Potential Faults Research Articles

An Innovative Technique for Fault Analysis of Electric Automated Vehicles

A serious accident might occur as a direct result of a defective component or system, especially at an automated vehicle. The objective of this study is to establish a fault analysis methodology that can effectively improve the reliability of electric and automated vehicles. To achieve this, We proposed a method for identifying potential faults by combining Failure Modes and Effects Analysis (FMEA) with Fault Tree Analysis (FTA), creating a database of possible (example) faults that maps the causal relationship between causes, symptoms and faults, which enables more thorough fault analysis and serves as the foundation for further study. Using the fault database, we demonstrate a practical application involving fault injection and simulation, which can provide a more intuitive and practical representation of the effects of faults. The methodology is validated with the demonstrator vehicle from the joint project. This approach is scalable and can also be well applied to other electric automated vehicles with similar structure, providing a reliable tool to the system fault analysis for future work.

PyFaults: a Python tool for stacking fault screening

PyFaults is an open-source Python library designed to model stacking fault disorder in crystalline materials and qualitatively assess the characteristic selective broadening effects in powder X-ray diffraction (PXRD). Here, the main capabilities of PyFaults are presented, including unit cell and supercell model construction, PXRD pattern calculation, assessment against experimental PXRD, and methods for rapid screening of candidate models within a set of possible stacking vectors and fault occurrence probabilities. This program aims to serve as a computationally inexpensive tool for identifying and screening potential stacking fault models in materials with planar disorder. Three diverse case studies, involving GaN, Li2MnO3 and Li3YCl6, are presented to illustrate the program functionality across a range of structure types and stacking fault modalities.

Engine Fault Detection System (Autoguard)

This report presents the development of a mobile application named AutoGuard, which serves as a comprehensive anomaly detection system specifically designed for the automotive industry. AutoGuard utilizes advanced machine learning techniques, including statistical modeling, to analyze data collected from various sensors in a vehicle’s engine. The application not only detects potential faults but also provides real-time diagnostics by processing and visualizing the information in a user-friendly format. Extensive testing and analysis demonstrate the system’s ability to enhance vehicle safety and maintenance efficiency, making it a valuable tool for both drivers and automotive professionals.

Application of Resonance Method in Composite Fault Diagnosis of Bearings

Poisson white noise is a more accurate representation than ideal Gaussian white noise for simulating the noise background during early bearing failures. In the context of strong Poisson white noise interference, the failure mode of rolling bearings usually remains undetermined. This paper proposes a new method for diagnosing unknown bearing faults in bearings, diagnosis is proposed, which effectively identifies unknown faults across various components. The theoretical characteristics of Poisson white noise are initially analyzed and discussed. Then, to extract potential fault characteristic information, a false characteristic frequency of bearing is constructed in the response spectrum, and the response results at this false fault characteristic are obtained. Finally, coherent resonance (CR) is introduced, and false frequencies caused by false faults are eliminated by defining a quality factor. To verify the effectiveness of the method, the experimental and simulation results were compared with the decomposition results of the SVMD algorithm. The SNR of the experimental signals for outer and inner ring faults under variable speed conditions increased to 8.62[Formula: see text]dB and 11.74[Formula: see text]dB, respectively. Results indicate show that this method not only successfully identifies fault features, but also exhibits a strong noise reduction effect.

Automatically Injecting Robustness Statements into Distributed Applications

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved.

A Text Generation Method Based on a Multimodal Knowledge Graph for Fault Diagnosis of Consumer Electronics

As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies.

AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration

Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.

Integration of drone and machine learning technology for predicting power infrastructure faults efficiently

Power transmission and distribution networks frequently face issues, especially in harsh environments, leading to high maintenance costs and the need for uninterrupted electricity. Current field inspections by skilled personnel are labor-intensive, costly, and slow, often lacking efficiency and posing safety risks. While automated helicopters, flying robots, and climbing robots have been explored for visual inspections, the widespread adoption of automatic vision-based inspection remains limited due to high accuracy demands and unique challenges. This highlights the need for a fully autonomous vision-based system to inspect electrical power infrastructure and predict potential future faults. This research introduces Unmanned Aerial Vehicles (UAVs) as a promising solution for infrastructure inspection, deep learning for data analysis and prediction, and a mathematical model to ensure system accuracy doesn't rely solely on the dataset. As a proof of concept, YOLO V8 was employed to predict electrical faults in insulators, achieving a box loss of 0.525, a classification loss of 0.3887, and a precision of 0.8976, demonstrating high accuracy and low loss.

