Fault Detection And Isolation Algorithm Research Articles

Data-Quality Assessment for Digital Twins Targeting Multi-Component Degradation in Industrial Internet of Things (IIoT)-Enabled Smart Infrastructure Systems

In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, defined as the measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms within smart infrastructure systems. This research employs a comprehensive methodology, starting with an MSA process for data-quality evaluation and leading to the development and evaluation of fault detection and isolation (FDI) algorithms. During the MSA phase, the repeatability of a water distribution system’s measurement system is examined to characterise variations within the system. A data-quality process is defined to gauge data quality. Synthetic data are introduced with varying data-quality levels to investigate their impact on FDI algorithm development. Key findings reveal the complex relationship between data quality and FDI algorithm performance. Synthetic data, even with lower quality, can improve the performance of statistical process control (SPC) models, whereas data-driven approaches benefit from high-quality datasets. The study underscores the importance of customising FDI algorithms based on data quality. A framework for instantiating the MSA process for IIoT applications is also suggested. By bridging data-quality assessment with data-driven FDI, this research contributes to the design of digital twins for IIoT-enabled smart infrastructure systems. Further research on the practical implementation of the MSA process for edge analytics for PHM applications will be considered as part of our future research.

Adaptive Actuator Failure Compensation Control: A New Scheme Based on Fault Detection and Isolation

In flight control systems, multiple actuator stuck (or hardover) failures would cause saturation of the remaining healthy actuators and result in system instability. In this article, a new adaptive actuator failure compensation control scheme is proposed for a class of nonlinear systems with multiple redundant inputs. Under a mild assumption on the time interval between two successive failures, an online fault detection and isolation (FDI) algorithm is introduced, with which an actuator failure can be exactly identified in a given finite time without using the persistent excitation (PE) condition. The main advantage of the proposed scheme is that with the help of the FDI algorithm, an actuator with stuck failure (SF) can be deactivated before a new failure occurs and therefore, the drawback of the current adaptive failure compensation control approach that may cause more control effort or even saturation with multiple actuators being stuck can be overcome.

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>

Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System

This paper presents a systematic fault analysis and diagnosis method of a PEM fuel cell control system using a model-based approach. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system, and to diagnose them under the assumption that the model and the process are similar. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system and diagnose them under the assumption that the model and the process are similar. In this work, a model-based approach was adopted for fault analysis and diagnosis, and its methods are suggested. A PEM fuel cell is mathematically modelled, analyzed, and verified for the analysis and simulations. Relationships among variables are shown using an incidence matrix and with a Dulmage–Mendelsohn decomposition of the matrix. When it is difficult to detect faults due to a deficient degree of redundancy, a bi-partite graph is used to analyze the effect of faults and to assess the possibility of fault detection through the appropriate redundant sensor placement. Thereafter, residuals are obtained based on analytical redundancies of the system, and a fault signature matrix is subsequently constructed. A fault detection and isolation (FDI) algorithm is developed based on a fault signature matrix that describes the connection between faults and residuals. The simulation results demonstrate the validity and effectiveness of the proposed FDI algorithm for diagnosing faults. With the proposed FDI algorithm, eight faults could be diagnosed by FDI algorithm with given sensors in the system.

A Dynamic Principal Component Analysis and Fréchet-Distance-Based Algorithm for Fault Detection and Isolation in Industrial Processes

Fault Detection and Isolation (FDI) methodology focuses on maintaining safe and reliable operating conditions within industrial practices which is of crucial importance for the profitability of technologies. In this work, the development of an FDI algorithm based on the use of dynamic principal component analysis (DPCA) and the Fréchet distance δdF metric is explored. The three-tank benchmark problem is studied and utilized to demonstrate the performance of the FDI method for six fault types. A DPCA transformation for the system was established, and fault detection was conducted based on the Q statistic. Fault isolation is also of critical importance for proper intervention to mitigate fault effects. To identify the type of detected faults, the fault responses within the PC subspace were analyzed using the δdF metric. The use of the Fréchet distance metric for the isolation of faults combined with DPCA for feature extraction is a novel technique to the best of the authors’ knowledge that provides a robust computational tool with low computational cost for FDI purposes that fits well into the Industry 4.0 framework.The robustness and sensitivity of the method was validated for a wide variety of signal-to-noise ratio (SNR) conditions, with findings indicating a possible average false and missed alarm rate of 0.1 and a macro-averaged F-score above 0.8 in all cases.

