Fault Monitoring System Research Articles

Fault Monitoring and Analysis System for FACTS Systems

This study focuses on the development of a fault monitoring and analysis system specifically designed for Flexible AC Transmission Systems (FACTS). The system records analog and digital data at high resolution and frequency, enabling graphical analysis of these data. Designed for various industrial applications, this system is particularly used in distribution networks, substations, generation facilities, and factories to detect and monitor voltage and current anomalies. The system is equipped with a user-friendly interface that allows detailed examination of the recorded data.

Enhancing Thermal Image Classification with Novel Quality Metric-Based Augmentation Techniques

Thermal image classification is critical in various applications, particularly fault detection and monitoring systems such as photovoltaic (PV) modules. However, a common challenge in these fields is the limited availability of large-scale, labeled thermal image datasets. To address this, color image augmentation is widely adopted in machine learning to artificially increase the size and diversity of training datasets, improving model generalization. Traditional augmentation techniques, such as geometric transformations, provide some benefits but may fail to fully capture the unique characteristics inherent in thermal images, which often have lower contrast and different noise patterns than visible spectrum images. So, we argued we need to develop a novel augmentation technique for thermal imaging, where data collection is costly and time-consuming. Our research proposes a novel offline augmentation technique guided by quality metrics to enhance the performance of thermal image binary classification models. By leveraging domain-specific quality metrics, such as image clarity, thermal contrast, and noise levels, we optimize the oversampling process for thermal datasets. For example, starting with a dataset of x images, we generate y additional thermal images, resulting in a total of x + y images used to train the deep learning classification framework. Using a dataset of PV module defects, we demonstrate the effectiveness of our quality metric-based oversampling strategy across several state-of-the-art image classification networks. Our approach outperforms traditional augmentation methods regarding classification accuracy and robustness, including geometric transformations and standard image enhancement techniques. The practical implications of our research are significant, as it provides a more effective and efficient way to improve model performance for thermal imaging tasks, mainly when data availability is limited.

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump

Centrifugal pumps are important equipment in industrial production, and their safe and reliable operation is of great significance to water supply and industrial safety. During the use of centrifugal pumps, faults such as bearing damage, blade wear, shaft imbalance, shaft misalignment and water hammer often occur. Among them, although water hammer faults occur at a low frequency, they are difficult to monitor and pose significant risks to valve and pipeline interfaces. This article analyzes the causes, mechanisms and phenomena of water hammer faults in centrifugal pumps, designs a monitoring method to effectively monitor the vibration signal of the centrifugal pumps, extracts vibration characteristics to determine and record water hammer events, designs monitoring and diagnostic models for the edge layer and server side, and establishes an experimental verification testing system. The test results show that, under the conditions of simulating water hammer faults, after high-pass filtering of the collected vibration data, the kurtosis index, pulse index and margin index all exceed twice the threshold, and both sensors emit water hammer alarms. The designed data acquisition method can capture water hammer signals in a timely manner, and the analysis model can automatically identify water hammer faults based on existing fault knowledge and rules. This fully demonstrates the scientific and effective nature of the proposed centrifugal pump fault monitoring method and system, which is of great significance for ensuring the safe operation and improving the design of centrifugal pumps.

IoT based Smart Fault Identification and Monitoring System for Electric Poles

Electricity is essential in modern life, but damaged power lines cause thousands of electrocutions in India every year. To fix a damaged power line, the power supply is turned off, and a fuse controller is placed on the electric feeder. If the fuse controller trips on a broken wire, the electricity is cut off until the damage is located. This process can be time-consuming and requires resources. This research aims to design a IoT based control system to reduce the time for repairing broken electrical lines and prevent accidents by adopting measures to expedite the repair process, enhance safety, and optimize maintenance efficiency. LoRa devices are installed every 1 km along the cable for a total range of 10 km. A central tower marks the halfway point. A single LoRa device at the tower may be more cost-effective than multiple devices at each pole. A control circuit embedded by transistor and diode safeguard the circuit's integrity and ensure current flows in a single direction, protecting it if a wire between two poles is damaged. After locating damage to a pole, an email is sent to the relevant authority, including location, pole number, and incident time. An automatic alarm will sound when electricity is turned off, warning the public of the danger. Additionally, this system saves time and resources, and is cost-effective.