DeLoSo: Detecting Logic Synthesis Optimization Faults Based on Configuration Diversity

Logic synthesis tools are the core components of digital circuit design, which convert programs written in hardware description languages into gate-level netlists, and optimize the netlists. However, the netlist optimization is complex, with numerous optimization parameters to be configured. Any minor optimization faults in logic synthesis tools may cause circuit diagrams to significantly deviate from the original design, posing risks in target systems. We propose DeLoSo, the De tector of Lo gic S ynthesis o ptimization faults, the first method specifically designed to identify potential faults in the optimization processes of logic synthesis tools. DeLoSo relies on netlist differences and parameter variations to guide the generation of diverse Logic Synthesis Optimization Configuration (LSOC) combinations to thoroughly test the optimization process. DeLoSo consists of three components: LSOC generator, which generates diverse LSOC combinations through configuration recombination and mutation; LSOC diversity evaluator, which assesses the diversity of optimization configurations; and LSOC validator, which validates the generated LSOC combinations to discover optimization faults. DeLoSo identified 19 faults in two established logic synthesis tools (i.e., Vivado and Yosys); 15 of them have been fixed by vendors. Particularly, the community maintainers of Yosys have considered incorporating DeLoSo into Yosys’ existing test suite.

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

Dynamic strain distribution monitoring and fatigue damage analysis on a 100 kW wind turbine blade based on field aerodynamic measurements

Abstract Operation and maintenance (O&M) costs can account for 10%–20% of the total costs of electricity for a wind project. Structural health monitoring method is a form of preventive maintenance consisting of regular monitoring of the wind turbine components to detect potential faults. The strain measurements on the blades are typically used for load monitoring and damage detection, but only obtain a small amount of concentrated load information. In this work, a strain distribution monitoring method was proposed and validated based on aerodynamic measurements in the field. Then the contribution and evolution of the strain distribution from the aerodynamic load, gravity load, inertial load, and centrifugal load in the complex start-up process of the wind turbine were analyzed. The results shows that the mean value of the synthetic strain is mainly determined by the aerodynamic load and the centrifugal load, while the fluctuation amplitude is mainly determined by the gravity and the aerodynamic load. Furthermore, the fatigue damage of the blade root was evaluated based on strain extrapolation, rainflow cycle-counting algorithm, Goodman diagram and Miner’s linear superposition principle. It is found that the fatigue damage is generally greatest near the pressure side and suction side, and least near the leading edge and trailing edge. The strain distribution monitoring method can capture the location of maximum stress and the most severe fatigue damage on the blade, which are helpful for damage detection and load control, and further reduces O&M costs.

Unveiling the microservices testing methods, challenges, solutions, and solutions gaps: A systematic mapping study

Microservices architecture (MSA) is widely used by enterprises and related practitioners. Due to the importance of MSA in the industry, failure to identify its potential faults and errors will cause serious consequences. Consequently, it is imperative to conduct testing on the MSA to guarantee its quality and reliability. As a key task in the software development lifecycle, microservices testing can effectively identify defects and errors caused by incorrect or unexpected behavior of microservices software, ensuring the quality of microservices software. Currently, related reviews lack a comprehensive summary of the challenges and solutions faced by microservices testing. To fill this gap, this study provides a comprehensive review of the microservices testing. It analyzes 93 primary studies from 19595 pieces of research, eventually identifying nine categories of microservices testing methods, four types of challenges, and 5 specific solutions to challenges. The conclusion shows that the current part of microservices testing methods is significantly different from other architectural testing methods, lacking exploration of testing methods under different communication mechanisms. Furthermore, this paper discusses the above conclusions, providing directions to further narrow the gap in solutions to challenges and future development directions for microservices testing.

Recent and active faulting along the exposed front of the Northern Apennines (Italy): New insights from field and geochronological constraints

