Multiple Sensor Faults Research Articles

Sensor Fault Diagnosis and Fault Tolerant Control for Automated Guided Forklift

For the problem of multiple sensor faults in the automated guided forklift (AGF), an equivalent model with system faults and disturbances is established. To detect multiple sensor faults in the AGF, a sliding mode observer (SMO) is proposed. It introduces a fault estimation algorithm that is designed by the fault residual and the residual is only sensitive to sensor faults which means that the SMO is robust to unknown input disturbances. On the other side, it can also judge the faulty sensor according to the feature vector of different sensors. To judge the type of sensor faults accurately, a mathematical model of sensor fault characteristics is established and it can provide a foundation for choosing appropriate fault-tolerant output compensation measures. Then an active fault-tolerant control method based on state feedback is proposed. It can restore the control system to normal and maintain the stability of the control system. Finally, experiments are given to verify the effectiveness of the proposed fault-tolerant control strategy.

A Fault-Tolerant Control Approach Based on Image Processing Applied to Three-Tank System

This paper presents an innovative and practical solution for fault-tolerant control of industrial systems based on hybrid redundant measurements. The paper proposes a fault-tolerant control scheme against single and multiple sensor faults that can occur in typical tank-based industrial applications. A robust fault detection filter based on $H_{-} /H_{\infty }$ index is designed to generate a residual signal that is robust against disturbance and sensitive to the fault, simultaneously. It also integrates camera information and image processing techniques in the control scheme to get level-measurement redundancy. The proposed scheme was tested using the well-known three-tank benchmark, with both offline and online computation. Results showed that the proposed scheme tolerated the sensor faults and preserved the correct operation of the tank levels in both single and multiple-sensor faults, without changing the original structure of the controller.

Event-triggered L1 filtering for uncertain networked control systems with multiple sensor fault modes

Considering the influence of sensor fault modes on the system performance in communication network, under the discrete event-triggered communication scheme (DETCS), the problems of the robust [Formula: see text] filtering for a class of uncertain networked control systems (NCSs) with multiple sensor fault modes and persistent and amplitude-bounded disturbance constraints are investigated. A set of stochastic variables are adopted to describe the sensor faults, the filtering error system is established, which characterizes the effects of sensor faults and DETCS. By constructing an appropriate delay-dependent Lyapunov-Krasovskii functional, new results on stability and robust [Formula: see text] performance are proposed for the filtering error system according to Lyapunov theory and the integral inequality method, and the co-design method for gaining the desired [Formula: see text] filter parameters and event-triggering parameters is given in terms of linear matrix inequalities (LMIs). We notice that with the structrue of Lyapunov-Krasovskii functional which considers the piecewise-linear sawtooth structure characteristic of transmission delay, a less conservative result is obtained. Finally, three examples are provided to illustrate the feasibility of the proposed method.

Adaptive fuzzy fault‐tolerant control for non‐linear systems under actuator and sensor faults: the practical fixed‐time stability

This study investigates the adaptive fault-tolerate control for strict-feedback non-linear system, in which actuator and sensor faults may happen simultaneously. Moreover, the additive and multiplicative faults are considered, which cover the bias, drift, loss of accuracy, and loss of effectiveness faults. Under the multiple sensor faults, a modified backstepping technique involving in the faulted states is proposed by utilising the fuzzy approximation to the unknown non-linear functions. Later, the adaptive fuzzy fault-tolerant control strategy is designed to ensure the practical fixed-time stability of the closed-loop system. Meanwhile, the convergence time is independent of the initial states of the system, and the control performance can be ensured for arbitrary bounded initial states in this study. Finally, the Buck converter circuit is employed to demonstrate the effectiveness of the proposed fault-tolerant control scheme.

