Actual Fault Research Articles

Time–frequency characteristics and influencing factors of the pressure wave caused by power–frequency arc inside a closed oil tank

AbstractThe arc faults inside oil‐immersed power equipment can produce high‐amplitude pressure waves inside tanks, which might cause ignition and explosion accidents. This kind of failure is one of the most severe faults for power equipment and has attracted considerable attention in recent years. However, due to the high risk of the experiments and the complex development of arc in oil, the characteristics of the pressure wave formed by the arc are still confusing. In this paper, the time–frequency characteristics of pressure waves are analysed using several experiments of 1–8 kA power–frequency arc inside a closed oil tank. The experimental results show that the pressure wave produced by the arc in oil contains three frequency bands, 0–500 Hz, 500 Hz–40 kHz, and above 40 kHz, which are related to the arc energy, the average current around arc ignition and the metal wire explosion respectively. This helps further understand the formation mechanism of the pressure wave caused by the arc in oil. This paper discusses the influences of ignition wire on arc‐formed pressure waves. A wire diameter selection method for arc experiments is established to reduce the pressure differences between wire‐ignited arcs and actual arc faults.

Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value.

Typhoon related cascading fault chain dynamic evolution model and risk mitigation in distribution systems

The resilience of distribution system is severely influenced by typhoon disasters and the secondary disasters such as floods and debris flows. The cascading propagation of typhoon disasters, coupled with the cascading propagation of faults in distribution systems, creates a dual-coupling dynamic that results in large-scale faults. The key to mitigating losses from typhoon-related cascading faults lies in understanding the potential paths of cascading propagation and taking corresponding measures to preemptively interrupt the chain propagation. In this paper, based on the analysis of actual typhoon related fault data of Guangzhou, China, we created the knowledge graph of the typhoon related cascading fault chains and modeled the chain formation mechanism, successfully integrating the distribution systems and the typhoon disaster propagation systems. We also achieved the dynamic evolution of typhoon related cascading fault chains by using system dynamics. In the case studies of Guangzhou, we selected three typical typhoon related fault scenarios, and then the proposed model is utilized to capture the fault cascading pathway, which can help mitigate typhoon related fault risks in distribution systems.

Fault detection observer design for T-S fuzzy systems based on membership function-dependent mixed H−/H∞ performance in the finite-frequency domain

This paper focuses on the design of a fault detection observer for T–S fuzzy systems with faults and unknown disturbances, based on a membership function-dependent [Formula: see text] performance index. Currently, common methods for disturbance suppression and fault detection are based on traditional [Formula: see text] performance criteria. Traditional [Formula: see text] performance indices impose the same constraints on the entire system, which does not align with the characteristic that T–S fuzzy systems do not operate continuously in all linear subsystems. Therefore, aiming to improve the performance of the linear subsystems where the system operates for long periods, this paper proposes a membership function-dependent [Formula: see text] performance index and designs a fault detection observer based on this performance index. In addition, considering that the frequency of actual system faults and disturbances is not across the entire frequency domain, the design of the fault detection observer based on the membership function-dependent [Formula: see text] performance index is achieved through the generalized Kalman-–Yakubovich-–Popov lemma in a finite-frequency domain.

Underground XLPE coaxial cable length calculation and fault detection based on broadband impedance spectroscopy

Abstract Accurately determining the tested cable’s total length is important in cable fault detection and localization. Therefore, an iterative method of relative propagation coefficients based on broadband impedance spectroscopy is proposed to solve the actual length of the cable and a phase difference integral transform method for fault detection. First, the overall detection process framework is designed. Then, the cable distribution parameter model and the characteristics of the input impedance spectroscopy are analyzed. The calculation methods for determining the cable length and propagation coefficients are explained, followed by a demonstration of the fault localization process. Finally, the model LCR1000A impedance analyzer is used to measure cable length and actual faults in cables with lengths of 35 m, 100 m, and 500 m. The final fault location error is less than 0.67%, proving that the method can calculate the length of cables and various fault point locations.

Shale Fracture Static Stability Evaluation Method Based on 3D Discrete Fracture Model: A Case Study on the Luzhou Area in Southern Sichuan Basin, SW China

