Remote Fault Research Articles

Study on Fault Prediction Method of Hydropower Equipment Based on Equipment Health State Detection

Abstract With the continuous development of China’s electric power industry, the installed capacity of power plants is increasing, and hydropower units are developing in the direction of large capacity and high efficiency. As the complexity of the equipment is getting higher and higher, different equipment are more and more different, showing different characteristics when faults occur, which puts forward higher requirements for the safe and stable operation of hydropower units. In response to the above problems, this paper develops a set of human-machine closed-loop remote fault diagnosis reasoning machine system with self-learning function and a set of equipment health state monitoring and prediction system. This remote fault diagnosis system reasoning machine system can reduce the cost of unit equipment fault diagnosis, improve the diagnosis efficiency, and ensure the safe, stable and economic operation of the unit.

Intelligent Street Lighting: An IoT-based System for Adaptive Brightness and Fault Management

The research aims to develop an intelligent fault detection and control system for street lights using an STM32 microcontroller and Internet of Things (IoT) technologies. The system uses various sensors and communication methods to manage lighting more effectively and maintain infrastructure better. Light-Dependent Resistors (LDRs), which turn lights on automatically at nightfall and off at dawn, are essential to the system's operation. Moreover, motion is detected by the infrared (IR) sensors on the street, allowing the lighting to save energy by dimming during times of low traffic and brightening when movement is detected. By using control logic and sensor readings, a relay functions as an electronically controlled switch to turn the lights on and off. To process sensor data, the STM32 microcontroller is used in the main functionality. Energy savings are facilitated by the automated on/off switching based on ambient light levels, enabled by the LDR sensor data. In addition, the device includes a GPS module for precise street light location tracking. An IoT platform transmits this location data as well as operational status and real-time sensor readings. Maintenance staff can access regional data on light quality and sensor readings through an LCD display. This extensive data enables maintenance team to focus better, use resources efficiently, and enable remote monitoring and fault identification (e.g., burned-out bulbs). This Internet-of-Things (IoT) system provides an economical and environmentally friendly approach to efficient street light management by integrating automation, remote control, and real-time monitoring.

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

Research on the Application of Intelligent Diagnosis of Major Equipment Failures in Coal Mines

Abstract Currently, the implementation of a coal mine production safety monitoring system, capable of real-time monitoring of the safety condition of large coal mine equipment and timely dispatch management, is crucial for the efficient and safe operation of coal mine enterprises. This paper designs the coal mine major equipment failure process based on the rolling bearing’s vibration mechanism, analyzes the major equipment’s failure signal using both time domain and frequency domain analysis methods, and employs the intelligent fuzzy control method to identify the major equipment failure. We have designed and practically applied a real-time, multi-threaded, and comprehensive fault diagnosis and prediction system for coal mine major equipment, combining information from coal mine safety, production, and operation. The results show that the normal state’s time-domain amplitude approximates (-0.2, 0.2), and the time-domain waveforms all display periodic shock signals when the bearing sustains damage. The faulty vibration amplitude of the inner ring is within 0.08 ms 2 and the faulty vibration amplitude of the outer ring is within 0.44 ms 2, and the diagnostic accuracy using the method of this paper reaches 97.2%. This study demonstrates the effectiveness of remote monitoring and fault analysis of major equipment in coal mines, which effectively improves the equipment start-up rate and ensures coal mine production safety.

Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

Remote Fault Diagnosis Method for Traction Drive Systems Based on Domain-Adapted Knowledge Graphs

Remote Fault Diagnosis Method for Traction Drive Systems Based on Domain-Adapted Knowledge Graphs

Remote monitoring and fault diagnosis of transport ships and engineering dredging ship propulsion systems based on data fusion algorithm

Remote monitoring and fault diagnosis of transport ships and engineering dredging ship propulsion systems based on data fusion algorithm

Smart Home Automated Control System using Android Application Based on Arduino

Smart Home System (SHS) is a dwelling incorporating a communications network that connects the electrical appliances and services allowing them to be remotely controlled, monitored or accessed. SHS includes different approaches to achieve multiple objectives range from enhancing comfort in daily life to enabling a more independent life for elderly and handicapped people. In this paper, the main four fields for SHS which are, home automation and remote monitoring, environmental monitoring, including humidity, temperature, fault tracking and management and finally the health monitoring has been considered. The system design is based on the Microcontroller MIKRO C software; multiple passive and active sensors and also a wireless internet services which is used in different monitoring and control processes. This paper presents the hardware implementation of a multiple- form control system for house automation and combines both hardware and software technologies. The system results show that it can be classified as a comfortable, secure, private, economic and safe system in addition to its great flexibility and reliability.