Observer canonical form based robust fault detection and estimation for hyperbolic spatiotemporal dynamic systems

In this study, the authors propose a novel state and a fault estimation scheme for a class of hyperbolic spatiotemporal dynamic systems in the presence of unknown external disturbance. They consider the occurrence of multiplicative actuator and sensor faults. In detail, they consider two cases of fault occurrence: (i) only one type (actuator or sensor) of fault happens; (ii) two types of faults occur simultaneously. This study discusses the fault detectability conditions by proposing a fault detection observer. To complete the estimation problem, three difficulties arise: (i) no prior information shows the type of faults; (ii) the observer design is non-linear due to multiplication between plant signals (state or input) and unknown fault parameters; (iii) only one boundary measurement is available. They convert the original faulty plant into its observer canonical form. By proposing two filters based on the resulting observer canonical form, they develop novel parameter update laws for fault parameter estimation. With the proposed update laws, the true state of the faulty plant can be estimated by the proposed observers. By selecting appropriate Lyapunov functions, they prove that estimation error of state and fault parameters exponentially decays to an arbitrarily small neighbourhood of zero despite unknown external disturbance.

Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant

Safety is the most important indicator of a nuclear power plant. The extensive use of sensors could help operators gain more information about nuclear power plants. However, it also increases the risk of sensor failures. Therefore, it is necessary to study the multiple fault detection, isolation and reconstruction (FDIR) of sensors. For the traditional principal component analysis (PCA) model, this paper proposes two improvements. The first improvement is to propose a Corrected Reconstruction Algorithm (CRA) to improve the accuracy of the reconstruction. The traditional PCA reconstruction has lower accuracy when reconstructing multi-sensor faults. The second improvement is a cyclic PCA (CPCA) monitoring model to detect multi-sensor failures. The purpose of this is to improve the PCA model's ability to detect multiple sensor faults. Finally, the data from an actual nuclear power plant is used for modeling and verification. Simulation tests show that the CPCA model could accurately detect the different kinds of sensor faults and successfully reconstruct fault data.

Adaptive fault-tolerant control for a class of stochastic nonlinear systems with multiple sensor faults

This paper investigates the problem of adaptive fault-tolerant control for a class of single-input and single-output nonlinear Itô stochastic systems with unknown dynamics and multiple sensor faults. Due to the partial loss of effectiveness of sensors, each measured state contains an unknown time-varying fault parameter. Then, to circumvent the main obstacle caused by the coupling of unknown fault parameters and real states, a new method of fault parameters separating of stochastic nonlinear systems is proposed. Combining with the modified backstepping design techniques, an adaptive state feedback controller is constructed recursively to estimate unknown fault parameters and guarantee the stabilisation of the stochastic system. By using quartic Lyapunov functions, it is proved that all signals of the closed-loop system are bounded in probability. Finally, simulation results are given to illustrate the effectiveness of the proposed controller design method.

Towards Simultaneous Actuator and Sensor Faults Estimation for a Class of Takagi-Sugeno Fuzzy Systems: A Twin-Rotor System Application.

The paper is devoted to the problem of estimating simultaneously states, as well as actuator and sensor faults for Takagi–Sugeno systems. The proposed scheme is intended to cope with multiple sensor and actuator faults. To achieve such a goal, the original Takagi–Sugeno system is transformed into a descriptor one containing all state and fault variables within an extended state vector. Moreover, to facilitate the overall design procedure an auxiliary fault vector is introduced. In comparison to the approaches proposed in the literature, a usual restrictive assumption concerning fixed fault rate of change is removed. Finally, the robust convergence of the whole observer is guaranteed by the so-called quadratic boundedness approach which assumes that process and measurement uncertainties are unknown but bounded within an ellipsoid. The last part of the paper portrays an exemplary application concerning a nonlinear twin-rotor system.

