtering and adaptive approximation-based approach for fault diagnosis of process and sensor faults in a class of continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques to derive tight detection thresholds, which is accomplished in two ways: 1) by learning the modeling uncertainty through adaptive approximation methods and 2) by using filtering for dampening measurement noise. Upon the detection of a fault, two estimation models, one for process and the other for sensor faults, are initiated in order to identify the type of fault. Each estimation model utilizes learning to estimate the potential fault that has occurred, and adaptive isolation thresholds for each estimation model are designed. The fault type is deduced based on an exclusion-based logic, and fault detectability and identification conditions are rigorously derived, characterizing quantitatively the class of faults that can be detected and identified by the proposed scheme. Finally, simulation results are used to demonstrate the effectiveness of the proposed approach.

An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks

Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.