Fault Detection Sensitivity Research Articles

H−/L∞UIO‐based fault detection and isolation design in finite‐frequency domain for nonlinear systems

AbstractThe issue of fault detection and isolation design in the finite‐frequency domain for discrete‐time Lipschitz nonlinear systems subjected to actuator faults and disturbances is investigated. A bank ofH−/L∞unknown input observers (UIOs) is established using the generalized observer scheme to generate residuals that are insensitive to a specific actuator fault but sensitive to the other actuator faults. Furthermore, in the finite‐frequency domain, theH−andL∞performance indices are simultaneously used to improve the fault detection sensitivity and attenuate the influence of disturbances on the residuals, respectively. The design conditions for the bank ofH−/L∞UIOs are derived from the generalized Kalman–Yakubovich–Popov lemma and converted into an optimization problem constrained by linear matrix inequalities to solve the design matrices more easily. To diagnose actuator faults, a fault detection and isolation scheme is established using the time‐varying threshold from theL∞performance analysis. Finally, the effectiveness of the fault detection and isolation scheme using the bank ofH−/L∞UIOs is validated by the simulation of two examples.

Research on anomaly detection in oil chromatography based on convolutional neural network and grey relational analysis algorithm

Abstract The power transformer is a crucial voltage conversion node and a core device in substations. Currently, dissolved gas analysis (DGA) in oil is a practical and validated method for assessing the operational status of transformers. In this study, addressing issues such as insufficient data mining from single dissolved gas measurements, we employed a combination of Convolutional Neural Network and Grey Relational Analysis algorithm to enhance the reliability of identifying abnormal dissolved gas data in transformer oil. This approach improves the sensitivity and accuracy of transformer fault detection. The study includes data processing and result analysis, indicating that the proposed algorithm effectively identifies abnormal dissolved gas data in transformer oil.

Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current-Vibration Signals.

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current-vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current-vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).

A novel monitoring method based on multi-model information extraction and fusion

Abstract Modern industrial processes are increasingly complex, where multiple characteristics usually coexist in process data. Therefore, traditional monitoring methods based on a single model may ignore other data characteristics and obtain poor monitoring performance. Aiming at this problem, a novel monitoring method based on multi-model information extraction and fusion is proposed in this paper. Firstly, several methods are used to extract different characteristics from process data. For example, principal component analysis, independent component analysis and slow features analysis can be used to extract Gaussian, non-Gaussian and dynamic characteristics respectively. Secondly, features extracted from multiple models are combined into new potential features. Then, Lasso regression models between potential features and process variables are established. In this way, not only are multiple characteristics in process data considered during the reconstruction, but key potential features (KPFs) can be selected for each process variable. The KPFs for each process variable can form a monitoring subspace to enhance the sensitivity for fault detection. Furthermore, cluster analysis is used to reduce the redundancy of monitoring subspaces based on the similarity of each subspace. Process monitoring can be achieved by fusing the monitoring results of finally determined multiple subspaces and residual space. Case studies on three simulation processes and a real industrial process demonstrate the effectiveness and better performance.

An Improved Fault Detection and Isolation Method for Airborne Inertial Navigation System/Attitude and Heading Reference System Redundant System

The integrity of airborne inertial navigation systems (INSs) is the key to ensuring the safe flight of civil aircraft. The airborne attitude and heading reference system (AHRS) is introduced into the construction of a redundant inertial navigation system. As a backup system for an airborne INS, the AHRS exhibits a different device performance. A sequential weighted generalized likelihood ratio test (SWGLT) method, based on a principal component parity vector (PPV), is proposed. The PPV method improves the adaptability of the detection threshold to the inertial sensors’ noise and improves the probability of correct detection. At the same time, the multiscale problem of a heterogeneous redundant system error is solved by sequential weighting, and the false alarm rate is reduced. Simulation experiments show that the proposed method can improve fault detection sensitivity, reduce false alarm rates, and ensure the integrity of civil aircraft navigation systems.