An innovation-based actuator/surface fault detection, isolation and filter tuning

PurposeThe purpose of the paper is to present an innovation-based new actuator/surface fault detection and isolation (FDI) method, which is sensitive to the changes in the innovation mean of the Kalman filter (KF) and the KF tuning method for the case of actuator/surface failure.Design/methodology/approachThe multiple system noise scale factors (MSNSFs) are used in this method as the monitoring statistics. MSNSFs are determined to make it possible to perform the actuator/surface FDI operations simultaneously.FindingsThe introduced FDI algorithm can detect and isolate the loss of effectiveness type actuator/surface faults in real time. The proposed KF tuning method works effectively against actuator/surface fault. The actuator/surface fault detection, isolation and filter tuning are achieved by just using a simple modification over the conventional KF.Originality/valueThe MSNSF-based actuator/surface fault detection, isolation and filter tuning algorithms are investigated together for the first time. The actuator/surface FDI operations are performed simultaneously.

Novel Approach to Fault-Tolerant Control of Inter-Turn Short Circuits in Permanent Magnet Synchronous Motors for UAV Propellers

This paper deals with the development of a novel fault-tolerant control technique aiming at the diagnosis and accommodation of inter-turn short circuit faults in permanent magnet synchronous motors for lightweight UAV propulsion. The reference motor is driven by a four-leg converter, which can be reconfigured in case of a phase fault by enabling the control of the central point of the motor Y-connection. A crucial design point entails the development of fault detection and isolation (FDI) algorithms capable of minimizing the failure transients and avoiding the short circuit extension. The proposed fault-tolerant control is composed of two sections: the first one applies a novel FDI algorithm for short circuit faults based on the trajectory tracking of the motor current phasor in the Clarke plane; the second one implements the fault accommodation, by applying a reference frame transformation technique to the post-fault commands. The control effectiveness is assessed via nonlinear simulations by characterizing the FDI latency and the post-fault performances. The proposed technique demonstrates excellent potentialities: the FDI algorithm simultaneously detects and isolates the considered faults, even with very limited extensions, during both stationary and unsteady operating conditions. In addition, the proposed accommodation technique is very effective in minimizing the post-fault torque ripples.

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.

The challenge of advanced FDI algorithms for aircraft systems

What is the Achilles’ heel of academic Fault Detection and Isolation (FDI) methods when it comes to application to real aircraft systems? This paper discusses some major and decisive issues that stand in the way of their transition from lab developments and simulations to real life applications in aeronautics. Often underestimated by academics, these issues determine the survivability of a new design for final V&V (Verification & Validation) activities. The paper recalls some practical items that should be considered at the design stage to help reach high Technological Readiness Level (TRL) scales for a given FDI algorithm. The paper will also take a look in the future and the way forward to anticipate future needs.

LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System

Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.

Fault-Tolerant Control of a Three-Phase Permanent Magnet Synchronous Motor for Lightweight UAV Propellers via Central Point Drive

This paper deals with the development and the performance characterization of a novel Fault-Tolerant Control (FTC) aiming to the diagnosis and accommodation of electrical faults in a three-phase Permanent Magnet Synchronous Motor (PMSM) employed for the propulsion of a modern lightweight fixed-wing UAV. To implement the fault-tolerant capabilities, a four-leg inverter is used to drive the reference PMSM, so that a system reconfiguration can be applied in case of a motor phase fault or an inverter fault, by enabling the control of the central point of the three-phase connection. A crucial design point is to develop Fault-Detection and Isolation (FDI) algorithms capable of minimizing the system failure transients, which are typically characterized by high-amplitude high-frequency torque ripples. The proposed FTC is composed of two sections: in the first, a deterministic model-based FDI algorithm is used, based the evaluation of the current phasor trajectory in the Clarke’s plane; in the second, a novel technique for fault accommodation is implemented by applying a reference frame transformation to post-fault commands. The FTC effectiveness is assessed via detailed nonlinear simulation (including sensors errors, digital signal processing, mechanical transmission compliance, propeller loads and electrical faults model), by characterizing the FDI latency and the post-fault system performances when open circuit faults are injected. Compared with reports in the literature, the proposed FTC demonstrates relevant potentialities: the FDI section of the algorithm provides the smallest ratio between latency and monitoring samples per electrical period, while the accommodation section succeeds in both eliminating post-fault torque ripples and maintaining the mechanical power output with negligible efficiency degradation.

