Fault Prevention Research Articles

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.

Cascading fault prevention and control strategy based on economic dispatch of AC/DC systems

Cascading fault prevention and control strategy based on economic dispatch of AC/DC systems

Impactability and susceptibility assessment based on D-S evidence theory for analyzing the risk of fault propagation among catenary components

As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components from the historical fault data, so that the fault propagation mechanisms among components can be revealed. Initially, based on the different roles played by components in the fault propagation process, we define fault impactability and susceptibility of components under different mechanical coupling relationships to capture the fault propagation mechanisms. Then, we propose a risk trust function model based on the D-S evidence theory to assess the fault impactability and susceptibility. Meanwhile, a belief and disbelief-based risk coefficient is proposed in the risk trust function model to construct the evidence source. Finally, the case study, based on the fault database of the Chengdu Railway Bureau, demonstrates that the proposed method can effectively assess the fault impactability and susceptibility of components to reveal the fault propagation mechanisms, which provides valuable references for formulating fault prevention strategies.

Fault prevention strategy for coupled networks of power grids and transportation with the participation of electric vehicles

The increasing number of electric vehicles (EVs) that are connected to the power grid has raised concerns about the security of both transportation and power grids. This paper presents a fault prevention strategy that involves the use of social networks to improve the performance of electric vehicle charging stations. Firstly, the goal of this strategy is to improve the electric vehicle charging station’s performance by developing an evolutionary game model that takes into account the various factors that affect the electric vehicle’s response and travel time. Secondly, a number of charging station sample data is generated and analyzed using a convolutional neural framework. The capacity of the stations is then calculated by taking into account the different incentive measures offered by the power grid.. Furthermore, an optimal power flow model considering fault risk constraints and charging and discharging capacity constraints is established in the power grid or power grid subsystem to adjust the generator output and charging station power to realize the safe dispatching of the power grid. The simulation example shows that this method can fully consider the influence of traffic networks in the participation of EVs in power grid fault prevention, which is more reasonable.

Grid interlocking fault prevention and control method considering safety and network loss

Grid interlocking fault prevention and control method considering safety and network loss

3D Acoustic Heat-Maps for Transformer Monitoring Applications


 
 
 We present a novel approach for measuring and representing acoustic emissions of transformers. The representation of location-based acoustic emissions enables improved monitoring of transformers, e.g., to detect and predict anomalies and failure events. Here, we introduce 3D acoustic heatmaps to visualize the sound emission patterns of a transformer. We use a combined sensing approach consisting of a 3D point cloud, a microphone array, and beamforming algorithms to generate the distributed representation of acoustic emissions over the entire surface of the transformer. In a further step, we intend to apply machine learning methods to the generated data to enable early fault prevention and predictive maintenance.
 
 

Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems

In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio.

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring

A key element of smart manufacturing is condition monitoring and heath controlling of production machines. In today's rapidly evolving landscape of industrial machinery and equipment, optimizing the operation of production lines is critical to ensure high productivity and product quality. Timely detection and prevention of faults in the production process plays a crucial role in minimizing downtime, reducing costs, and ensuring optimal performance. The scientific challenge here is that the increasing number of sensors and actuators with digital input and output signals in the production machines creates different patterns, which are difficult to evaluate using conventional statistical methods. Another difficulty is identifying the cause of the failure to be able to intervene rapidly and in a focused manner in the event of irregularities. For this reason, this research study presented a comprehensive analysis of anomaly detection in binary time series data using various machine learning models. The study included preprocessing of the dataset, normalizing the data, and evaluation of the anomaly detection performance of the different models. The accuracy, detection rate, and F1-score are used as evaluation measures. The execution time of each model is also analyzed. In addition, the identification of sensors that cause anomalies is investigated and the impact of false detections is discussed. Experimental results show the strengths and weaknesses of each model and provide valuable insights for selecting the appropriate anomaly detection approach. The Isolation Forest, Local Outlier Factor, DBSCAN, and kMeans models show high precision and detection, while the Autoencoder and Variational Autoencoder models show high precision but lower detection. The one-class Support Vector Machine model achieves balanced performance. AutoML shows excellent results in recognition rate but is not real-time capable. The results highlight the trade-offs between performance and computational efficiency and point to further research potential in real-time implementation.

