Fault Model Research Articles

Data‐driven modelling for online fault pre‐warning in thermal power plant using incremental Gaussian mixture regression

AbstractThis study introduces a data‐driven model for online fault pre‐warning in thermal power plants using incremental Gaussian mixture regression. To tackle the issue of outdated parameters in existing fault pre‐warning models, this study puts forth an incremental Gaussian mixture regression that leverages the merging of Gaussian components to reconstruct the model and enable online modelling. Due to its criticality, a forgetting factor is introduced during the merging process to efficiently manage the weight allocation between present and historical patterns, thereby guaranteeing the model's accuracy. The results of the sine function case demonstrate that the incremental Gaussian mixture regression (IGMR) model exhibits excellent pattern control performance and modelling efficiency. Furthermore, the IGMR model is employed to forecast parameter alterations in pulverizer blockages with mode switching, and experimental validation indicates that IGMR precisely anticipates parameter changes following mode switching. Compared to on‐site solutions, the pre‐warning of coal blockage has a clear advantage in advance warning.

Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation.

A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault prediction model for the first time. Furthermore, an embedded platform for intelligent fault diagnosis and prediction was developed using an Application Specific Integrated Circuit chip. The results show that the simulation model of the DEH system has good performance. A jam fault, internal leakage, and a device fault could be accurately identified through the fault diagnosis model. The multi-parameter gray error neural network prediction model improves the accuracy of fault prediction. The embedded platform developed by the Application Specific Integrated Circuit chip solves the problem of transmission limitation and insufficient computing power. It realizes the intelligent diagnosis and prediction of DEH system faults and guarantees the regular operation of the DEH system.

Анализ напряженно-деформированного состояния моделей разрывных нарушений

The paper analyzes the procedure of creating three-dimensional models of discontinuous and tectonic faults for numerical modeling problems using the finite element method. A number of uncertainties arise in the geomechanical analysis of the stress-strain state of faults due to the choice of the type of their computational geological model, e.g. the dislocation, domain or dynamic impact zone models. A comparative analysis of the stress-strain state in the mentioned computational geological models is performed. The geological and structural models are based on the geological sampling data collected in the investigated area. Special attention is paid to building the structural-tectonic model with account of discontinuous and tectonic faults that affect the deformations and stresses of the rock mass. As the result, the most preferable of the three given options is the computational geological model with the dynamic impact zone. The domain of the dynamic impact zone with lower physical and mechanical properties makes it possible to cover the entire potential volume of the rupture zone.

Variation Parameters of The 2004 Indian Ocean Tsunami Model for Tsunami Prone Area Mapping in The Northern Part of Aceh Province

A total of ten tsunami events occurred in Aceh Province from 1991 to 2012. This condition confirms that AcehProvince is very vulnerable to tsunami hazards. In 2004, a tsunami occurred due to the activity of the subduction zone onthe west coast of Aceh with a magnitude of Mw 9.3. Since the 2004 tsunami, research on tsunamis has increased. The aimof this study is to reconstruct the 2004 tsunami models, to find out which models are suitable for the events of 2004. Thereare at least five models of earthquake source faults used for tsunami modeling in this study. The tsunami modeling wascarried out numerically using the Tohoku University’s Numerical Analysis Model Investigation (TUNAMI) programme.The maximum height obtained from the modeling is 58,05 m at the shoreline. The maximum height obtained from thebest model is 17,73 m on land. The M5 fault model produced a tsunami height model that best matches the observationresults validated by lowest RMS and highest correlation coefficient values Keywords: tsunami height, fault model, correlation coefficient, RMSE

Event‐triggered security control for fuzzy‐model‐based cyber‐physical systems under Denial‐of‐Service attacks and actuator faults

AbstractThis article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results.

Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.

Testing and incremental conformance testing of timed state machines

We present methods for testing and incremental testing of systems modeled as finite state machines with timeouts (TFSMs). For testing, we establish an appropriate fault model and show how a complete test suite can be derived for a given TFSM specification using traditional FSM-based test derivation approaches considering an untimed FSM abstraction of the given specification. In addition, we consider reducing the cost of testing a modified or an evolving TFSM specification by the selection of appropriate incremental test suites that can verify whether the modified parts of a modified specification are correctly implemented in a corresponding implementation under test. In particular, we define the incremental testing problem for TFSMs and investigate appropriate fault models that can be used for incremental test derivation and accordingly propose related test selection algorithms. According to conducted experiments length and run time (sum of time delays) of obtained test suites are much lower than their theoretic upper bounds; in some cases, these bounds are linear. In addition, for incremental testing, when the modified part is up to 20% of the whole specification, length and run time of incremental test suites are at least twice as less than those obtained using the whole modified specification.