The recent development of protection coordination schemes based on inverse of AC microgrid: A review

AbstractIntegration of distributed generation systems and diversity of microgrid operations led to a change in the structure of the power system. Due to this conversion, new challenges have arisen when employing traditional overcurrent protection schemes. As a consequence, non‐classical protection schemes have attracted significant attention in the last few years. Engineers and scholars have proposed different non‐standard methods to increase the power protection system and ensure the highly selectivity performance. Although the non‐standard characteristics and their requirements, in general, have been outlined and analyzed in the available literature, protection coordination based on voltage current–time inverse, as a branch of non‐standard optimization methods, has not yet been thoroughly discussed, compared, or debated in detail. To close this gap, this review introduces a broad overview of recent research and developments of the voltage current–time inverse based protection coordination. Focuses on assessing the potential advantages and disadvantages of related studies and provide a classification and analysis of these studies. The future trends and some recommendations have been included in this review for improving fault detection sensitivity and coordination reliability.

Characterizing the fault performance of real-time precise satellite orbit and clock correction products

The a priori fault probability of the real-time precise satellite orbit and clock correction products is the critical parameter for integrity monitoring of precise point positioning (PPP). The traditional fault probability evaluation methods use the worst-case instantaneous user ranging error (IURE) as the conservative test statistic. However, the systematic biases of IURE contained in the worst-case IURE barely affect the PPP accuracy, which will undermine the statistical distribution of test statistic and reduce the sensitivity of fault detection. The fault probability will be estimated over-conservatively for the traditional methods. By clarifying the sources of the systematic biases, a new test statistic is constructed by deliberately removing the systematic biases of IURE originated from satellite orbit and clock errors. One-year Global Positioning System correction products evaluation results have demonstrated that the constructed test statistic follows the Gaussian distribution with the decreased uncertainty and the improved fault detection sensitivity. The real-world data experiments have shown that the a priori probabilities of the satellite fault and the constellation fault are at the order of 10−4 and 10−5 levels, respectively.

A comparative study of energy graph-based fault detection and isolation techniques applied to a lignite plant

Energy and exergy interactions in industrial systems hold meaning across physical domains. This paper builds on the notion that capturing the energy and exergy interactions of a system, while retaining physical structural context, enables fault detection and isolation. To this end, three energy graph-based visualisation methods were developed for the purpose of fault detection and isolation. This paper presents a comparative study of the three analysis methods designated the 1) distance parameter method, 2) eigenvalue decomposition method, and 3) residual method. The study utilises data from a physical lignite plant in Janschwalde, Germany, in combination with simulation data of specific faults in order to compare the sensitivity and robustness of the three methods. The comparison is done firstly in terms of detection and secondly in terms of isolation. The distance parameter and eigenvalue decomposition methods showed high sensitivity and robustness for fault detection, while the residual method showed moderate comparative performance. In terms of fault isolation, the distance parameter method showed high sensitivity and robustness, while the eigenvalue decomposition method had irregular isolation performance. The residual method isolation results proved inconclusive.

Permanent fault identification and location scheme for MTDC grid based on equivalent impedance phase characteristics and distributed parameter line

To ensure the reliability of the MTDC grid's recovery system during the reclosing stage, identifying permanent faults is crucial. Therefore, a permanent fault identification and location scheme is proposed based on equivalent impedance phase characteristics and distributed parameter lines. The equivalent impedances with distributed parameter lines in permanent and temporary faults are investigated. The proposed scheme for identifying a permanent fault is based on the phase characteristics of the equivalent impedances. The proposed scheme for permanent fault location is achieved by solving the fault location equation considering distributed parameter lines to determine the fault distance. Simulation results indicate that the proposed scheme is suitable for long-distance DC transmission lines and is less affected by the sampling rate, fault locations, fault resistance, and noise. The proposed scheme improves the reliability and sensitivity of permanent fault detection.