Retracted: Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

Retracted: Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

Seismological Indicators of Geologically Inferred Fault Maturity

AbstractVariations in fault zone maturity have intermittently been invoked to explain variations in some seismological observations for large earthquakes. However, the lack of a unified geological definition of fault maturity makes quantitative assessment of its importance difficult. We evaluate the degree of empirical correlation between geological and geometric measurements commonly invoked as indicative of fault zone maturity and remotely measured seismological source parameters of 34MW ≥ 6.0 shallow strike‐slip events. Metrics based on surface rupture segmentation, such as number of segments and surface rupture azimuth changes, correlate best with seismic source attributes while the correlations with cumulative fault slip are weaker. Average rupture velocity shows the strongest correlation with metrics of maturity, followed by relative aftershock productivity. Mature faults have relatively lower aftershock productivity and higher rupture velocity. A more complex relation is found with moment‐scaled radiated energy. There appears to be distinct behavior of very immature events which radiate modest seismic energy, while intermediate mature faults have events with higher moment‐scaled radiated energy and very mature faults with increasing cumulative slip tend to have events with reduced moment‐scaled radiated energy. These empirical comparisons establish that there are relationships between remote seismological observations and fault system maturity that can help to understand variations in seismic hazard among different fault environments and to assess the relative maturity of inaccessible or blind fault systems for which direct observations of maturity are very limited.

Research on Fault Diagnosis System of IOT for Oil Well Pump Based on Machine Learning

Abstract In order to realize automatic prediction and processing of remote fault diagnosis of oil well pumps distributed in different regions by crude oil production enterprises, a fault diagnosis system for oil well pumps based on machine learning was researched and designed. This fault diagnosis system is composed of three layers (perception layer, network layer and control application layer) Internet of Things structure. The function and characteristics of each layer are analyzed in this paper, and the hardware composition and control principle of sensor nodes and aggregation nodes of the measurement and control system are discussed and gives the node microcontroller program design flow chart and the main module content of the IoT central computer software design. This paper focuses on the principle of machine learning for fault diagnosis and prediction, and deeply explains the algorithm steps of using LSTM for fault diagnosis of oil well pumps. The enterprise application experiment results show that, compared with the traditional manual well patrol fault diagnosis method, this system has the advantages of convenient operation and maintenance, low labor intensity, high timeliness and accuracy of fault diagnosis, which can better reduce equipment maintenance costs for enterprises.

Cloud Server-Assisted Remote Monitoring and Core Device Fault Identification for Dynamically Tuned Passive Power Filters

Reliability and safety are crucial for the operation of a dynamically tuned passive power filter (DTPPF). Safe performance of DTTPFs implies complete normal filtering without failure within a specified period. To prevent potential disaster or economic loss, it is desirable to achieve early warning of any core device faults in a DTPPF based on its running state and to optimize its harmonic mitigation performance. In this paper, we explore effective methods for identifying core device faults in DTPPFs. First, we summarize the characteristic parameters of faults, running state parameters, parameters required for fault monitoring, and fault type parameters. Then, a cloud server-assisted remote monitoring and fault identification system for DTPPF is proposed, which consists of monitoring system’s architecture and cloud servers’ software architecture as well as software design of the back-end service layer and functional design of the front-end application layer. Our experiments demonstrate that the proposed system can monitor the real-time operational status of the DTPPF, enabling remote diagnosis and identification of core device faults. Moreover, it is user-friendly, as it is capable of optimizing equipment maintenance schedules and utilizing manufacturers’ service capacities. Therefore, this research provides a theoretical foundation for harmonic mitigation in low-voltage distribution networks and is valuable for practical engineering applications in industrial power grids.

Remote fault detection and location of power fiber optic cable based on a logistic regression model

Abstract The current fiber optic communication network presents the characteristics of network scale, complexity, and efficiency, and the use of traditional means for the maintenance of power fiber optic cable has been greatly limited. In this paper, based on the characteristics of simple structure, convenient construction, strong randomness, strong sequence autocorrelation, and good iterative performance of chaos theory in a logistic regression model, a logistic-based remote fault detection and location system for power fiber optic cables is proposed. The fault location test is carried out through with TMS200 series fiber optic cable automatic monitoring management system and GIS method. The fault location error of the logistic-based fault detection method is less than ±3m, that of the GIS method is less than ±10m, and that of the TMS200 series cable automatic monitoring and management system is less than ±15. The comparison shows that this paper’s fault detection and location system is more accurate and can help maintenance personnel have a faster and more comprehensive understanding of the line situation. Realize the rapid repair work of faults, which provides great help to maintain the fiber optic cable lines.

Distribution Automation: Enhancing Efficiency and Reliability in Power Distribution Systems

This paper investigates the importance of distribution automation in power distribution systems. The introduction highlights the challenges faced by traditional distribution systems, such as high losses, inefficient processes, and outdated infrastructure. Distribution automation, referred to as smart grid technology, is a transformative solution that integrates advanced technologies and automation devices to enhance power distribution, operational efficiency, and system reliability. This paper provides a comprehensive examination of various distribution automation devices, such as remote fault indicators, smart relays, automated switches and reclosers, automated capacitors, voltage regulators and load tap changers, feeder monitors, and transformer monitors. The importance of distribution automation is emphasized, including enhanced reliability, improved operational efficiency, enhanced customer satisfaction, and the integration of renewable energy resources. Opportunities for distribution automation, such as enhanced reliability, improved operational efficiency, enhanced data collection and analysis, integration of distributed energy resources, and demand response programs, are highlighted. Furthermore, the challenges associated with distribution automation, such as the cost of implementation, technical complexity, cybersecurity risks, workforce training and transition, and regulatory and policy framework, are discussed. Addressing these challenges requires collaboration among utilities, regulators, policymakers, and technology providers. The successful implementation of distribution automation can revolutionize power distribution, leading to more efficient, reliable, and sustainable electricity supply.