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.

3D geometry and displacement transfer of an oblique relay zone on outcropping normal faults

Relay zones on normal faults accommodate transfer of displacement between adjacent fault segments. We study vertical and horizontal displacement transfer mechanisms across a relay zone adjacent to the Moab Fault at Courthouse Rock (Utah, USA). The relay zone has a reservoir scale (map overlap length ca. 750 m, separation ca. 150 m), and is bounded by two normal fault segments with maximum throw of ca. 12 m. The relay zone is exposed on multiple, sub-parallel cliff faces and intervening rock pavements. We use photogrammetry of these exposures to build a 3D virtual outcrop model of the relay-bounding faults, subordinate faults deforming the relay zone and seven faulted stratigraphic horizons. The relay-bounding faults are right-stepping in map view and contractional in cross-section, defining a relay zone oblique to bedding in 3D. Displacement is transferred both horizontally and vertically across the relay zone. Horizontal transfer of displacement is achieved by a shallow dipping (<1°) relay ramp, whereas vertical transfer is achieved by antithetic faults within the relay ramp. Our analysis demonstrates that multiple mechanisms can work in conjunction to facilitate transfer of displacement across individual relay zones.

Modeling and analysis of rotating shaft with bearing faults based on transfer matrix method

Some mechanical systems, such as drilling machines, hydroelectric/nuclear power plants, and rotating machinery parts, are usually including a rotating part. When operating such systems, bearing faults frequently occur, causing system malfunctions. In order to prevent them, continuous system diagnosis with early warning should be required. To realize this, the analysis of fault signals should be performed to understand the features of fault states. However, experimental data with faults is limited. Therefore, the analysis should be performed along with appropriate numerical modeling to compensate for the limitations. Thus, in this research, bearing-shaft modeling with an outer fault is proposed in order to perform signal analyses for the bearing fault states. The overall shaft system is modeled based on the transfer matrix method (TMM), and the bearing is modeled using the lumped parameter method with outer fault modeling. With the proposed method, it has been shown that it is possible to analyze a situation where a bearing has failed.

A probabilistic neural network with adaptive capability to optimize fault trees for the intelligent fault diagnosis of rapier loom throughout its operating cycle1

The mechanical structures of the rapier loom are strongly coupled, resulting in faults that are characterized by strong coupling, hierarchy, phase dynamics, and a transient nature. However, current fault diagnosis methods using a single approach are not satisfactory. Additionally, fault diagnosis of the entire operation cycle of the rapier loom equipment is lacking. This paper proposes a fault tree diagnosis method with probabilistic neural network optimization to build a complete fault diagnosis system for rapier looms and improve their intelligent diagnosis capability. The method has strong fault tolerance and self-adaptive capability, allowing for accurate location of the root cause of the fault from multiple fault sources. By accumulating fault samples and continuously improving the diagnosis network, the accuracy of diagnosis can be further enhanced. Initially, the failure mechanism of key subsystems of rapier loom is analyzed. A fault tree model is established for each subsystem based on expert experience and historical data. The model identifies the characteristic sign quantities of typical fault types and serves as the basic input for fault identification. A probabilistic neural network is used to train the fault sample set and complete the diagnosis of the cause of the fault. According to field experiments, the proposed method has demonstrated a significant improvement in the efficiency of locating and identifying fault signs in rapier looms. This improvement allows for accurate and quick identification of faults.