Observer and fault‐tolerant controller design for discrete‐time multiple state‐delayed T–S fuzzy systems

This study is concerned with observer-based fault estimation (FE) and fault-tolerant controller design for a class of discrete-time Takagi–Sugeno (T–S) fuzzy systems. There exist multiple time-varying state delays, sensor and actuator faults, local non-linear dynamics and exogenous disturbances in the systems. In comparison with the existing results, the approach suggested in this study is more flexible and feasible. By means of the n-step induction FE, a novel fuzzy adaptive descriptor observer is developed to obtain the n-step error functions. Then, an active dynamic output feedback fault-tolerant controller is designed to stabilise the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function which utilises the form of linear matrix inequalities. The stability results from the observer and the controller in this study have less conservatism compared with the ones from the existence of observers and fault-tolerant controllers. At last, a simulation example is presented to demonstrate the advantages and effectiveness of the approach proposed in the study.

An Online Data-Driven Method for Simultaneous Diagnosis of IGBT and Current Sensor Fault of Three-Phase PWM Inverter in Induction Motor Drives

This article presents an online data-driven diagnosis method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in the three-phase pulsewidth modulation inverter. The fast Fourier transform (FFT) algorithm is used to extract the fault frequency spectrum features of the three-phase currents. Then, a feature selection method named ReliefF is introduced to select the most critical features by removing redundant and irrelevant features. In addition, as novel fast learning technology, a random vector functional link network is applied to learn the faulty knowledge from the historical dataset. Based on the well-learned model, the fault type and location of the converter can be accurately identified as long as the three-phase current signals are measured. Offline test results verify that the proposed method can identify the IGBT and sensor faults with an accuracy of 98.83% and outperforms the state-of-the-art learning algorithms. Moreover, the real-time hardware-in-the-loop test results show that the proposed method can successfully identify the IGBT faults and current sensor faults within 22 ms. It is robust to the dc-link voltage fluctuations, model parameters, and speed or load variations. The extensibility of the proposed method is also validated based on the test results in terms of other fault modes and drive systems.

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.

H∞ control for uncertain discrete-time MJSs with piecewise-constant transition probabilities subject to multiple intermittent sensor faults

This paper addresses the problem of H∞ control for an uncertain discrete-time Markov jump system subject to multiple intermittent sensor faults. The intermittent fault considered in this paper is formulated as a probabilistic event and is additive to the normal signal of a sensor in systems. The occurrence of the intermittent fault is assumed to satisfy the Bernoulli random binary distribution which takes values of either 0 or 1. The nonlinearity is related to time-varying delay and satisfies a sector-bounded condition. The Markov chain is considered as finite piecewise-constant. To keep the desired performance of the faulted system, a dynamic full-order output-feedback controller is presented. By using the Lyapunov method and inequality techniques, exponential mean-square stability criteria of the fault-free closed-loop system are obtained and a design of the dynamic full-order output-feedback controller is given. Finally, a numerical example is given to show effectiveness and advantage of the obtained results.

Scalable distributed sensor fault diagnosis for smart buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants' productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

Sensor Fault Reconstruction based on Adaptive Sliding Mode Observer for Forklift Fault-Tolerant Control System

For the problem of multiple sensor fault detection and reconstruction in the forklift fault-tolerant control system, a sliding mode observer (SMO) with adaptive regulation law is proposed. Based on the three-degree-of-freedom (3-DOF) model of forklift, a linear state equation with output disturbance is designed as its equivalent sensor fault model. The sensor fault is converted into an actuator fault by defining an auxiliary state variable as an output signal filter. Then the SMO-based method of sensor fault detection and reconstruction is given. Without knowing the upper bound of an unknown fault, an adaptive sliding mode observer (ASMO) can also be effective through the adaptive algorithm. Finally, experimental results further verify the effectiveness of the method, and provide a foundation for forklift fault-tolerant control.

Nonparametric Method for Aircraft Sensor Fault Real-Time Detection and Localization

This paper is a part of a series of articles on unified nonparametric methods in dynamic systems theory. Here the authors propose a new method for dynamic system sensor fault real-time detection and localization based only on some past measurements of monitored system input-output signals. The described nonparametric method needs no any priori information on system or sensor model parameters and does not require functional redundancy, identification, prediction, training or statistical calculations. An example of nonparametric detection and localization of aircraft multiple sensor faults is presented.