A Condition and Fault Prevention Monitoring System for Industrial Computer Numerical Control Machinery

A Condition and Fault Prevention Monitoring System for Industrial Computer Numerical Control Machinery

Single Line-to-Ground Fault Type Multilevel Classification in Distribution Network Using Realistic Recorded Waveform.

The further identification of fault types for single line-to-ground faults (SLGFs) in distribution networks is conducive to determining the cause of grounding faults and formulating targeted measures for hidden danger treatment and fault prevention. For the six types of SLGFs generated in the actual power grid, this paper deeply studies their fault characteristics. Firstly, the classification criterion of fault transition resistance is derived by the generation mechanism of fault zero sequence voltage (ZSV). At the same time, by comparing and analyzing the same and different characteristics between faults, three criteria for fault classification are obtained. Based on the above four criteria, a multilevel and multicriteria fault classification method is proposed to judge six types of SLGFs. Then, the proposed method is verified by various fault state simulations of the distribution network model with a balanced topology and unbalanced topology. The engineering application of the method is demonstrated by the verification of actual power grid data. Finally, noise and data loss interference test results show the robustness of the method.

Condition Based Maintenance Method for Power Relay Protection Equipment Based on Full Life Cycle

Power Relay Protection Equipment (abbreviated as RPE for convenience) is the foundation for ensuring the safe and stable operation of circuit components, and is crucial for the maintenance of power RPE. The traditional regular maintenance method for RPE requires a large number of frontline maintenance personnel to participate. The regular maintenance method can easily cause unnecessary maintenance, and excessive maintenance can lead to resource waste. This article introduced the concept of Full Life Cycle (FLC) cost management for power RPE and obtained the occurrence of faults in the FLC of RPE. This article analyzed the periodic fault occurrence rate of RPE, and fitted the fault curve to analyze the fault rate boundary point. On the basis of the fault distribution curve, the service life regression factor was introduced to obtain the preventive maintenance years, and the actual dynamic changes of preventive maintenance were obtained. The content of RPE condition maintenance was explored by balancing the FLC cost. This article analyzed the transformer RPE and obtained the periodic fault rate of the equipment. The cutoff point for equipment failure rate was 0.02, and the cutoff points for preventive maintenance were the 7th, 9th, and 11th years, respectively. The annuity cost in the 11th year was the lowest. By analyzing the fault status and FLC cost of RPE, the boundary points for equipment fault prevention and maintenance could be determined, and the optimal maintenance plan for RPE could be determined.

Frequency Characterization of Medium Voltage Cables for Fault Prevention Through Multi-Valued Neural Networks and Power Line Communication Technologies

Frequency Characterization of Medium Voltage Cables for Fault Prevention Through Multi-Valued Neural Networks and Power Line Communication Technologies

A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion

The increase in the number of channels for extracting bearing fault features can to some extent enhance diagnostic performance. Therefore, this article proposes a SENet (squeeze and excitation network)—TSCNN (two flow convolutional neural network) model with high accuracy and generalization characteristics for fault diagnosis of rolling bearings. Firstly, use convolutional pooling layers to construct a basic diagnostic model framework. Secondly, due to the unsatisfactory performance of feature extraction solely on one-dimensional frequency domain signals or two-dimensional time-frequency signals, there may be misjudgments. Therefore, a dual stream convolutional model is integrated to process both one-dimensional and two-dimensional data. Fast Fourier transform is used to process one-dimensional frequency domain data, and continuous wavelet transform is used to process two-dimensional time-frequency maps. Once again, integrating the SENet module into the dual stream diagnostic model, the addition of attention mechanism can enable the model to better understand key features of input data. Finally, the data obtained from the processing of two channels is fused and classified in the Softmax layer. This article uses the rolling bearing fault standard data from Case Western Reserve University and the American Society for Mechanical Fault Prevention Technology, and verifies through multiple controlled experiments that the model established in this article has high accuracy and good generalization characteristics.