Upper Plate and Subduction Interface Deformation Models in the 2022 Revision of the Aotearoa New Zealand National Seismic Hazard Model

ABSTRACT As part of the 2022 revision of the Aotearoa New Zealand National Seismic Hazard Model (NZ NSHM 2022), deformation models were constructed for the upper plate faults and subduction interfaces that impact ground-shaking hazard in New Zealand. These models provide the locations, geometries, and slip rates of the earthquake-producing faults in the NZ NSHM 2022. For upper plate faults, two deformation models were developed: a geologic model derived directly from the fault geometries and geologic slip rates in the NZ Community Fault Model version 1.0 (NZ CFM v.1.0); and a geodetic model that uses the same faults and fault geometries and derives fault slip-deficit rates by inverting geodetic strain rates for back slip on those specified faults. The two upper plate deformation models have similar total moment rates, but the geodetic model has higher slip rates on low-slip-rate faults, and the geologic model has higher slip rates on higher-slip-rate faults. Two deformation models are developed for the Hikurangi–Kermadec subduction interface. The Hikurangi–Kermadec geometry is a linear blend of the previously published interface models. Slip-deficit rates on the Hikurangi portion of the deformation model are updated from the previously published block models, and two end member models are developed to represent the alternate hypotheses that the interface is either frictionally locked or creeping at the trench. The locking state in the Kermadec portion is less well constrained, and a single slip-deficit rate model is developed based on plate convergence rate and coupling considerations. This single Kermadec realization is blended with each of the two Hikurangi slip-deficit rate models to yield two overall Hikurangi–Kermadec deformation models. The Puysegur subduction interface deformation model is based on geometry taken directly from the NZ CFM v.1.0, and a slip-deficit rate derived from published geodetic plate convergence rate and interface coupling estimates.

Self-Test Library Generation for In-Field Test of Path Delay Faults

New semiconductor technologies for advanced applications are more prone to defects and imperfections related, among several different causes, to the manufacturing process, aging and cross-talks. These phenomena negatively affect the circuit’s timing and can be effectively modeled by means of the path delay fault (PDF) model. While path delay testing is currently supported by commercial Automatic Test Pattern Generation tools for scan designs, functional testing covering PDFs is not widely adopted, mainly because of the high cost for test generation. On the other side, functional test is already widely adopted for in-field test of stuck-at faults, which is often performed resorting to the execution of suitable test programs (Self Test Libraries, or STLs). This approach is attractive, since it can be performed at-speed with limited time constraints and high flexibility, making it a suitable in-field test solutions. Previous work assessed the feasibility and validity of functional approaches based on test programs targeting PDFs. In this work, we present the first systematic method for the development of very high fault coverage test programs for PDFs, which largely outperform test programs written for other fault models. Moreover, the proposed method allows the identification of functionally untestable faults. The effectiveness of the proposed approach was proven on an open-source RISC-V processor core, where 100% coverage of the functionally testable longest paths was achieved, compared with an initial coverage of 0.52% achieved with test programs targeting stuck-at faults. Results demonstrate that shorter paths are also effectively covered.

PWM Voltage-Based Modeling for PM Machines With Interturn Short Circuit Fault Considering the Effect of Drives

This paper presents a novel analytical fault model based on PWM voltage rather than ideal sinewave phase voltage for permanent magnet (PM) machines with inter-turn short circuit fault. This model is important because PM machines are usually driven by the pulse width modulation (PWM) inverter. In addition, main contributors to the accuracy of the fault model such as (i) the cables between the inverter and the machine, (ii) the precision of the rotor position estimation and (iii) the inverter, in particular the metal-oxide semiconductor field-effect transistors (MOSFETs) have been considered. Therefore, this developed model can fully account for the effects of PWM harmonics and accurately predicts the machine's behaviors (particularly the fault current) under fault conditions considering the effect of the drives. This has rarely been studied in the literature as most existing fault modelling methods adopt sinewave phase voltage as inputs, which cannot account for the PWM harmonics in the fault currents. However, the investigations in this paper showed that the PWM harmonic component could be close to 25% of the fundamental component in the fault current, and hence cannot be neglected. The accuracy of the proposed fault model has been validated by a series of experimental tests.

A Differential Fault Attack on Security Vehicle System Applied SIMON Block Cipher

With the application of new technologies in vehicles such as wireless communications, microelectronics, Artificial Intelligence (AI) and Autonomous Vehicles (AV), there are lots of embedded hardware and software in vehicles, and so cryptographic technologies such as block ciphers are also applied to the vehicle system to protect its security and privacy. In order to better find potential security vulnerabilities and improve security of the vehicle system which applied SIMON block cipher, in this paper, a novel Differential fault attack (DFA) method is presented. By inducing a random bit fault in <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$L^{T-4}$</tex> </formula> (the fourth round from the last), we first demonstrate the process of identifying the fault-induced position. Then, on the basis of the determined position, we describe how to retrieve three rounds of keys by inducing only one round of faults. For retrieving the SIMON family, compared with previous DFAs under the random bit fault model, the lowest numbers of fault-induced and fault locations (or rounds) are required by our attack. In particular, when the key words m is 2 or 3, the proposed attack only needs to induce one round of faults. Finally, we carry out simulation verification and a comparison to illustrate the correctness and effectiveness of the proposed attack.

