Fault Isolation Algorithm Research Articles

A novel auxiliary signal design algorithm for weak fault isolation based on zonotopic optimization

A novel active isolation method for weak faults based on zonotopes is proposed. First, a zonotopic filter is designed to estimate the system state; subsequently, an auxiliary signal is designed based on the obtained state set, and the auxiliary signal is added to both the normal and fault models to ensure that the intersection of their state sets is empty. Then, the intersection solution process is transformed into an optimization problem. Finally, simulation results from a numerical simulation and an experiment on a DC–DC circuit platform verify that the auxiliary signal designed using this method is more optimal and less conservative compared with other auxiliary signal design methods.

Fault detection and isolation of floating wind turbine pitch system based on Kalman filter and multi-attention 1DCNN

In this paper, the fault detection and isolate problem is investigated for the pitch system of floating wind turbine. In the addressed system model, the system noises and measurement noises are correlated, and the measurement is affected by the missing phenomena. A Kalman filter is designed to handle the correlated noises and estimate the pitch angle, and a residual of the measurement of the pitch system is constructed to detection the faults. Then the fault isolation algorithm is presented based on a multi-attention mechanism one-dimensional convolutional neural network, which is employed to accurately isolate the faults. The simulation results show that the proposed method can significantly improve the accuracy of fault detection and isolation, which the fault isolation accuracy of the simulation results reaches 99.15%.

Machine Learning based Drive Fault Diagnosis and Isolation Algorithm for 4WIS4WID Vehicle using Residual Visualization

Machine Learning based Drive Fault Diagnosis and Isolation Algorithm for 4WIS4WID Vehicle using Residual Visualization

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.

An independent component analysis based correlation coefficient method for internal short-circuit fault diagnosis of battery-powered intelligent transportation systems

Battery-powered intelligent transportation systems (BPITS) are growing at an alarming rate, and a problem that cannot be ignored is that the safety of battery packs is currently the weak link in the security of BPITS. However, developing a fast and reliable diagnostic method is not easy due to the large number of battery cells and the difficulty to guarantee consistency among the individual cells. To better resolve this problem, a new fault diagnosis scheme based on voltage correlation coefficient (VCC) and independent component analysis (ICA) model is proposed in this paper. Where VCC is used to extract the fault signature of the battery pack independent of the cell inconsistency; and ICA is used to model the non-Gaussian distributed high-dimensional VCC signals in parallel and derive a statistical-based fault detection index. The kernel density estimation-based algorithm is used to calculate the theoretical threshold and to determine the optimal window width for VCC. In particular, a variable reconstruction-based fault isolation algorithm is proposed for accurate and reliable determination of the location of the faulty cell to prevent the smearing effect. Experimental results conducted on a real battery testbed under different conditions and configurations show that the new method provides significant improvements in both detection latency and positioning reliability in diagnosing internal short-circuit (ISC) faults with different failure grades.

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>

Protection Algorithm for Fault Identification and Isolation in DC Microgrid

In renewable energy dominated distributed ring configuration direct current (DC) networks, the protection philosophy is one of the critical challenging task. It is due to the existence of power electronic converters and erratic attributes of distributed energy sources. Consequently, conventional current direction based as well as over current protection strategies is not applicable for DC microgrids. In this paper, protection algorithm for fault recognition and isolation of faulty line is presented based on the polarity of change in inductance immediately after fault inception. The voltage and current sample information is used to determine the parameter by employing the least square estimation (LSE) technique. The efficiency of the proposed method is tested for internal and external faults, the impact of fault resistance and fault location, different system configurations, and load change conditions in MATLAB/Simulink simulation. It is noted that proposed method would categorize internal and external faults perfectly. The operating time of the proposed protection method is comparatively less than the existing methods. It also improves selectivity, security, and reliability under above mentioned abnormal cases.

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.

An Integrated Model-Based and Data-Driven Gap Metric Method for Fault Detection and Isolation.

This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm.

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.

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.