A Novel Data-Driven Fault Detection Method Based on Stable Kernel Representation for Dynamic Systems.

With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.

Fault detection sensitivity analysis and optimization of the few-mode fiber link under a dynamic spatial mode crosstalk cumulative effect.

Due to the cumulative effect of dynamic spatial mode crosstalk, it is difficult to obtain the amplitude distribution of backscattering with high purity. Hence, the fault detection sensitivity (FDS) of the few-mode fiber (FMF) link is deteriorated, and the fault location accuracy is limited. This work investigates the characteristics and optimization method of FDS for the FMF link under the dynamic spatial mode crosstalk cumulative effect. We establish a mathematical model and analyze the influence of dynamic spatial mode crosstalk on the FDS of LP01, LP11a, and LP11b modes. The crosstalk mentioned above is caused by different splice misalignments and rotation angles during FMF fusion splicing. The results show that the dynamic spatial mode crosstalk has a great influence on the detection sensitivity of the high-order spatial mode. To solve this problem, the FDS optimization method is proposed based on high-order mode waveform reconstruction. The coupling efficiency matrix at the fusion splice point is estimated by the Rayleigh backscattering waveform of each mode, and the Rayleigh backscattering waveform of high-order spatial modes is reconstructed. The simulation experiment is carried out based on the above theoretical model. The results show that the proposed method can effectively eliminate the influence of the cascading dynamic crosstalk accumulation on the fault loss of the high-order spatial mode, and then optimize the FDS of the FMF link. This study offers new opportunities to develop FMF fault detection devices with high detection sensitivity performance.

Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning

Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time–frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the synthetic minority class oversampling technique (SMOTE). The hyperparameters of the evaluated classifiers, namely decision trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Ensemble trees, were optimized using the Bayesian optimization algorithm. The extracted features were reduced using several methods. The classification performance was evaluated in terms of the accuracy, specificity, sensitivity, and precision metrics. The results show that the data balancing improved the specificity of DT, SVM, and k-NN classifiers (DT: from 99.86% for unbalanced data to 100% for balanced data; SVM: from 99.28% for unbalanced data to 99.93% for balanced data; k-NN: from 99.64% for unbalanced data to 99.74% for balanced data). The forward feature selection combined with the Bag ensemble classifier achieved 100% accuracy, sensitivity, specificity, and precision for fault detection (binary classification), while the Adaboost ensemble classifier had the highest accuracy (99.4%), compared to the other classifiers when using the complete set of features. The classification models with the highest performance were further tested using a new dataset test case. They showed high detection and classification capabilities. The proposed approach was compared with the previous methodologies from the literature.

Airgap Search Coil Based Identification of PM Synchronous Motor Defects

Online detection of rotor and load faults in permanent magnet synchronous motors (PMSM) based on spectrum analysis of current or vibration is not capable of identifying the root cause of the fault, since all faults produce identical fault signatures. The sensitivity of fault detection also depends on the motor and controller design making fault detection unreliable. In addition, operation under variable frequency and load limits the effectiveness of spectrum analysis based methods. In this article, airgap flux monitoring is investigated as an alternative for providing reliable identification of rotor and load faults in PMSMs. Based on the analysis of airgap flux under partial and uniform demagnetization, dynamic eccentricity, and load unbalance, an airgap search coil voltage based method for detection and classification of the faults is proposed. The claims made in the article are verified through experimental testing on an IPMSM under emulated fault conditions along with a comparison to vibration and current spectra-based detection. It is shown that the proposed method provides reliable online identification of the faults for cases where conventional spectrum analysis based methods fail.