Sensor fault-tolerant control of DFIG based wind energy conversion systems

This paper proposes a fault-tolerant control (FTC) strategy for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) with sensor faults. The DFIG system is decomposed into stator, rotor, grid current models, and the sensor fault observer (SFO) is presented to provide accurate estimations of sensor fault components in the presence of model uncertainties. Based on the independent current dynamics of DFIG system, three SFOs are designed in parallel to respectively estimate the stator, rotor, and grid current senor fault signals. During the occurrence of faults, the fault estimations derived from current observers are adopted to compensate the faulty signals, thereby achieving the system reconfiguration of DFIG without requiring any fault diagnosis schemes. Simulation studies are undertaken on a grid-connected WECS under the conditions of stepped and random wind speeds, and single and multiple current sensor faults. Simulation and experimental results reveal that the FTC-controlled DFIG system is resilient from the sensor faults and maintains continuous stable operation.

Fault diagnosis of nonlinear systems using recurrent neural networks

In this work, we propose a fault detection and isolation (FDI) methodology that enables diagnose of single, multiple and simultaneous actuator and sensor faults regardless of the utilized model structure. The basis of the proposed methodology is modelling and inversion of nonlinear systems using recurrent neural network (RNN)s. To this end, a bank of RNNs is used to estimate system inputs and/or outputs and build predictive models using the obtained estimates and an RNN as the plant model. Then a bank of residuals is generated in a way each residual is sensitive to a subset of faults and insensitive to the rest and as a result of this, a unique fault signature is obtained for each fault scenario. RNNs can be replaced by other machine learning techniques such as partial least square (PLS)s, random forest, etc. One of the advantages of the proposed methodology is that it does not require the existence of plant fault history or first principles models unlike other existing results in the literature. Also, it enables isolation of actuator and simultaneous actuator and sensor faults in highly interconnected systems. The effectiveness of the proposed FDI scheme is shown via simulation examples.

Multiple-sensor fault detection and isolation using video processing in production lines

ABSTRACT Production-line sensors are essential for synchronisation and execution of the correct workflow in a manufacturing process. Usually, sensor faults lead to serious delays, or even the complete termination of the manufacturing process, to carry out maintenance. Thus, sensor faults compromise the reliability of the manufacturing system and disturb the production schedule. This study proposes a multiple-sensor fault detection and isolation scheme for manufacturing series production lines. The proposed scheme adopts a global-redundancy method, using a digital camera and an ad-hoc video processing algorithm to detect and isolate faulty sensors. The main objective of this research is to preserve continuity of the production workflow and solve the problem of production delays and interruptions. Moreover, the scheme provides the possibility of online and post-process system maintenance. Further, the collected information on sensor false alarm rates can be used for a reliability analysis of the production line. The proposed scheme was tested using a laboratory production line model. The results show that the proposed scheme achieves the established objectives and improves the reliability of the manufacturing process.

Adaptive fuzzy fault compensation tracking control for uncertain nonlinear systems with multiple sensor faults

This paper investigates the problem of fault-compensation tracking control for a class of uncertain nonlinear strict-feedback systems. The presence of multiple sensor faults leads to that the existing feedback control based on perfect measurement will be completely invalid and cannot achieve the desired tracking control. To solve this problem, a new-type control strategy is proposed. A cubic absolute-value Lyapunov stability theory is used to online estimating the fault information, and an adaptive signal compensation mechanism is proposed to eliminate the adverse effects caused by the sensor faults. It is proved that by applying the presented method, both the tracking performance and the boundedness of closed-loop signals can be guaranteed. Finally, simulation results are given to illustrate the established theoretical findings intuitively.

Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems

The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.