Abstract The stress interference in deep shale may lead to shale fracturing and instability and consequently results in engineering risks such as casing deformation in horizontal wells. Therefore, in the early stages of shale gas exploration and development, it is of great significance to evaluate the stability of shale fractures in advance. The stability evaluation of shale reservoir faults can be mainly divided into two categories at present: quantitative evaluation of single faults and qualitative evaluation of wide-range faults. The quantitative evaluation of single faults primarily relies on numerical simulation techniques, while the qualitative evaluation of wide-range faults primarily employs geophysical methods. Both methods are difficult to meet the needs of shale gas exploration and development. The shale fracture stability evaluation method based on 3D discrete fracture model which is developed in this article can quantitatively evaluate the static stability of wide-range shale fractures in the early stage of exploration and development. In this article, seismic geometric attributes were calculated, and fracture seismic facies was established by means of the Bayesian probabilistic cluster analysis method. Then, 3D discrete fracture modeling under the constraint of fracture seismic facies was performed to establish a discrete fracture model. Finally, according to the Mohr–Coulomb criterion, the shear stress and effective normal stress of discrete fracture were calculated by using the formation pressure data in other exploration and development results and the regional in situ stress obtained through seismic prestack inversion. And thus, the stability evaluation of the fractures in shale reservoirs was eventually completed. In addition, this method was verified by using the casing deformation points in some actual shale-gas horizontal wells, the actual small fault points, and microseismic data. It indicates that the method has higher accuracy and is better applicable to the early shale gas exploration and development.

Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition-Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy.

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition-stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.

Simulation inversion analysis of simultaneous commutation failure of Zhaoyi and Yindong DC

Abstract Aim at the three times simultaneous commutation failure of two Multi-outfeed and Multi-infeed DC Systems in a Similar Path of Ningxia, the Ningxia power grid model is built in Power System Analysis Software Package (PSASP) with actual parameters, and the accident inversion simulation is carried out based on the operation state of the power grid before the fault. Based on the inversion simulation, the simulation results were compared with the measured PMU data. The results showed that the trend of simulation and the measured curve is consistent, and the simulation was slightly more serious than the actual fault, but the overall error was less than 10%, which verified the accuracy of the simulation model and method, and provided a basis for the subsequent simulation analysis of the power grid operation mode.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background.

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

Research on Gate Charge Degradation of Multi-Chip IGBT Modules in Power Supply for Unmanned Aerial Vehicles

In recent years, with the burgeoning application of high voltage in various industrial sectors, the deployment of unmanned equipment, such as industrial heavy-load Unmanned Aerial Vehicles (UAVs), incorporating high-capacity Insulated-Gate Bipolar Transistors (IGBTs), has become increasingly prevalent. The demand for high-voltage IGBT modules in UAV is continuously growing; therefore, exploring methods to predict fault precursor parameters of multi-chip IGBT modules is crucial for the operational health management of unmanned equipment like UAVs. This paper analyzes the gate charge degradation in multi-chip IGBT modules after thermal cycling, which can be used to evaluate the operational state of these modules. Furthermore, to delve into the electrical response of a gate drive circuit caused by local damage within the IGBT module, an RLC model incorporating parasitic parameters of the gate drive circuit is established, and a sensitivity analysis of the peak current in the gate charge circuit is provided. Additionally, in the experimental circuit, an open sample of an IGBT module with partial bond wires lifted off is used to simulate actual faults. The analysis and experimental results indicate that the peak current of the gate charge is closely related to L and C. The significant deviation in the gate current, influenced by the partial bond wires lift-off, can provide a basis for the development of predictive methods for IGBT modules.

Traveling wave network location method based on adaptive waveform similarity

High-voltage transmission technology can effectively address the issue of uneven spatial and temporal energy distribution, leading to its rapid development in recent years. To address the challenge of accurately identifying the source of the second traveling wave head in complex transmission network scenarios with existing single-ended fault location methods, a traveling wave network fault location method based on adaptive waveform similarity is proposed. The paper analyzes the propagation process of traveling waves in transmission lines and quantitatively derives the time-domain expression of the traveling wave waveform. The BFS algorithm is enhanced by incorporating the propagation characteristics of traveling waves, allowing for the determination of all paths from any location in the topological network to the measurement points. Based on the path information and the derived expression, the traveling wave waveform at the measurement points for the fault location is calculated. An optimization algorithm is used to iteratively solve for unknown parameters such as fault location, traveling wave speed, and fault point information, with the objective of maximizing the similarity between the adaptive waveform and the real waveform by adaptively adjusting the waveform shape. When the similarity between the adaptive waveform and the real waveform is maximized, the adaptive fault location is identified as the actual fault location. Verified through the PSCAD simulation platform, this method can achieve accurate location under different fault conditions.

Analysis of Fault Events in Rail Transit Vehicle Traction Systems Based on Knowledge Graph Reasoning

Fault text records provide detailed information on faults and handling steps, which are valuable for fault analysis. However, the different individuals’ recording styles can lead to ambiguities, and it is challenging to uncover potential fault associations in complex systems. To address these issues, this paper proposes a novel method for fault information extraction and analysis. Firstly, to tackle the problem of ambiguous boundaries between entities in fault texts, an integration algorithm is employed to accurately recognize fault entities considering contextual semantic features to establish fault knowledge graph (FKG). Then, a Relational Graph Convolutional Networks (RGCN) is improved with Graph Attention Networks (GAT) for sparse nodes caused by specific types of faults, to dynamically adjust the weight distribution of node learning, inferring potential links within the graph. The proposed method was validated using actual fault records from the traction system of rail transit vehicles, and contributes a reference for the mining and analysis of fault records in complex systems.