A distributed adaptive protection scheme based on multi‐agent system for distribution networks in the presence of distributed generations

Distribution networks are operated in different conditions and the traditional protection systems might not be proper for all available operation conditions by using directional overcurrent relaying. Besides, the presence of distributed generations makes the protection setting and coordination more complex, and may even make it impossible to access a reliable protection system. Therefore, a fully distributed adaptive protection scheme based on the multi-agent systems is proposed to improve the reliability, selectivity and sensitivity of relays. It uses only numerical directional overcurrent relays with instantaneous characteristics as agents, so that only need to pick-up settings and react quickly. All agents dynamically update their pickup value to increase the fault detection sensitivity, and make reliable and selective on-board decisions for fault location and isolation. In addition, in order to achieve high sensitivity and mitigate miscoordination, using setting groups of relays, a novel adaptive backup protection scheme, which does not need complex computational coordination, is proposed. The proposed system is performed in the distributed manner to enhance efficiency and robustness against cyber-attacks and one-point failures. The proposed system is tested on the meshed IEEE 14-bus system and radial IEEE 13-bus system with distributed generations to evaluate its performance.

Data‐driven prescriptive maintenance toward fault‐tolerant multiparametric control

AbstractPrescriptive maintenance can improve system effectiveness and system safety via integrated production and maintenance optimization. However due to system disruptions there is potential for abnormal operations and an undesirable increased occurrence of process safety incidents. This research provides a multiparametric‐based framework for safety‐aware, maintenance‐aware, and disruption‐aware process control. It leverages ensemble classification via machine learning classifiers for fault detection, mixed‐integer nonlinear programming for integrated safety‐aware production and maintenance scheduling, as well as hybrid multiparametric model predictive control for fault‐tolerant setpoint tracking. The results show that the ensemble classifier outperforms the individual classifiers in terms of fault detection accuracy, sensitivity, and specificity. Furthermore, it is seen that the developed controllers are able to reconfigure the control actions based on process disruption information. The framework is illustrated with a chemical complex system, and a cooling water system. The approach can be used to help improve the safety and productivity of industrial processes.

Stray Flux Multi-Sensor for Stator Fault Detection in Synchronous Machines

The aim of this paper is to detect a stator inter-turn short circuit in a synchronous machine through the analysis of the external magnetic field measured by external flux sensors. The paper exploits a methodology previously developed, based on the analysis of the behavior with load variation of sensitive spectral lines issued from two flux sensors positioned at 180° from each other around the machine. Further developments to improve this method were made, in which more than two flux sensors were used to keep a good sensitivity for stator fault detection. The method is based on the Pearson correlation coefficient calculated from sensitive spectral lines at different load operating conditions. Fusion information with belief function is then applied to the correlation coefficients, which enable the detection of an incipient fault in any phase of the machine. The method has the advantage to be fully non-invasive and does not require knowledge of the healthy state.

Fault detection and isolation of gas turbine using series–parallel NARX model

This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series–parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve the fault detection robustness against uncertainties, the Monte Carlo technique is used in the proposed fault detection structure to select the best threshold. The analysis of fault detectability and fault detection sensitivity are accomplished to analyze the performance of the suggested technique. The fault isolation process is also achieved by using the residual classification approach. The results show the feasibly, robustness, and performance of the presented approach for fault diagnosis of nonlinear systems in the presence of uncertainties.

Fault detection sensitivity enhancement based on high-order spatial mode trend filtering for few-mode fiber link.

In this paper, we propose and experimentally verify a method for optimizing the fault detection sensitivity of few mode fiber (FMF) link based on high-order spatial mode trend filtering. The employment of high-order mode trend filtering as a signal processing tool identifies meaningful level shifts from FMF optical time-domain reflectometer (FMF-OTDR) profile, which is associated with the problem of the minimization of the intrinsic random noise and modal crosstalk impact on the acquired data. A FMF link fault detection system is built, and the proposed method is utilized to detect the fault loss characteristics of 7.2 km 6-mode fiber with three fusion splice points with different fusion quality, and the detection results of each mode are compared with the results obtained by FMF-OTDR. The experimental results show that our proposed method can effectively improve the low fault detection sensitivity of high-order spatial mode caused by random noise and mode crosstalk.

A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.