Automated Fault Detection and Location Monitoring of Street Lights in Smart Cities

This work will implement an automated fault detection and monitoring system for streetlights in smart cities. The system will use smart streetlights with sensors, communication modules, and intelligent controllers to create a sensor network across the city. These sensors, like light intensity, motion, and temperature sensors, will collect real-time data on the status of street lights and the environment. The data will be analyzed by a centralized monitoring system that will use algorithms to detect abnormalities in the sensor data that may indicate faults or malfunctions in the street lighting infrastructure. GPS tracking technology will be used to pinpoint the location of each street light. An automated alert system will notify maintenance teams and city officials through a webpage if a fault is detected. The system will also use historical data to anticipate potential issues before they arise.The benefits of this system include improved energy efficiency, cost savings through timely maintenance, quick response times for fault resolution, and data-driven decisionmaking for urban planning. It will provide real-time information on the status of street lights and their environment, which can be used to optimize energy consumption and reduce costs. The system will also enable city officials to make data-driven decisions for urban planning. Ultimately, this system will help to create smarter and more sustainable cities by enhancing public safety, optimizing energy consumption, and ensuring the reliability of street lighting infrastructure. The system will provide a more efficient and costeffective approach to street lighting maintenance, leading to better services for citizens.

Electrical Ground Fault Monitoring System Utilizing Arduino and Android Application

Abstract: The increasing residential fire incidence caused by electrical ground faults worldwide challenges present and future researchers to develop a system capable of mitigating its occurrence. The absence of periodic equipment maintenance and monitoring facilities leads to potential electrical hazards to humans and properties. However, technological advances have made online electrical ground fault monitoring equipment possible. Users can now remotely gain information regarding the operational condition of electrical equipment in their homes. This paper presents the developed Electrical Ground Fault Monitoring System using Arduino with Android Application (EGFMSAAA), which can detect and transmit electrical ground fault parameters and safety precautions. This study developed a ground fault detector and a mobile application for the monitoring system.

Exploration on Wireless Network-Based Monitoring and Early Warning Algorithms for Power Grounding Faults

Electricity is an indispensable resource in daily life. While it brings great convenience to people, it also brings some safety hazards, especially in the event of power failure, which may cause significant harm. Therefore, monitoring and warning of power faults are very important. Traditional power grounding fault monitoring and warning systems have problems such as untimely monitoring, inaccurate warning, and high error rates in fault location. In order to solve the above problems, this paper uses wireless networks to construct a power grounding fault monitoring and early warning system. The wireless network collected fault data based on the complexity of power grounding fault data, and used threshold monitoring method to analyze the collected data. The wireless network was used to construct a prediction model to monitor grounding faults. Through experiments, it can be found that the accuracy of the wireless network-based power fault monitoring system for predicting grounding faults was over 92.57%, and the average warning accuracy of 20 experiments was 93.856%. This paper studied a wireless network-based algorithm for monitoring and warning of power grounding faults, which can effectively improve the monitoring and warning capabilities of power systems, and reduce the risk of power equipment faults and the probability of power accidents.

Active trailing edge flap system fault detection via machine learning

Abstract. Active trailing edge flap (AFlap) systems have shown promising results in reducing wind turbine (WT) loads. The design of WTs relying on AFlap load reduction requires implementing systems to detect, monitor, and quantify any potential fault or performance degradation of the flap system to avoid jeopardizing the wind turbine's safety and performance. Currently, flap fault detection or monitoring systems are yet to be developed. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach combines manual feature engineering with a random forest classifier. The second approach relies on random convolutional kernels to create the feature vectors. The study shows that the first method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the second method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production. These results contribute to developing the systems for detecting and monitoring active flap faults, which are paramount for the safe and reliable integration of active flap technology in future wind turbine design.

Autonomous Fault Monitoring for Efficient Multi-Actuator Compressed Air Systems:Data Analytics of Demand-Oriented Parameters

Industry 4.0 has given rise to an increased use of system data and analytics. Autonomous fault detection in pneumatic systems has also been increasingly explored in the last decade, with one of the main objectives being to reduce the energy consumption and resulting carbon footprint of such systems. Nevertheless, pneumatic fault monitoring systems tend to mainly focus on the supply-side, neglecting the demand-side. Studies covering pneumatic fault monitoring on the demand-side, perform research on simple systems, typically investigating the effects on a single actuator or a very small system. This study aimed at tackling this issue, with tests performed on an industrial multi-actuator pick-and-place setup, logging data (i.e. cycle time, flow rate and system pressure) concurrently at two locations within the system. Furthermore, different sized leaks were introduced at three distinct locations, while monitoring their impacts on the system. It was found that with the use of the average, standard deviation and impulse factor of the cycle time and the other two parameters, it was possible to identify the presence of faults on a relatively large system. For instance, the retraction time for one of the actuators reduced by 26% as one of the faults was induced. With modern industrial setups already logging the cycle time and system pressure along the demand-side, this study shows that by using existing equipment, one can develop a reliable fault monitoring system. Such information makes it possible to determine additional fault characteristics, mainly the fault's severity, type and location, thus paving the way towards smart and energy efficient compressed air systems.