Modeling of Asymmetrical Grid Faults for P-HIL Accuracy and Stability Analysis in LVRT Tests

Modeling of Asymmetrical Grid Faults for P-HIL Accuracy and Stability Analysis in LVRT Tests

A mathematical analytical modeling method for rotor eccentricity fault of large VSPSU

Aiming at the rotor eccentric fault modeling problem of large VSPSU (VSPSU), a mathematical analytical modeling method based on air gap magnetic field analysis is proposed. This method uses Fourier series decomposition to represent the air gap permeability of static and dynamic eccentricity faults. Then, taking a single coil as a unit, the eccentric self-inductance coefficients between each branch of the windings are derived. Furthermore, the mathematical analytical modeling of the eccentric fault is completed by combining the voltage and flux formulas of each winding branch. The model can reliably reflect the typical fault characteristics of large VSPSUs with eccentric faults, which can provide an analysis basis for subsequent relay protection research.

Fault identification and classification algorithm for high voltage transmission lines based on a fuzzy-neuro-fuzzy approach

ABSTRACT Traditional techniques are used for fault location detection in high-voltage transmission lines that mostly depend on traveling waves and impedance-based techniques suffer from large errors owing to the intricacy of fault modeling for various types of faults. Although single-line to ground faults are dominant in high-voltage transmission lines, fault resistance as well as fault inception angle might distort the current fault detection techniques. In addition, other types of faults exist and that raises the need to develop an accurate fault detection technique to minimize the recovery time. The current paper introduces a fuzzy and neuro-fuzzy algorithm to detect, analyze, and locate different faults taking place in high-voltage transmission lines. A MATLAB Simulink Model is used for analyzing different fault cases; fault detection and classification are done by the Fuzzy Interface System (FIS), while fault location detection is done using the Adaptive Neuro-Fuzzy Interface System (ANFIS). The introduced algorithm is evaluated via the Mean Square Error (MSE) technique. The results showed full success in detecting and identifying different fault types, with a 0.0042 validity performance factor for fault location detection using ANFIS.

Set-based fault diagnosis for uncertain nonlinear systems

Automated fault diagnosis algorithms aim to identify the root cause of a fault after it is detected, which is crucial for determining a safe and effective response. Set-based methods are attractive for this task due to their unique ability to provide formal guarantees that the observed data is inconsistent with candidate fault models. However, such diagnosis methods have scarcely been applied to nonlinear systems because nonlinearity often makes the required inconsistency tests either very conservative or very computationally demanding using existing techniques. This paper proposes a new set-based fault diagnosis algorithm for nonlinear systems and demonstrates improved diagnosis speed and accuracy compared to existing set-based methods. The method tests for inconsistency using a new set-based observer enabled by recent advances in discrete-time reachability analysis.

Experimental EMFI detection on a RISC-V core using the Trace Verifier solution

Physical attacks are powerful threats that can cause changes in the execution behavior of a program. Control-Flow Integrity (CFI) is used to check the program’s flow execution, ensuring that it remains unaltered by these attacks. The RISC-V Trace Encoder (TE) provides valuable information about the user program’s execution path, and is used as part of a CFI solution. An enhanced version of the TE specifications permits detecting intricate fault models such as the corruption of any discontinuity instruction, using an additional Trace Verifier (TV) hardware module. In this paper, we present a buffer overflow software attack simulation and experimental ElectroMagnetic Fault Injection (EMFI) attacks conducted on an Field Programmable Gate Array (FPGA) board that implements a RISC-V core linked to the enhanced TE and TV modules. Unlike existing CFI solutions, our proposed approach does not require modifications to the RISC-V compiler, user application code or the RISC-V core. The average overhead of our solution in terms of hardware area, memory and power consumption are equal to 13.6%, 3.5% and 9% respectively.