Research on the HHO improved BP neural network fault prediction algorithm for distribution networks based on weather factors

Abstract Weather factors have a substantial impact on the operation and maintenance of the distribution network, but the current system fault prediction platform mainly focuses on the operating characteristics of the system, that is, the internal parameters, and rarely considers the external parameters. Firstly, by studying the fault data of the distribution network in a city in northern China caused by weather factors, this paper obtains the main weather factors affecting the reliability of power supply in this area. Then, the Harris Hawks optimization (HHO) improved BP neural network algorithm is used to establish the main reliability evaluation models suitable for this region, and based on this, the fault probability of this region is predicted under the target weather. Finally, the prediction results are compared with the actual fault data caused by weather factors in this area. The results show that both the PSO-BP algorithm and HHO-BP neural network algorithm achieve better prediction accuracy than the BP neural network algorithm, and the HHO-BP algorithm has better convergence speed.

Intelligent fault diagnosis for unbalanced battery data using adversarial domain expansion and enhanced stochastic configuration networks

An accurate and efficient fault diagnosis method for battery systems is crucial to ensuring the safety of battery packs. Addressing the issue of insufficient actual fault data in battery operations, this paper proposes an intelligent fault diagnosis method based on feature-enhanced stochastic configuration networks and adversarial domain expansion of imbalanced battery fault data (AFDEM-FESCN). Firstly, we designed an adversarial fault domain data expansion method (AFDEM). By learning the distribution of fault data through adversarial training, the distribution of sample domains is balanced, thereby reducing model bias. Subsequently, we adjusted the distribution of SCN iterative parameters and added a linear feature layer. This enhances the feature extraction capability of the network through distribution overlay, enabling fault diagnosis. Finally, the effectiveness and feasibility of the proposed method were validated through a practical battery system fault diagnosis case, achieving a diagnostic accuracy of 92.1%. Experimental results demonstrate that the AFDEM-FESCN method exhibits good accuracy in battery system fault diagnosis, providing an effective solution to the challenge of imbalanced data in intelligent fault diagnosis.

Analysis of interturn short circuit in regulating winding of power transformer based on field-circuit coupling

The interturn short circuit fault is one of the common faults in power transformers. At present, research on interturn short circuit faults focuses on high, medium, and low voltage windings, while there is relatively little research on interturn short circuit in regulating windings. Specifically, there is a lack of reported studies on the transient electromagnetic processes, magnetic field distribution, and electromagnetic force characteristics of interturn short circuits in regulating windings unconnected to the circuit. This study presents an actual fault scenario involving interturn short circuits occurring in the untapped portion of the regulating windings of a specific power transformer. A field-circuit coupled model was established to analyze the transient electromagnetic processes during the fault, and the model’s effectiveness was validated by comparing its results with actual fault recording data. Additionally, the magnetic field distribution and electromagnetic force characteristics during the fault were analyzed, and discussions were carried out regarding various ratios of short-circuit turns in the regulating windings. The results indicate that even when an interturn short circuit occurs in the portion of the regulating winding that is not connected to the circuit, the current in the short-circuited turns can reach several tens of times the rated value. Additionally, the leakage magnetic field and the electromagnetic force experienced by the short-circuited ring also increase significantly. The short-circuit ratio has a significant impact on the current of the short-circuited ring, leakage magnetic field intensity, and electromagnetic force. This study contributes to a better understanding of the impact of interturn short-circuit faults in the untapped portion of the regulating windings, offering crucial technical support for fault diagnosis and prevention of power transformers.

TFASOT for bearing damage feature extraction and enhancement under complex working conditions

Abstract In some complex working conditions, such as variable speeds and variable loadings, events like bearing skidding and/or contact angle changing may happen during the operations. In such cases, order tracking based on the shaft speed, i.e. the first order tracking (FOT), can no longer completely eliminate the smearing effect on spectrum, and thus affects the bearing damage detection precision and accuracy. The existing second order tracking is unable to resolve these issues either in some cases. To resolve these problems, a new approach called time frequency analysis based second order tracking is developed in this paper. Based on the FOT, short-time Fourier transform and synchro-reassigning transform are used to obtain the actual fault characteristic order (FCO) information. Then the FCO is extracted by ridge searching approach to set new synchronous resampling clock. Finally, synchronous resampling and order tracking are combined to correct and enhance the bearing damage feature, resulting in more accurate damage feature extraction. The effectiveness of proposed method is verified by numerical simulations and practical examples as well. The results show that the proposed method can effectively correct the dispersion and deviation of the FCO induced by speed variations as well as operation condition variations, and thus significantly improve the damage feature extraction.