Pneumatic Fault Monitoring and Control for Sustainable Compressed Air Systems

Sustainable development in the industrial sector has been widely explored in recent years, to achieve a carbon-neutral industry. Compressed air systems are widely used in the industrial sector. Numerous energy-saving improvements have been studied within this scope, as leaks make these systems inefficient. Nevertheless, it has yet to be seen how different optimisation techniques can be utilised to mitigate fault effects. This study shows how fault monitoring was performed on a multi-actuator system, using different time domain indicators including, mean and standard deviation. This was followed by exploring the system behaviour when pressure and flowrate control strategies were executed to minimize fault impacts. Fault monitoring, using the indicator data, was successful. For instance, as a 1 mm leak was induced and the consumption increased by 34%, the standard deviation in pressure drop reduced by 6% and the mean actuation time decreased by 13%. Though the mean in pressure drop was useful for fault monitoring other pressure indicators, including standard deviation, provided additional monitoring capabilities. During this monitoring exercise, it was also found that improved sensor accuracy resulted in less reading variations, obtaining more conclusive results and identifying faults of smaller sizes. As faults were accurately identified and characterised, it was then possible to mitigate their effects via pressure and flowrate adjustments. Results proved promising, as both contributed to air consumption decreases of 16% and 11%, respectively. Although such modifications decreased the production rate by 2-5%, the previously mentioned savings outweighed this decrease. This work highlights the need for development of fault monitoring and control systems which maintain the productivity and energy performance of pneumatic systems.

Electrical Ground Fault Monitoring System Utilizing Arduino and Android Application

The increasing incidence of residential fires caused by electrical ground faults worldwide challenges researchers to develop systems capable of mitigating their occurrence. The absence of periodic equipment maintenance and monitoring facilities leads to potential electrical hazards to humans and properties. Technological advances have made online electrical ground fault monitoring equipment possible, allowing users to remotely access information about the operational condition of electrical equipment in their homes. This paper presents the development and testing of an Electrical Ground Fault Monitoring System using Arduino with an Android Application (EGFMSAAA). The system integrates an Electrical Ground Fault Detector Device (EGFDD) with an Arduino GSM Shield to detect and transmit electrical ground fault parameters. It also includes an Android-based application for real-time monitoring and control. The system was tested for accuracy and reliability, demonstrating its capability to detect faults, provide early warnings, and allow users to take preventive measures remotely. The findings indicate that the EGFMSAAA effectively enhances safety and reduces the risk of electrical fires in residential settings.

Design and Anti-Interference Analysis of Fault Monitoring System Based on Multi-Wavelength Grouping for Passive Optical Network System

Design and Anti-Interference Analysis of Fault Monitoring System Based on Multi-Wavelength Grouping for Passive Optical Network System

Engineering fault intelligent monitoring system based on Internet of Things and GIS

Abstract The power grid (referred to as PG for convenience) structure is becoming increasingly complex. Aiming at the problem that it is difficult for traditional PG monitoring methods to accurately detect PG faults, an intelligent PG fault monitoring system is constructed using Internet of Things (IoT) and geographic information system (GIS) to improve the effectiveness of fault monitoring. The sensor equipment is used to collect the current information in the circuit, and the change of induced current is used to judge the cause of the fault, and the fault information is transmitted to the monitoring center through communication technology. The staff can directly locate the geographical location of the fault in the visual interface. One hundred overhead lines of Xianyang Power Supply Company are selected for analysis, and the performance of the traditional PG monitoring method and intelligent PG fault monitoring system is compared. The average fault detection accuracy of the traditional PG monitoring method and the system proposed in this article is 72.0 and 94.8%, respectively. The average fault location accuracy of the traditional PG monitoring method and this system is 80.8 and 96.5%, respectively. The intelligent monitoring system of PG fault based on IoT and GIS has high accuracy in PG fault detection and fault location, which can improve the effectiveness of fault monitoring.