Establishing genetic links between active shallow faulting and deep seismogenic sources is challenging, especially in areas where seismogenic faults lack clear and readily interpretable geological evidence at the surface. The architecture of the Pedeapenninic margin of the Northern Apennines (Italy) reflects a regional-scale and complex NE-verging blind thrust system, which is dissected by transpressive/transtensive faults resulting from active NE-SW orogenic compression. The local geological framework is defined by allochthonous ocean-derived units resting atop Pliocene-to-present successions exposed along the Northern Apennines margin and to the NE of it, while the innermost chain sector mostly contains Adria-related units. We present results from field structural-geological investigations to identify and characterize potential active faults along the margin. There, the Pliocene-to-present units are faulted and folded, indicating that tectonic activity is still on-going, thus contributing to the local seismic hazard. Top-to-the NE and SW normal faults are common in the area and deform the Pliocene-to-present succession together with NE-SW strike-slip and transpressional/transtensional faults. Based on field evidence, we define four potentially active thrust segments affecting Middle Pleistocene to Holocene deposits exposed along the margin. Calcite U-Th dating on samples from faults extend the most recent datable tectonic activity back to the Middle Pleistocene. Paleostress analysis from inversion of fault-slip data from the most recent identified striated fault planes constrains a NE-SW shortening direction parallel to the Apennines regional migration direction. A distinct but coaxial extensional stress regime, recorded by structures measured within Plio-Pleistocene formations, was also identified. Our results offer a sound starting point for future investigations aimed at improving our understanding of active and seismogenic faulting in the area, so as to create robust Probabilistic Seismic and Fault Displacement Hazard Assessment (PSHA and PFDHA) models that can implement refined seismic hazard maps benefitting from structural-geological deterministic inputs in addition to the classic seismological constraints.

Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection

In an era of increasing digitalization, the integration of diverse sensors has become commonplace. Within the transportation sector, state Departments of Transportation have extensively deployed traffic sensors to support various engineering endeavors. However, these sensors are prone to faults such as data loss, random noise, bias, and drift due to sensor aging, defects, or environmental influences. Therefore, detecting these faults is imperative to maintain data integrity. This paper presents a novel approach for detecting faults in traffic data based on a cluster-guided data reconstruction process. First, a traffic sensor clustering module is designed using a dual-encoding attention graph auto-encoder (DA-GAE) to identify clusters of traffic sensors. This module leverages a joint embedding of node and edge features in a low-dimensional vector space. Subsequently, utilizing the identified clusters, a cluster-guided denoising graph auto-encoder (CG-DGAE) is devised and trained for data reconstruction. The CG-DGAE employs a diffusion graph convolutional network (DGCN) and is trained with a cluster-wise sampling strategy. Extensive experiments were conducted using traffic data obtained from a real-world large sensor network, demonstrating superior performance of the CG-DGAE model in data reconstruction compared to various baseline methods. For fault detection, a score function is devised to discern potential faults by contrasting the sensor data sequence and the reconstructed data sequence. The fault detection results show that our proposed CG-DGAE model achieved an impressive overall accuracy of 99.09%, a precision of 99.13%, a recall of 99.53%, and a F1 score of 99.53%. The superior capability of CG-DGAE in data reconstruction and fault detection is attributable to the cluster-wise spatial–temporal context leveraged by the model.

Active fault-tolerant control with prescribed performance and reachability judgement for the altitude ground test facility

This research proposes an active fault-tolerant control (AFTC) method based on reachability judgement with asymmetric appointed-time prescribed performance to ensure the safety of altitude ground test facility (AGTF) under potential valve actuator and sensor faults. Firstly, a long short-term memory (LSTM) neural network is employed for fault diagnosis. Given the unique characteristics of AGTF, the equivalent multiplicative fault is introduced to ensure the successful fitting of the fault diagnosis network. Then, a reachability judgement network based on the distance field on grids (DFOG) method is designed to assess the impact of faults on subsequent tasks of the system, thereby ensuring the safety of the AGTF. Subsequently, this study proposes an error transformation function of prescribed performance control (PPC) that ensures error convergence even in cases of asymmetric performance function. This enables the performance boundaries to be further reduced to improve the control performance of the system. Finally, experiments validate the effectiveness of the methods proposed in this manuscript.

Whistle or squeal? Distinguish and evaluate two typical abnormal sounds of collaborative robots

The whistle and squeal are two typical abnormal sounds among the radiated noise of running collaborative robots, indicating potential faults and causing discomfort through auditory perception. This study investigated whether psychoacoustic parameters for sound quality could distinguish the whistle and squeal and evaluate their discomfort. A total of 20 abnormal sound stimuli, including 6 whistles and 14 squeals, were obtained from various collaborative robots. The cepstrum characteristics and sound quality metrics (i.e., roughness, sharpness, fluctuation strength and tonality) of all stimuli were analyzed by controlling the equal loudness. Twenty-four participants rated the 'discomfort of abnormal sound in the stimuli' on an 11-point scale. The results show that the modulation frequencies of the fundamental frequency of the whistles were 2.9 Hz to 5.5 Hz, whereas the squeals had no obvious modulation characteristics. The modulation frequencies of the fundamental frequency were highly correlated (with a correlation coefficient of 0.89) to the discomfort responses for whistles, whereas tonality was significantly correlated (with a correlation coefficient of 0.82) to the discomfort responses for squeals. The linear models using single dependent variables of modulation frequency in cepstrum and tonality had determination coefficients of 0.77 and 0.66, respectively, in evaluating the discomfort of whistles and squeals.