A Cost-Aware DNN-Based FDI Technology for Solenoid Pumps

Fluid Pumps serve a critical function in hydraulic and thermodynamic systems, and this often exposes them to prolonged use, leading to fatigue, stress, contamination, filter clogging, etc. On one hand, vibration monitoring for hydraulic components has shown reliable efficiencies in fault detection and isolation (FDI) practices. On the other hand, signal processing techniques provide reliable FDI parameters for artificial intelligence (AI)-based data-driven diagnostics (and prognostics) and have recently attracted global interest across different disciplines and applications. Particularly for cost-aware systems, the choice of diagnostic parameters determines the reliability of an FDI/diagnostic model. By extracting (and selecting) discriminative spectral and transient features from solenoid pump vibration signals, accurate diagnostics across operating conditions can be achieved using AI-based FDI algorithms. This study employs a deep neural network (DNN) for fault diagnosis after a correlation-based selection of discriminative spectral and transient features. To solve the problem of hyperparameter selection for the proposed model, a grid search technique was employed for optimal search for parameters (number of layers, neurons, activation function, weight optimizer, etc.) on different network architectures.The results reveal the high accuracy of a three-layer DNN with ReLU activation function, with a test accuracy of 99.23% and a minimal false alarm rate on a case study.

Observer-based IM stator fault diagnosis: Experimental validation

In this paper, an experimental validation of an efficient approach to the Fault Detection and Isolation (FDI) of Induction Motor (IM) is proposed. The problem of Inter-turn short circuits (ITSC) in the stator windings is addressed. By introducing fault factors in the IM model an observer-based residual generator is designed, allowing the detection of ITSC in stator windings. The residual generator is built around an extended Kalman Filter (EKF) in order to estimate state variables and fault factors, which permits the evaluation of the severity of the fault. To overcome the problem of tuning the EKF a PSO algorithm is developed. It carries out a heuristic search of the noise matrices by optimizing a cost function. The proposed solution is validated by computer simulations and by real-time implementation on dSPACE 1104 Digital Signal Processor (DSP) test-bench under the healthy and the faulty conditions of IM. To perform tests under faulty conditions, an IM with customized design is built and the stator is rewound permitting to create ITSC. The results reveal the quick detection of the faults, the quantification of its severity and confirm the efficacy of this observer-based FDI algorithm.

Teaching Model-based Fault Detection and Isolation using a Virtual Laboratory Environment

Abstract Fault detection and isolation (FDI) systems play a key role to provide efficiency, reliability and safety in today’s industrial processes. The teaching of FDI systems is facilitated if it is carried out not only with theoretical lectures but also with practical experiences. This paper proposes a virtual laboratory environment (VLE) to carry out online practical experiences with FDI systems for a benchmark process. Thanks to this VLE, students can set up faults in sensors, actuators or in the process itself, program model-based FDI algorithms and test FDI system performance. The use of this environment is illustrated by testing the performance of FDI systems for the quadruple-tank process (4TP) under different fault scenarios. Finally, the procedure of using this proposal for practical experience with two model-based FDI design methods is shown.

