Sensor Fault Isolation Research Articles

Reliability evaluation of the hybrid-redundancy sensor fault tolerate system in the dredging perception system

The accurate evaluation of equipment reliability is critical in engineering applications and especially challenging for equipment with complex structures and high reliability requirements. In this paper, a reliability evaluation method based on semi-Markov process is proposed and applied to the hybrid-redundancy sensor fault tolerant (HRSFT) system within the perception system of a cutter suction dredger (CSD). The method takes into account the harsh operating environment of the CSD sensor, and considers the temporal occurrence of sensor faults as well as the imperfection of the sensor fault diagnosis and isolation (SFDI). The outcomes of the model applied to a case study indicate that three key factors, namely, the failure rate of the sensors, the false alarm rate (FAR) of the SFDI system and the self-correction rate (SCR) of the HRSFT system, have a significant impact on the system reliability. Additionally, we performed a comparative analysis between the HRSFT and Double Redundancy Sensor Fault Tolerant (DRSFT) systems, from which the superiority of the HRSFT system in terms of reliability is demonstrated. A series of experiments were conducted with varying parameters values, to show the high adaptability of the model to the HRSFT system.

Sensor fault detection and isolation for smart irrigation wireless sensor network based on parity space

<p>In the recent years, wireless sensor network technology (WSN) has been widely adopted in precision agriculture for determining the needs of the soil in term of water by monitoring some environmental parameters. To do this, WSN is constructed using several sensor nodes; these small sensing devices are prone to failure and may produce erroneous measurements. To ensure good management of freshwater, the network service quality is necessary. To avoid the degradation of service, the detection of the faulty sensor in WSN is required. In this paper, a fault detection and isolation (FDI) algorithm derived from a parity space approach and based on direct redundancy is proposed toward detecting and isolating sensor fault in WSN. In laboratory experiments, the proposed FDI algorithm proved its effectiveness.</p>

Fault detection and isolation for a secondary loop refrigeration system

Secondary loop refrigeration systems offer advantages over conventional system architectures: refrigerant charge reduction, maintenance simplification, and reduced safety hazards. Currently, no model-based fault detection for a secondary loop system has been reported. This article presents a fault detection and isolation scheme for a secondary loop refrigeration system where parametric faults (dirt/ice-buildup and secondary loop pump failure) and sensor faults (four temperature sensors in the secondary loop) are detected and isolated. The fault detection and isolation system is based on unknown input observers and on extended Kalman filters. The models are obtained through physical relations combined with experimental parameter determination (gray-box model) and are cross-validated against measurement data. The complete fault detection and isolation architecture is experimentally validated on a secondary loop refrigeration system mounted on a cooling box inside a climate chamber. Experimental results show that important parametric faults can be reliably detected and isolated, and the fault detection and isolation of sensor faults are effective.

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies instantaneously in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable DTs. More specifically, a real-time general machine-learning-based architecture for sensor validation is proposed, built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behavior and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world datasets is conducted and the performance of the proposed architecture is validated under hard and soft synthetically generated faults.

Actuator and Sensor Fault Detection and Isolation System Applied to a Distillation Column

Actuator and Sensor Fault Detection and Isolation System Applied to a Distillation Column

Sensor FDI System Based on Discrete‐TimeFractional‐Order Observers

The design of sensor fault detection and isolation (FDI) systems has been studied widely by several authors of different fields of science (engineering, mathematics, physics) to ensure the continuous operation of systems or processes. This research presents the design and evaluation of a fault detection and isolation (FDI) system based on discrete fractional‐orderhigh‐gain observers (DFOHGO) for the outlet temperature sensors of a heat exchanger. The formulation of the discrete fractional‐order observers is based on the Grünwald–Letnikov fractional‐order derivative. The FDI system is based on a bank of two DFOHGOs to carry out a residual evaluation. The obtained results showed that the DFOHGOs provide accurate estimations of the temperatures. This allows switching the faulty sensor signal to the temperature estimations provided by the DFOHGOs when a fault occurs.

Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

Sensor fault detection and isolation based on zonotopic Kalman Filter for accelerometer system in drilling tools

Sensor faults of the accelerometer system impact the efficiency and security of the drilling process, so the sensor fault detection and isolation are significant. However, there are insufficient relevant studies on the accelerometer system affected by time-varying disturbances with unknown bounds. This paper proposes a residual generator based on the Zonotopic Kalman Filter with the process disturbance estimation, and the bounds of parametric uncertainties and measurement noises are obtained by calibration. Then, a fault detection test is realized by time-varying residual bounds regarded as the combinations of residuals and dynamic thresholds. Moreover, a fault isolation decision is developed by analyzing fault directions. Finally, simulation and experiment results demonstrate the effectiveness of the proposed method. The proposed method estimates time-varying process disturbances and performs better than existing methods. This paper provides a reference for the reliable application of the accelerometer system.

Hyperelliptic Kalman filter-based aeroengine sensor fault FDIA system under multi-source uncertainty

Aeroengine faces multi-source uncertainty while operating. To reduce the conservativeness and improve the performance of the diagnosis system in complex environments, the multi-source uncertainty environment, which consists of aeroengine epistemic uncertainty and stochastic uncertainty of the control system, is considered in this paper. Based on polynomial chaos expansion (PCE), the uncertainty of the state response and output response is quantified. In addition, by proposing the hyperelliptic Kalman filter (HeKF), the optimal estimation of state correlation and output variance is realized, and the adaptive weighting matrix is provided on this basis. Combined with the thresholds calculated by design indicators of the diagnosis system, conservativeness-reduced fault detection is achieved. Ultimately, based on the fault matching idea, the hyperelliptic Kalman filter bank (HeKFB) is established and implemented for fault isolation and accommodation. Open-loop and closed-loop numerical simulations demonstrate that the proposed HeKF-based aeroengine sensor fault detection, isolation, and accommodation (FDIA) system is efficient under multi-source uncertainty conditions. Furthermore, compared to the extended Kalman filter (EKF) and the Kalman particle filter (KPF), the HeKF-based FDIA system is less conservative, more sensitive to minor faults, and superior in guaranteeing the security and reliability of aeroengine operation.

Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors

The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams.

Fault detection and anti-icing technologies in wind energy conversion systems: A review

Over the past few decades, wind energy has emerged to be one of the most promising and popular sources of renewable energy in different continents. The efficiency and robustness of wind energy conversion systems are highly dependent on the safe operational status of the system and the capability to achieve cost-effective operation. In order to prolong the systems’ operational time and increase the reliability of the wind turbines, predictive and preventive maintenances are required. This is important in reducing the operational and maintenance (O&M) costs of the wind energy conversion systems. This review paper acts as a platform for readers to comprehend the most recent, up-to-date and state-of-the-art approaches in detecting and diagnosing faults in wind energy conversion systems, including cepstral editing procedure, sensor fault detection and isolation method, induction abrasive particle sensor, novel model-based approach and machine learning based data-driven approach. Current innovations in anti-icing technologies, including resistive heaters, application of hybrid electro-heating and superhydrophobic coating, ultrasonic deicing, research on materials of ultrasonic transducers for anti-icing and a novel embedded anti-icing system as a part of the O&M measure to reduce the levelized cost of energy of the wind energy conversion systems are also presented and discussed in this paper.

Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator

This paper proposes a data-driven sensor fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear predictor for a nonlinear system. Then, the obtained Koopman predictor has been used in a geometric framework for sensor fault detection and isolation purposes without relying on a priori knowledge about the underlying dynamics as well as requiring faulty data, leading to a data-driven sensor fault detection and isolation framework for nonlinear systems. Finally, the approach's efficacy is demonstrated using simulation case study on a two-degree of freedom robot arm.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

Fault-tolerant control with high-order sliding mode for manipulator robot

<p>The safety of manipulators systems is receiving a lot of attention these days. Faults have the ability to carry out harmful activities that harm equipment, the environment, or persons. As a result, it is critical to detect and diagnose problems as soon as possible, as well as to incorporate fault tolerance to avoid performance deterioration and harmful circumstances. Robust sensor fault detection and isolation (FDI) as well as fault-tolerant control (FTC) for a robotic system are discussed in this work. The goal of this research is to create an FDI method that uses a super-twisting third-order sliding mode (STW-TOSM) observer to estimate residual signals. A suggested system based on a higher-order sliding mode (HOSM) observer/controller approaches is used to accomplish this. In comparison to an active FTC technique, the test results demonstrate high level of performance. Finally, simulation results illustrate the efficacy of the presented techniques.</p>

