Fault Identification System Research Articles

Research on Equipment Fault Identification Method and System based on Big Data Correlation Analysis

With the rapid progress in the industrial field, equipment fault identification plays a crucial role in improving production efficiency and reducing operating costs. This article proposes a specialized and efficient equipment fault identification method and system based on big data correlation analysis. This system achieves precise identification of equipment faults by finely collecting and preprocessing equipment operation data, deeply exploring potential association rules between data. The method proposed in this article not only improves the accuracy of fault recognition, but also significantly enhances recognition efficiency, providing strong support for intelligent and refined management in the industrial field.

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.

MTG_CD: Multi-scale learnable transformation graph for fault classification and diagnosis in microservices

The rapid advancement of microservice architecture in the cloud has led to the necessity of effectively detecting, classifying, and diagnosing run failures in microservice applications. Due to the high dynamics of cloud environments and the complex dependencies between microservices, it is challenging to achieve robust real-time system fault identification. This paper proposes an interpretable fault diagnosis framework tailored for microservice architecture, namely Multi-scale Learnable Transformation Graph for Fault Classification and Diagnosis(MTG_CD). Firstly, we employ multi-scale neural transformation and graph structure adjacency matrix learning to enhance data diversity while extracting temporal-structural features from system monitoring metrics Secondly, a graph convolutional network (GCN) is utilized to fuse the extracted temporal-structural features in a multi-feature modeling approach, which helps to improve the accuracy of anomaly detection. To identify the root cause of system faults, we finally conduct a coarse-grained level diagnosis and exploration after obtaining the results of classifying the fault data. We evaluate the performance of MTG_CD on the microservice benchmark SockShop, demonstrating its superiority over several baseline methods in detecting CPU usage overhead, memory leak, and network delay faults. The average macro F1 score improves by 14.05%.

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

DT is the digital representation of physical systems to simulate real situations in a digital version. Here, the DT of the GaT-AE is created for the Fault Detection (FD) process. The GaT is a type of internal combustion engine that uses the air as the working fluid to propel the flight. However, the incorporation of big data analysis in the DT model is challenging in previous works. So, a multimodal GaT-AE fault identification system is proposed. First, the 3D print of the GaT-AE is created. Then, the data is collected from both built-in sensors and DT. Afterward, the multimodal data is pre-processed and balanced by using HT-QNN and QADASYN, respectively. Next, the features are processed by GT-PFS, and finally, the faults are detected by Geo-TLSVM. The analysis proved that the developed model outperformed the other state-of-the-art model with its effective FD rate of 97.24%.

Energy Management System of Luminosity Controlled Smart City Using IoT

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.
 OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.
 METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.
 RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.
 CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

Improving fault identification in smart transmission line using machine learning technique

<span lang="EN-US">In this work inevitable for power transmission boards such as Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) to look for a low-cost communication system with low power usage and to improve supply reliability, to transmit reliable fault information back to the control centre in real time. This work aims to design an automated and effective fault identification and position system for all overhead power transmission network networks using all current fault indicator technologies, machine learning methods, and commercially tested communication technology to easily and reliably pin a transmission system's flawed point parts. This will help to people avoid touching the electrical wire and prevent electrical shocks and current wastage as well. Smart transmission lines have played a decisive role in developing human protection and preventing current wastage. The transmission line is opened and the state of the line is evaluated, and the information goes to electrical board (EB) office. The system monitors the data by sending the alert message to the person responsible for the GPS location, either via SMS or BUZZER, or by displaying the alert message lives. Transmission line distribution is broad and most of them are spread around the geographical environment.</span>

Implementation of Fast Fourier Transform and Artificial Neural Network in Series Arc Fault Identification and Protection System on DC Bus Microgrid

A microgrid is a cluster of electrical sources and loads that are interconnected and synchronized. Microgrid operation is typically divided into two modes, isolated or connected to the grid with a single or standalone control system. In this context, it can enhance the reliability and quality of electricity supply for connected customers. When using a microgrid system, it is important to consider the risk of series arc faults. Series arc faults are sudden bursts of flames resulting from ionization of gas between two electrode gaps. These faults can occur due to manufacturing defects, installation Errors, aging, or corrosion on conductor rods, leading to imperfect connections. Detecting series arc faults in DC microgrid system operations can be challenging using standard protective devices. Failure in the protection system can pose risks of fire, electrical shock hazards, and power loss in the DC microgrid.Therefore, a device has been designed to detect series arc faults by utilizing the fast Fourier transform method and artificial neural network, which function to analyze DC signal and make decisions when faults occur by examining the average sum of current frequency values during normal and fault conditions. In this study, the average sum of current frequency values during normal conditions was found to range from 0.35437 to 0.36906 A, while during fault conditions, it ranged from 0.21450 to 0.22793 A, with an average protection identification time of 1087 ms and an ANN output accuracy of 99.98%.