DC motor fault detection and isolation scheme with the use of directional residual set

The subject of research is presented as online-estimation of characteristics of DC motors under various loads. The paper is devoted to a modern approach to solve the problem of detecting DC motor failures. Proposed detection method is based on the set of full state Luenberger observers. Isolation scheme uses directional residual set and relationships between fault direction and residual vector. The procedure of synthesizing the fault detection and isolation algorithm for DC motor is designed. This scheme performance is proved with computer modeling of typical DC motor RK 370CA with faults caused by unaccounted force momentum acting on rotor, input voltage disturbance, velocity and current sensors failures. The algorithm correctly defines motor state (fault presence or absence) and also properly isolates fault cause. Proposed method advantage is compared to other solutions based on hardware and timing redundancy, identification and observers lies in the opportunity to detect and isolate faults of input and output signals with trivial synthesis and absence of the need to expand system hardware. Proposed method is applicable to any second order system, and also there is a possibility to use it for higher order systems with the corresponding changing of the equation systems solving for observer synthesis. This algorithm allows realizing online fault isolation and does not require additional measuring which promotes decrease of diagnostic costs, repair and serving time saving, modern accident detection. The results can be applied to DC motor control to increase reliability and to develop DC motor control systems.

An Acoustic Fault Detection and Isolation System for Multirotor UAV

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well.

Fault-Tolerant Control for a High Altitude Long Endurance Aircraft

High Altitude Long Endurance (HALE) aircraft consist of extremely light-weight structures in combination with a high wingspan and high aspect ratio. The combination of these properties results in an unique dynamic behavior of the aircraft system featuring a strong interaction of structural and rigid body eigenmodes. These characteristics lead to specific demands on the robustness and fault tolerance of flight control algorithms of such aircraft. The control system must be able to navigate the aircraft safely along defined tracks even in case of fault scenarios. Due to the size of these aircraft they are usually over-actuated featuring multiple redundant control surfaces. This redundancy is used in this paper to design a fault tolerant control system ensuring optimal control performance during fault scenarios. The strategy is based on a fault detection and isolation (FDI) algorithm to detect malfunctioning control surfaces. This fault information is used to switch to alternate control laws in a multi-model control approach. The FDI filters are designed using the nullspace-based design paradigm, while the alternate controllers are synthesized applying structured H∞ control design techniques.

Attitude estimation and control based on modified unscented Kalman filter for gyro-less satellite with faulty sensors

A modified unscented Kalman filter is presented to estimate the quaternion parameters as well as the angular velocities of a rigid gyro-less satellite under faulty sensor conditions. The task is carried out using the Sun sensor and magnetometers as attitude sensors with bounded noise and unknown fault(s). Following the presentation of the satellite attitude dynamics and filtering formulations, a new fault detection and isolation algorithm is proposed. The latter is based on a modified unscented Kalman filter structure for improved fault detection, sensor isolation, and attitude control (AC). A Backtracking Search Algorithm (BSA) is also used to design and optimize the PID controller gains, as well as to optimize sensor installation orientation for residual signal decoupling. In this regard, the paper presents and proves a theory that describes how the proposed optimization process for sensor installation can analytically decouple the faulty orientation signal from the healthy ones. A comparison of attitude and angular velocity estimation approaches using the standard Kalman, extended Kalman, and a proposed modified unscented Kalman filter is also provided. The results show that the proposed filter performs significantly better in closed-loop AC systems and copes adequately with sensor faults.

Air data fault detection and isolation for small UAS using integrity monitoring framework

A Fault Detection and Isolation (FDI) algorithm is developed to protect against Water-Blockage (WB) pitot tube failure in the safety-critical Air Data System (ADS) used on small Unmanned Aircraft Systems (UAS). The algorithm utilizes two identical Synthetic Air Data Systems (SADS) as the basis for state estimation. Each SADS works independently with a pitot tube while sharing an IMU and GNSS receiver. The fault detection is designed using the integrity monitoring framework, and the isolation is obtained via independent fault detection channels. The ADS requirements are established, and the WB failure mode is analyzed based on real faulty air data. A new residual-based test statistic is introduced, and the link among the test statistic, observability matrix, and Minimal Detectable Error (MDE) are examined. Finally, a flight data set with a known water-blockage fault signature is used to assess the algorithm's performance in terms of the air data protection levels and alert limits.