CRDS‐based measurement of CO/CO2 concentration ratios for assessing transformer insulation aging

AbstractA gas analyzer has been developed using cavity ring‐down spectroscopy (CRDS) technology, designed to simultaneously measure the concentrations of CO2 and CO. These gases are critical indicators of transformer insulation degradation. The analyzer covers a CO2 concentration range from a few ppm to several thousand ppm and can measure CO/CO2 concentration ratios from 0.076 to 0.8. The selected absorption lines for CO and CO2 are centered at 6337.99 and 6338.59 cm−1, respectively. The two candidate lines are proximate to each other and nonoverlapping. The experimental results indicate that the system accurately fits the measured and calculated signals, enabling concentration measurements at the ppm level. This confirms its ability to measure the gas concentrations and ratios critical for assessing the condition of transformer insulation. This CRDS‐based system offers a reliable and sensitive method for early detection of potential transformer faults.

Unsupervised identification of zone-level anomalies in VAV terminal units utilizing autoencoders and PCA

The effective operation of HVAC systems is crucial to minimize energy inefficiencies and occupant discomfort. However, these systems can experience various problems, including hardware and software-related anomalies. In contrast to most existing fault detection and diagnostic approaches, which rely on simple rules and alarms, this study introduces novel unsupervised approaches for detecting zone anomalies in variable air volume (VAV) air handling units (AHUs). The methods utilize autoencoders (AE) and principal component analysis (PCA). To evaluate the effectiveness of the proposed methods, both a synthetic dataset and measured data from a 28-zone VAV AHU system were investigated. The proposed method successfully detected several zone temperature and airflow anomalies using the AE-based method, and several zone anomalies were also identified using the PCA-AE approach by considering four commonly available zone-level trend logs in VAV AHUs namely temperature, airflow, airflow set-point, and VAV terminal damper position. The findings demonstrated the great adaptability of the proposed methods in detecting a wide range of zone anomalies in any modern building equipped with VAV AHUs, giving operators valuable insights about the system and notifying them of potential faults at an early stage.

Review: Geological Structure impacts to hydrocarbon potential and active faults in the East Java Basin, Indonesia

Review of the geological structure patterns in the East Java Basin (EJB) to understand the geodynamics has implicated to the hydrocarbon potential and active faults. However, the impact of those structures on hydrocarbon potential and active faults is unclear. This study reviewed structure patterns using surface and subsurface data, GPS, seismicity and tremors in the East Java Basin, Indonesia and Nankai Trough, Japan. In EJB, Indonesia, the tectonic setting is constrained by the Rembang Fault. The north of the Rembang Fault, the pattern exhibits NE-SW structures, while to the south, it shows W-E structures. The results indicate that the upper crust (including ophiolitic basement) has greater density to the north than to the south. Thus, vertical motion of the crust is more dominant than lateral motion to the north of the Rembang Fault. This vertical motion may trigger the reactivation of the Meratus Fault (weak zone or as active fault) located on the northern platform (e.g., the Bawean earthquake on March 22, 2023). Conversely, to the south of the Rembang Fault, there is a significant hydrocarbon potential associated with W-E structures. Those structures could form by subduction and collision tectonic. Similary, tectonic backstop may account for presence of structures in Nankai Trough, Japan.

Degradation Mechanism and Online Electrical Monitoring Techniques of Stator Winding Insulation in Inverter-Fed Machines: A Review

Inverter-fed machines are widely used in electric vehicle drive systems and have shown a trend toward high voltage and frequency in recent years. They are subjected to multiple types of stress during operation, causing potential short-circuit fault damage to the stator winding insulation. Online condition monitoring of the insulation before or in the early stage of the short circuit fault can effectively reduce maintenance costs and ensure its health. This paper reviews and summarizes the deterioration mechanism and the recent online electrical monitoring techniques. First, four types of failure stress and each type’s failure factors and mechanisms are analyzed. The coupling effect and overall process of multi-physical fields on stator insulation failure are considered. Secondly, the latest online electrical monitoring technologies are summarized. Each technique’s principles, methods, advantages, and disadvantages are analyzed. Finally, existing problems and possible directions for improvement in current research are discussed, focusing on their feasibility and accuracy in practical applications.