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.

Location of faults based on deep learning with feature selection for meter placement in distribution power grids

Abstract A problem of great interest for power distribution companies is ensuring uninterrupted service in extensive power distribution systems. Thus, the monitoring of networks and identification of system faults become essential. This work focuses on identifying a fault’s occurrence from a small number of low-cost measurements in a power distribution system. The determination of sensor locations is based on the recent feature selection approach LassoNet, where the measurement locations are ranked. It provides the most informative measures during a fault resulting in a shortening data set. It is used as input to a deep neural network without a significant loss in accuracy. We validate our method on the IEEE 13 and 34 node test feeders for distribution systems to conduct the suggested approach’s experimental studies.

A Survey on Fault Identification System in OTN using Neural Network

The diversity of information and services that are sent via optical networks is growing day by day, hence the survivability of optical networks has emerged as a significant research issue in recent years. The location of the fault in an optical transmission network (OTN) is very important when investigating the survivability of optical networks. The first step is to create network model with three channel, after which it is necessary to analyze common alarm data, as well as fault monitoring points and common fault sites. The fault location field of OTN incorporates the artificial neural network (ANN), which is used to identify whether or not a probable fault site is present. In this paper, we had done a review of earlier research work in this field and identified the research gap in the introduction of neural networks into the area of fault identification in optical transport networks.

A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems.

Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

Application Research of Power Battery System Fault Identification Method Based on Multi-Classifier Fusion

In this paper, a module fault identification method for a power battery system based on multi-classifier fusion is designed. Taking the inverter single tube open circuit fault of the power battery system as an example, the deep learning method of deep neural network and convolutional neural network is used to build the primary diagnosis layer. The fusion method of evidential reasoning rules is used to fuse the primary diagnosis results. The fault data are obtained through simulation of the simulation model, which verifies that the scheme avoids the disadvantage of unstable diagnosis results of a single classifier, and obtains more stable diagnostic results with higher diagnostic accuracy.

Fault Diagnosis of Gearbox Using Machine Learning Approach

Gear box is crucial in industrial processes, enabling adjustments of speed and load conditions to meetoperational needs. As gearboxtechnologyadvances, their capabilities increase, but component failure can result in product losses and maintenance costs. Detecting potential failures before handis essential, and vibration measurementis a proven method for monitoring machine condition and predictinggearboxfaults. This study explores the use of machine learning to develop an automated fault diagnosis system for gear boxes using vibration signals. The performance of the developed model is compared with existing methods to determine the most effective algorithm. This research paper explores the application of machine learning techniques for faultdiagnosis of gear boxes using vibration signals. The study involved collecting vibration data fromgearboxes in both good and defective conditions, under various loading conditions. Statistical features were extracted from the collected data and used to develop a fault identification system. The performance of the developed model was evaluated and compared with existingmethods. The study also aimed to determine the mostsuitablealgorithm for the collected data. Overall, the paper provides insights into the effectiveness of using machine learning approaches for gear box fault diagnosis and identifies the best-performing algorithm for this task. Key Word: Machine Learning, Gearbox, Vibration, Data preprocessing, Algorithms.

A smart fault identification system for ball bearing using simulation-driven vibration analysis

A smart fault identification system for ball bearing using simulation-driven vibration analysis

On-site fault experiment and diagnosis research of the carbon dioxide transcritical heat pump system for energy saving

Carbon dioxide transcritical heat pump air conditioning has an excellent performance in heating conditions, and is a very potential heating alternative, which will help to improve the energy saving level of buildings. The occurrence of the fault will lead to an increase of energy consumption, damage of system components and other consequences. However, the current research focuses on the heat pump system with Freon as refrigerant, and the experimental research on the fault of the carbon dioxide transcritical heat pump system has not been studied. Therefore, this research carried out the operation experiment of the carbon dioxide heat pump with eight fault types under the actual conditions in winter. The operation characteristics under different fault conditions are analyzed. Based on the fault characteristics, a causality table is proposed, which can quickly identify the fault type when there are at least 6 characteristic variables. Besides, it is concluded that after the fault occurs, the energy consumption will increase directly or indirectly. Most of the faults will directly cause the decrease of system heating capacity and reduce indoor thermal comfort. The research results will contribute to the early identification of carbon dioxide system faults and the improvement of building energy efficiency.

Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation

Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation

Novel PMU Model for Dynamic State Disturbance Analysis with Effective Data Handling System

In this paper a hybrid DFT phasor calculation method is presented. This method is used to calculate the fundamental component phasor value of the harmonic signal without any physical filter. With this method the computational time for each phasor value calculation is reduced and the calculated phasor value has the constant magnitude and rotating phase angle. This calculated phasor values are used for the disturbance or fault identification in the power system based on Total Vector Error (TVE). The %TVE-based fault identification is more effective, because the TVE value is calculated with reference phasor value. If any fault/disturbance occurs or frequency changes then %TVE value changes. This change in TVE value is reflected in the calculation of the next sample (1/f.*N sec), giving the advantage to this method as compared to the other magnitude-based fault identification systems. Normally with the phasor calculation large data is produced, which requires large memory for the storage of this data and makes the analysis difficult. To avoid large data storage system conditional-based data storage system is proposed, where the data is stored during only the disturbance conditions or at every one second. With this technique, the data to be stored is reduced and the analysis of this data also becomes simpler for the post disturbance and for future load prediction. The performance of the proposed method is evaluated in terms of accuracy of calculation and its implementation ability. The simulation results are as per the IEEE C37.118.1a2014 for the power system monitoring and fault identification.

Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data

Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in design, capacity, and chemical composition. In addition, the battery monitoring systems of the various vehicles are also diverse, so communication across the board is not available or can only be achieved with significant difficulty. Hence, unique type-dependent data queries and filtering are necessary in most cases. In this paper, a Volkswagen e-Golf electric vehicle is investigated; communication with the vehicle was implemented via an onboard diagnostic port (so-called OBD), and the data stream was recorded. The goal of the research is principally to filter out, identify, and localize defective/weak battery cells. Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels. The methodology demonstrated in this article uses a fault diagnosis technique based on voltage abnormalities. In addition, it employs a hybrid algorithm that executes calculations on measurement and recorded data. In the evaluation, a status line comprising three different categories was obtained by parametrizing and prioritizing (weighting) the individual measured values. It allows the cells to be divided into the categories green (adequate region), yellow (to be monitored), and red (possible error). In addition, several querying strategies were developed accordingly to clarify and validate the measurement results. The several strategies were examined individually and analyzed for their strengths and weaknesses. Based on the results, a data collection, processing, and evaluation strategy for an electric vehicle battery system have been developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery.

Intelligent Fault Quantitative Identification for Industrial Internet of Things (IIoT) via a Novel Deep Dual Reinforcement Learning Model Accompanied With Insufficient Samples

Industrial Internet of Things (IIoT) is mainly a data-oriented network, so intelligent processing of massive data is desiderated to realize the interconnection between machines. Currently, deep-learning-based methods are widely applied for intelligent construction of the IIoT, so as to maximize the self-monitoring and self-management capabilities of various machines. However, the quantity and quality of data and the optimization of parameters greatly limit the properties of such methods. As a breakthrough of artificial intelligence (AI), deep reinforcement learning (DRL) provides inspiration and direction, which combines the advantages of deep learning and reinforcement learning to construct an end-to-end fault identification system. Therefore, a novel deep dual reinforcement learning model was proposed, which consisted of an actor model and a critic model. The dual structures avoid the over-self-optimization of the network. The action model continually learns the knowledge of identifying unknown samples by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$greedy$ </tex-math></inline-formula> algorithm, while the critic model dynamically adjusts the policy to guide the action model in right training direction. The effectiveness of the proposed method was verified by three bearing data sets. The results indicate that the proposed method enables agents to independently realize precise fault quantitative identification. The establishment of an experience storage unit overcomes the problem of insufficient samples, which avoids blind trial and error of the proposed mode.

A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.