Application of vibration signal detection in mine hoist fault monitoring system

Due to the harsh working environment and various types of faults in mine hoists, the implementation of conventional detection is relatively difficult. In order to improve the maintenance and fault detection efficiency of mine hoist, a portable vibration detection system is designed and applied in this paper, which can judge the type and characteristics of mechanical faults according to the different responses of vibration signals. According to the test conditions of the vibration system, the overall structure and functional composition of the hardware system are established. The key functional modules are designed, including signal conditioning module, main control module, vibration sensor and power management module. The main reasons that affect and produce the hoist fault and mechanical vibration are analyzed. The wavelet packet analysis method is proposed to realize the decomposition of low frequency and high frequency signals to improve the signal resolution of the whole frequency band in the frequency domain. The vibration test system has the function of real-time data transmission and control. The realization methods include WiFi and reserved RS485 communication interface, which can match the communication rate with the data sampling rate. The amplifying circuit of the electric signal is designed to effectively ensure that the output voltage of the charge amplifier is within the input range of the amplified ADC value. In order to verify the working effect of the system, the most common gearbox and bearing failures are taken as examples. The response signals of vibration test and diagnosis can be effectively obtained, and the fault types and characteristics can be accurately judged. Through confusion matrix analysis, the reliability of the system detection index is effectively verified. The research results show that the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect. It also can be known that different fault types can excite different vibration responses, especially at resonance frequencies. According to the verification, the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect.

Under Ground Fault Monitoring system Using Cloud Computing and IoT

An underground fault monitoring system project's major objective is to develop and put into use a system that can quickly identify and pinpoint faults in subterranean power cables. The system uses a network of sensors put along the cable's length to monitor and communicated at a on a variety of characteristics, including temperature, vibration, and current. For analysis to a central control unit. Advanced algorithms are used by the control unit to process the data and find any anomalies or potential problems. The system alerts the maintenance crew in real time when a fault is found, allowing them to swiftly find and fix the issue. Reducing the possibility of failure is the project's main objective in order to increase the availability and dependability of the electricity system.

Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review

The industrial sector is a crucial economic pillar, seeing annual increases in the production output. In the last few years, a greater emphasis has been placed on the efficient and sustainable use of resources within industry. The use of compressed air in this field is hence gaining interest. These systems have numerous benefits, such as relative low investment costs and reliability; however, they suffer from low-energy efficiency and are highly susceptible to faults. Conventional detection systems, such as ultrasonic leak detection, can be used to identify faults. However, these methods are time consuming, meaning that leakages are often left unattended, contributing to additional energy wastage. Studies published in this area often focus on the supply side rather than the demand side of pneumatic systems. This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side of compressed air systems, leading towards a comprehensive understanding of smart and sustainable pneumatic systems. Fifty-three studies were classified and reviewed under the following three areas: (a) demand parameters which help in identifying fault sources; (b) approaches taken to analyse the parametric data; and (c) the role of Artificial Intelligence (AI) in pneumatic fault monitoring systems. This review shows that fault detection on the demand side has received greater importance in the last five years and that data analysis is crucial for AI to be implemented correctly. Nevertheless, it is clear that further research in this sector is essential, in order to investigate more complex systems. It is envisaged that this study can promote the adoption of such systems, contributing to an energy-efficient and cost-effective industry.

LSTM based data driven fault detection and isolation in small modular reactors

Nuclear power stations revealed their value in the power sector by supplying reliable, emission-free power for many years. The highest standards of safety must be attained since a nuclear power station is a nonlinear, intricate, time-varying system that has the probability of leaking radiations. Pr edominantly, it is challenging for operators to quickly and precisely extract critical data about the real plant variables as a result of the vast monitoring data obtained in modern NPPs. However, current developments in machine learning techniques have made it conceivable for operators to interpret these vast amounts of data and take appropriate action. Thermal hydraulic analysis using the RELAP5 algorithm was done on the IP-200 NPP. A long short-term memory architecture was trained to categorize six different simulated IP-200 circumstances. The outcomes improved the accuracy and dependability of nuclear power plant fault monitoring systems.

ANN-Based Statistical Computation for Remote End Fault Monitoring of the IEEE 14 Bus Microgrid Network

This paper focuses on an artificial neural network-based fog computing approach for remote end fault super vision in microgrid systems. The work presented here considers an Artificial Neural Network-based adaptive Line-to-Ground (L-G) and Line-to-Line (L-L) fault monitoring system for a standard IEEE 14 bus microgrid. Discrete Wavelet Transformation (DWT)-based study of statistical parameters of the currents going out from the various generator buses is carried out both in healthy and faulty situations. Faults (L-G and L-L) are created at different load buses and also an algorithm is proposed for detecting fault location. The rule set proposed here is unaffected by variations in fault resistance, making it very much suitable for ground fault monitoring. It provided satisfactory results when examined with various unknown cases. A fog computing layer helps in fast execution and fewer data storage requirements in the cloud. This assessment may be expanded for other kinds of faults in a microgrid system.

Retracted: Design of an Underground Transmission Line Condition Fault Monitoring System for Power Grids Based on Data Analysis Algorithms

Retracted: Design of an Underground Transmission Line Condition Fault Monitoring System for Power Grids Based on Data Analysis Algorithms