Sensor fault detection and isolation via networked estimation: rank-deficient dynamical systems

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

An active approach for isolating replay attack from sensor faults

In this paper, a new approach for isolating sensor faults from replay attack in the presence of model uncertainties, based on Unknown Input Observer (UIO) and Luenberger observer is proposed. The main idea of the proposed approach depends on designing two groups of observers with adding watermarking signals to the system inputs. The first group consists of three observers for rebuilding the uncertainties and detecting the abnormalities depending on the watermarking signals. In the second group, watermarking signals are treated as unknown inputs by designing a suitable UIO observer that makes this group insensitive to replay attack. An advantage of the proposed approach is the simple mathematical formulations and capability to reconstruct accurately the uncertainties in abnormal free cases. Two examples are provided to demonstrate the effectiveness and advantages of the proposed approach.

Development of Input Training Neural Networks for Multiple Sensor Fault Isolation

This paper considers the problem of inhibiting smearing effects for multiple sensor fault isolation. Although the reconstruction-based approach has received considerable attention and achieved excellent results, the investigation into nonlinear systems has been relatively limited. A new reconstruction-based input training neural network aided by the bound and branch method (BAB-RBITNN) is proposed for the nonlinear systems so as to achieve the desired performance of multiple sensor fault isolation. In BAB-RBITNN, a hypothetical faulty direction is naturally integrated into the objective testing function as residual weights for reducing the negative impacts of inaccurate sensor data, taking advantage of the non-static ITNN testing process. The weighted objective function is then considered as the fitness function to obtain the minimum predicted residual by the covariance matrix adaptation evolutionary strategy algorithm. The corresponding optimized fitness is regarded as the reconstruction-based index, which is the critical indicator of fault isolation in the proposed method. Besides, the BAB method is implemented to handle the combinatorial optimization problem instead of a brute search for intelligently pinpointing the real faulty sensors. The efficiency of the proposed method is shown through a validation example and two industrial examples. Comparisons with other methods, including reconstruction-based principal component analysis and reconstruction-based auto-associative neural network techniques, are also presented.

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation.

Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.

A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack

Considering the limitations in existing correlation coefficient-based, entropy-based and big data analysis-based voltage sensor fault diagnosis methods, we develop a novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack in this paper. Firstly, a “ohmic resistance”-based selection method is periodically performed to artificially divide all in-pack cells into “representative cell” and non-representative cells. Secondly, during the “representative cell”-based battery pack state-of-charge (SOC) and cell SOC inconsistence estimation process, the measurement innovation (MI) between measured and estimated voltage of the “representative cell” and non-representative cells is generated in micro time-scale and macro time-scale, respectively. Regarding the “representative cell”, the faulty voltage sensor is immediately detected at the moment of the voltage sensor fault occurrence by catching the abnormal MI. As for the non-representative cells, through analyzing the discreteness degree of generated MI under faultless and faulty voltage sensors, an abnormal variance-based voltage sensor fault diagnosis method and an abnormal variance contribution-based fault location method are developed. The validation results through three sophisticated cases demonstrate that this method can rapidly catch the abnormal features for further voltage sensor fault diagnosis with low complexity and satisfactory robustness even though there exist certain faulty current.

Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers

Wind turbine power generation is becoming one of the most critical renewable energy sources. As wind power grows, there is a need for better monitoring and diagnostic strategies to maximize energy production and increase its security. In this paper, a fault diagnosis approach based on a data-driven technique, which represents the system behavior employing a Takagi–Sugeno (TS) model, is developed. An adaptive neuro-fuzzy inference system (ANFIS) method is used to obtain a set of polytopic-based linear representations and a set of membership functions to interpolate the linear models of the convex TS model. Then, considering the TS model, a fault diagnosis strategy based on convex state observers generate residuals to detect and isolate sensor faults. Unlike other methods, this proposal only needs to be trained with fault-free data. The proposed methodology is tested under different fault scenarios on a well-known wind turbine benchmark built upon fatigue, aerodynamics, structures, and turbulence (FAST). The results demonstrate the method’s effectiveness in detecting and isolating different sensor faults.