Model-based FDI for Agile Spacecraft with Multiple Actuators Working Simultaneously

Current and future space missions require agile and reliable spacecraft capable of trailing and keeping the required attitude. Most of the agile spacecraft missions are near-Earth based but some are placed far away from Earth and its influence. One example of such missions is the Athena mission, which requires the spacecraft to perform fast and large-angle attitude slew manoeuvres. Such manoeuvres often imply simultaneous use of multiple actuators such as thrusters and reaction wheels (RWs). A fault in any of these actuators might lead to partial or full damage of sensitive spacecraft instruments. In this research project, a novel model-based Fault Detection and Isolation (FDI) strategy is proposed, which is able to detect and isolate various actuator faults, such as stuck-open/closed thruster, thruster leakage, loss of effectiveness of all thrusters, and change of RW friction torque due to change of Coulomb and/or viscosity factor. Moreover, the proposed FDI strategy is also able to detect and isolate faults affecting the RWs tachometer. The design of the FDI algorithm is based on a multiplicative extended Kalman filter, a generalised likelihood ratio thresholding of the residual signals, and a logic algorithm which unequivocally link the faults to the symptoms. The performance and robustness of the proposed FDI strategy are evaluated using Monte Carlo simulations and carefully defined FDI performance indices. In addition, the influence of faults’ magnitudes, times of fault occurrence, and uncertainties’ magnitudes on the FDI system performance are evaluated. Preliminary results suggest promising performance in terms of detection/isolation times, miss-detection/isolation rates, and false alarm rates. Also, uncertainties on the spacecraft inertia seem to have a negative impact on the FDI performance. In order to fully understand the research project presented here, graduate-level knowledge on rigid body dynamics and kinematics, control theory, and filters applied to estimation might be required. If any of these areas are not known by the reader, it is recommended to read some of the associated literature referenced in the bibliography.

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams.

Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors.

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

Robust Positioning Algorithm for a Yard Transporter Using GPS Signals with a Modified FDI and HDOP

This paper proposes a robust positioning algorithm using a modified fault detection and isolation (FDI) algorithm and the horizontal dilution of precision (HDOP) for localising a yard transporter using the global positioning system (GPS). In conventional approaches, each GPS signal received is analysed to determine if it is contaminated and only the clean signals are used for positioning the current location. This feasibility test is called an FDI scheme, and it is commonly used in commercial GPS receivers, where only the signal-to-noise ratio (SNR) is used to detect false GPS signals. On the other hand, in urban environments or near buildings, the selection of satellite signals by the SNR only is unreliable, which results in an insufficient number of satellite signals to position a transporter at a certain moment. This paper proposes a modified FDI algorithm that detects and isolates faulty signals using the SNR, the measurement quality indicator (mesQI), and the Doppler shift in sequence to improve the reliability of selecting good satellite signals and improving the positioning accuracy. In addition, the HDOP was adopted to improve the accuracy and reliability further. The effectiveness of the proposed algorithm was assessed by experiments with a mobile robot to simulate a transporter in a shipbuilding yard.

Robust Navigational System for a Transporter Using GPS/INS Fusion

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper.

Active Fault Diagnosis on a Hydraulic Pitch System Based on Frequency-Domain Identification

The blade pitch system is a critical subsystem of variable-speed variable-pitch wind turbines that is characterized by a high failure rate. This paper addresses the fault detection and isolation (FDI) of a blade pitch system with hydraulic actuators. Focus is placed on incipient multiplicative faults, namely hydraulic oil contamination with water and air, bearing damage resulting in increased friction, and drop of the supply pressure of the hydraulic pump. An active model-based FDI approach is considered, where changes in the operating conditions (i.e., mean wind speed and turbulence intensity) are accounted through the identification of a linear parameter-varying model for the pitch actuators. Frequency-domain estimators are used to identify continuous-time models in a user-defined frequency band, which facilitates the design of the FDI algorithm. Besides, robustness with respect to noise in measurements and stochastic nonlinear distortions is ensured by estimating confidence bounds on the parameters used for FDI. The approach is thoroughly validated on a wind turbine simulator based on the FAST software that includes a detailed physical model of the hydraulic pitch system. This paper presents the design methodology and validation results for the proposed FDI approach. We show that an appropriate design of the excitation signal used for active fault detection allows an early fault diagnosis (except for oil contamination with water) while ensuring a short experiment duration and an acceptable impact on the wind turbine operation.