Development and application research of startup failure and operation failure model of emergency diesel generator in nuclear power plants

In order to fully grasp the law of startup failure and operation failure of emergency diesel generator in nuclear power plants, this paper first presents the development method of integrated fault model for emergency diesel generator startup failure and operation failure on the basis of fully integrating the existing mature fault tree analysis methods. Then, taking a certain type of Emergency diesel generator as an example, a complete startup failure and operation failure model was developed, and qualitative analysis and quantitative evaluation were carried out on the basis of the model, which verified the accuracy and applicability of the model development method. Finally, this paper puts forward the qualitative analysis and quantitative evaluation results based on model, which are directly used in the basic application scheme of equipment safeguard measures and the advanced application scheme of dynamic risk assessment and long-term trend monitoring of emergency diesel generator sets based on model + data driven by emergency diesel generator sets. Through the advanced application solution, users can identify dynamic high-risk defects and long-term reliability management weaknesses in advance, and formulate effective short-term fault elimination and long-term fault prevention measures in time to avoid the consequences of startup failure and operation failure. The above research results have important practical significance for the continuous improvement of the startup reliability and operation reliability of emergency diesel generator in nuclear power plants.

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Abstract Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.

Prognostic fault prevention by segmented digital transformation of manufacturing process signals

Faults during operation of a system can occur at any time. Contemporary fault diagnosis systems focus on identifying the problem. However, when faults occur, the system has already incurred losses, either as rejects or machine damage. In this paper, a new method that predicts future faults and subsequently prevents losses caused by the faults has been developed. The method makes use of a series of digital transformations of signals from the manufacturing process. An important finding of this research is to divide the continuous signal stream into data segments from which a parameter called sum standard deviation frequency (SSDF) can be extracted. SSDF reflects the change of inconspicuous conditions in the signal in different data segments over time. To predict how the signal stream will perform in the next minute, a new density peak clustering computational algorithm is developed to transform the SSDF database into “normal”, “marginal”, and “abnormal” records. Finally, a novel method “moving SSDF deep learning” (MSDL) concept has been proven to have a better prediction of future capability than four other prediction methods. The method has been applied to a 3D printing process in which belt tension affecting machine performance has been predicted 30 s ahead of the problem, giving sufficient time for the operator to adjust or stop the machine before a fault occurs.

Reliability Assessment of Nuclear Thermal Engine Configuration and Health Monitoring System

Today’s space nuclear technology programs are often confronted with two fundamental challenges early in the project life cycle: 1) development and testing will be more expensive than a non-nuclear alternative, and 2) the consequences of failure will be more severe. As a result, many space nuclear programs have been designed to minimize testing and maximize their probability of success: their reliability. The United States' Nuclear Engine for Rocket Vehicle Applications Program recognized these facts early on, and by 1961 the program’s primary objective set safety and reliability as the overriding considerations. This focus on reliability greatly influenced the engine's design towards minimizing the possible number of catastrophic failures modes. As such, the final configuration heavily relied on duplicate components for redundancy, including duplicate turbopumps. Despite these efforts, at program cancellation in 1972, the non-nuclear subsystem only achieved a mission predicted reliability of 33%. Some of the most significant contributions to rocket engine reliability in the last 50 years have been from advancements in the Health Monitoring System (HMS). Through rigorous instrumentation and control an engine's HMS has the potential to convert over 90% of a system's catastrophic failure modes to a safe shutdown situation. Due to this, many modern rocket engine designs no longer require redundant components and can prioritize performance-enhancing configurations over those that inherently minimize failure modes. Unfortunately, a standard liquid rocket engine HMS will likely not be compatible with a nuclear rocket engine (NRE). The HMS for a liquid rocket engine most often prevents catastrophic failure by shutting off flow to the combustion chamber. This would not work for a NRE, as removing propellant flow to the reactor could result in reactor overheating and meltdown. Maintaining flow to the reactor is often essential for safe NRE operation, such that reliance on an advanced HMS system alone may not be sufficient. This work investigates the feasibility of an NRE HMS by comparing the HMS designs for liquid rocket engines and terrestrial nuclear power plants and evaluates the necessity for redundant components to maximize overall system reliability. Keywords: Nuclear Thermal Propulsion, Nuclear Rocket Engine, Reliability, Fault Prevention, Fault Tolerance, Turbopump

Causes and Prevention of Photovoltaic Module Hot Spot Effect

The photovoltaic module is the basic link in the photovoltaic power generation system, which has an important impact on the economic operation of photovoltaic power plants. Hot spot effects account for a large proportion of photovoltaic module failures, so it is of engineering significance to study them and put forward suggestions for fault prevention. Firstly, this paper briefly introduces the composition of photovoltaic power generation system and the structure of photovoltaic modules then analyzes the working process and typical models of photovoltaic modules as well as the causes of the hot spot effects of photovoltaic modules. Finally, it puts forward some measures to prevent faults to improve the operational reliability of photovoltaic modules.