A computational topology-based method for extracting fault surfaces

Fault surface extraction is a crucial step in seismic interpretation, which can help structural interpretation and structural modeling. A key focus of fault surface extraction research is to extract fault surfaces in their entirety as much as possible, rather than just in fault segments, which is more challenging in some complex fault situations. To address this challenge, we develop a fault surface extraction method based on computational topology to extract fault surfaces in their entirety as much as possible from a fault attribute and effectively handle some complex fault situations, such as intersecting faults. From a given seed point on the target fault, we use the idea of regional growth to search for high-confidence points on the target fault, called fault control points, under the constraints of the fault attribute and the calculated fault orientations. Through these fault control points, we extract the fault boundary and process the fault attribute so that only the target fault is included. Furthermore, we use an operation in computational topology called collapse to extract the target fault from the processed fault attribute using the fault boundary as a constraint. By incorporating fault orientation information and using a relatively large search distance during the control point search, our method enables the integration of segmented faults and facilitates the handling of complex fault situations such as intersecting faults. The collapse operation ensures that the extracted fault surfaces align with the fault attribute, correspond to the actual fault locations in seismic data, and enhance fault continuity. In addition, we develop an automatic method for picking seed points to realize the extraction of all the faults in the research data. We test our method on several field data sets and the experimental results demonstrate its effectiveness. In some complex fault situations, such as intersecting faults, our method performs well and indicates a significant improvement over the compared method.

Dynamic modeling and verification of rotating compressor blade with crack based on beam element

Aero-engine blades may crack under harsh operating environments. This reduces the reliability of the aero-engine and causes catastrophic accidents. Although dynamic modeling and vibration characteristics of simplified blades with cracks have been analyzed extensively, there are few studies on the actual compressor blade with varying sections and twisted shapes. Based on Timoshenko beam theory, a new modeling approach for the compressor blade is proposed. Next, according to the strain energy release rate and Castingliano theory, the dynamic model of cracked compressor blades is established considering the stiffness nonlinearity induced by the breathing crack. The modeling approach is validated by the measured natural frequencies of an intact blade. Then, the dynamic model of the cracked blade is verified by comparing the modal characteristics and vibration characteristics using ANSYS software. Finally, some results show that the natural frequencies fn1, fn2, and fn4 increase as the pre-twisted angle and stagger angle increase. Due to the breathing effect, some integer frequencies appear in the spectrum including 0fe and 2fe, and the variations in the amplitude of frequencies 0fr and 2fr are monotonic under different aerodynamic amplitudes. The research can provide some help for the modeling of the actual blade and fault diagnosis of cracked blades.

Research on the optimization of belief rule bases using the Naive Bayes theory

The belief rule base is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule base for fault diagnosis, multiple antecedent attributes are often initially determined by domain experts. Multiple fault symptoms related to multiple antecedent attributes are different when an actual fault occurs. This leads to multiple antecedent attributes matching with multiple fault symptoms non-simultaneously, thereby resulting in a fault diagnosis lacking timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. First, a fault sample is taken in a long enough window and divided into several interval samples, making the analysis samples approximate the overall samples. Second, using Gaussian mixture clustering and Naive Bayes optimization, iteration is performed over the threshold and limit values of fault symptoms in the belief rule base based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule base is optimized. Using fault samples from high-pressure heaters and condensers, the validation results show that there is a there is a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule base.

An [formula omitted] non-fragile fault detection observer design for discrete-time Takagi–Sugeno fuzzy systems using zonotopic method

In practical engineering applications, as the system becomes complex, the need to ensure its safety and reliability increases, and fault detection technology is the key to determining whether it usually operates. The assumption of precise realization implicit in the design of conventional observers is a significant reason why actual fault detection observers find it difficult to detect faults accurately. False alarms can quickly occur during fault detection due to the observer’s uncertainty. For this reason, a novel fault detection method using zonotopic analysis based on a non-fragile H∞ observer via T-N-L structure is proposed for the discrete-time Takagi–Sugeno (T–S) fuzzy system. The observer proposed in this paper is designed in a two-step form. Firstly, the H∞ technique gives a set of design conditions for the fault detection observer with T-N-L structure, which maintains the robustness of the observation dynamics to disturbances while considering the uncertainties existing in the actual observer. Then, the dynamic fault detection thresholds in the form of intervals are given by zonotopic analysis, which has the advantage of zero false alarm rate when the observer parameter ingress is within the range of design considerations. Simulation results show that the method proposed in this paper provides better safety and reduces the problem of missed false alarms compared with the traditional method. It also reflects that the nature of a non-fragile fault detection observer is a trade-off between safety and speed of fault detection.