A pluggable module for enabling a trusted edge device management system based on microservice

In the development of the intelligent Internet of things, edge computing plays a key role. Not only that it has the characteristics of a quick response, but it can also effectively reduce the burden of cloud computing. In addition, it can also extend the application of network edge through the coexistence and collaboration with the cloud system. However, with a large number of edge devices being deployed, the previous remote device management system will face the challenges of resource constraints and software firmware compatibility. In addition, implementing remote fault management, configuration management, accounting management, performance management, and security management through edge device management is a key task. Therefore, this study has designed and implemented a trusted edge device management system based on a microservice and a plug-and-play hardware management device. The running state of the edge device, the functions of remote device control, and system restart are monitored remotely by in- and out-of-band management modes. In addition, in terms of data transmission security, the design concept of a trusted platform module is introduced to realize data encryption and authentication, and ensure security and reliability. In this paper, the operational feasibility of management services based on containerized microservice in edge devices is verified with system benchmarking tools without affecting system performance. According to the experimental results, the proposed architecture in this study can be effective in edge device management.

Outage Management of Active Distribution Systems With Data Fusion From Multiple Sensors Given Sensor Failures

Complex scenarios of multiple faults, sensor failures, and the fault current from distributed generations (DGs) pose a challenge for efficient outage management in distribution systems. To solve the issue, this study proposes a new algorithm for sequential determination of outage scenarios with data fusion from multiple sensors, i.e., smart meters and remote fault indicators (RFIs). To this end, an active distribution system is modeled as an undirected graph with vertices representing load zones, protective devices and RFIs, and edges denoting their adjacency. Set operation is proposed to derive the expected RFI alarms for each load zone if faulted and smart meter outage reports for each protection device. This serves as an input to the proposed evidence-driven methodology to sequentially quantify the credibility of the potential outage scenarios supported by RFIs and smart meters. By ranking the credibility, the most credible scenario of the activated protection and fault location, either on the primary feeder or laterals, is determined for expedited power recovery. Test cases of the IEEE System and a utility circuit show that the proposed approach outperforms the state-of-the-art technologies in terms of its accuracy and computational efficiency, leading to more efficient outage management of distribution systems.

Equilibrium analysis of doubly fed-based variable-speed pumped storage unit under remote grid fault

As the flexible-controlled power source in the power system, the doubly fed-based variable-speed pumped storage unit (DF-VSPSU) has a significant role in the power rebalance and the reactive power support. However, as with the voltage sag in the remote grid side, the DF-VSPSU with the conventional low-voltage ride-through control may lose the synchronization and result in instability due to the absence of an equilibrium point. Thus, taking a DF-VSPSU infinite system as a case, the generator-side, and the grid-side model is well established. The large disturbance instability mechanism of doubly fed-based variable-speed pumped storage unit under remote grid faults is explained in detail, which is caused by the absence of the equilibrium point. Together with the key influencing factors, including the voltage depth and the short circuit ratio, the available gain of the reactive current control is presented. Finally, the availability of the theoretical analysis is demonstrated based on MATLAB/Simulink.

An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things

In this paper a novel embedded system for remote monitoring and fault diagnosis of photovoltaic systems is introduced. The idea is to embed machine leaning algorithms into a low-cost edge device for real-time deployment. First, an artificial neural network is developed to detect faults. Then an effective stacking ensemble learning algorithm is developed to classify the nature of the fault. The method performance is evaluated through common error metrics such as RMSE, MAE, MAPE, r and confusion matrix. Additional algorithms are also embedded into the edge device in order to remotely control the photovoltaic array parameters. Users can be notified by email and SMS about the state of their photovoltaic array. The Blynk IoT platform is used to monitor remotely the photovoltaic array parameters. The experimental results demonstrate the ability of the proposed embedded system to diagnose and monitor the photovoltaic array with a good accuracy.

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.

Remote Fault Identification and Analysis in Electrical Distribution Network Using Artificial Intelligence

This research describes a method for wavelet decomposition and machine learning-based fault site classification in a radial power distribution network. The first statistical observation is produced using wavelet decomposition and wavelet-based detailed coefficients in terms of Kurtosis and Skewness parameters. For this objective, six distinct machine learning methods are deployed. They are evaluated and compared using unknown data sets with varying degrees of unpredictability. One approach has been shown to be the most accurate in locating the location of the problem bus.