Efficiency of the Bond Graph Approach for Robust Diagnosis Micro Electro Mechanic System

Improving the operational safety of systems is essentially based on fault detection and isolation algorithms, these algorithms mainly consist in deriving the different kinds of faults while minimizing false alarms, nondetections and delays in fault detection. The choice of robust diagnosis by the bond graph approach (BG) is based on the use of a robust analytical relations redundant (ARR) generation algorithm from linear fractional transformation models (LFT), where the uncertain part of the ARR is used to generate the adaptive residue thresholds. These relationships not only allow the detection and isolation of defects on the various elements of the system, but also the location since the industrial system is modeled element by element.The BG-LFT modeling and the robust diagnosis of an accelerometer using micro-electromechanical are presented in this paper. The interaction of the various phenomena is taken into account, thanks to the energy properties of the bond graph tool. The residuals and the adaptive thresholds for normal operation and in abnormal operation are determined. The results suggest that the use of the Bond Graph model for normal operation, the evolutions of the residuals converge towards zero, whereas a fault caused by an element (for example, the stiffness springs [Formula: see text] or [Formula: see text] are modeled, respectively, by capacitive elements [Formula: see text]and [Formula: see text] shows that the residues [Formula: see text], [Formula: see text] and [Formula: see text] become different from zero. These variations explain that these residues are sensitive to these elements, which are confirmed by results presented in Table 2. The analysis of the sensitivity of the residuals to parametric and structural faults is carried out to determine the detectable values of the faults and therefore to monitor the performance of the diagnosis.

Optimized fault detection using bond graph in linear fractional transformation form

This article deals with the optimal robust fault detection problem using the bond graph in its linear fractional transformation form. Generally, this form of the bond graph allows the generation of two perfectly separate analytical redundancy relations, that are used as residual and threshold. However, the uncertainty calculation method gives overestimated thresholds. This may, for instance, lead to undetectable faults. Therefore, enhancing the robustness of fault detection and isolation algorithms is of utmost importance in designing a bond graph–based fault detection system. The main idea of this article is to develop optimized thresholds to ensure an optimal detection, otherwise this article proposes a method to detect tiny magnitude faults concerning parameter’s uncertainties. This work considers the issue of optimal fault detection as an optimization problem of the gap between the residuals and its threshold. New uncertainty values will be calculated in a way that these estimated parameters ensure the desired optimized gap between residuals and thresholds. These estimated uncertainty values will be used to generate optimized adaptive thresholds. Through these thresholds, we increase the sensitivity of the residuals to tiny magnitude faults, and we ensure an optimal and early detection.

Fault Diagnosis and Isolation for Brushless Electronic Returnless Fuel Systems

The brushless electronic returnless fuel system (ERFS) is a complex system, which is subject to different faults such as motor resistance increase and pressure sensor bias. Any of these faults may lead to no-start and safety-related issues such as loss of engine power. It’s challenging to develop a diagnosis and isolation method to detect and isolate faults in the field, with feature signals which capture different fault signatures. In this study, we first develop a set of feature signals which are used to monitor the behavior, track the degradation, predict the potential failures, and diagnose the issues of the brushless ERFS. The feature signals include the estimated resistance, current error ratio, PWM duty cycle error ratio, zero pump speed ratio, etc. Then we develop a fault diagnosis and isolation algorithm to generate the diagnostic results based on feature signals. The algorithm is tested on a vehicle with fault injection. The results show that different faults can be identified correctly with the developed algorithm. This study enables development of diagnosis and prognosis for the brushless ERFS, which can protect customers from walk-home scenarios and safety related issues due to the brushless ERFS failures.