Fault Management Research Articles

Experimental investigation of machine-learning-based soft-failure management using the optical spectrum

The demand for high-speed data is exponentially growing. To conquer this, optical networks have undergone significant changes, getting more complex and versatile. The increasing complexity necessitates that the fault management be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning-based soft-failure management framework. It utilizes a variational autoencoder-based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identify unknown failure types. The investigations show that the VAE-GAN outperforms the other machine learning algorithms when up to 10% of the total training data are available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

An overview of the Legio fault resilience framework for MPI applications

While the computational power of HPC clusters breached through the exascale milestone, it highlighted the need for some critical features like fault management. The current de facto standard for inter-process communication at scale, MPI, lacks proper fault management functionalities, precluding result production in the presence of faults. Many efforts have already addressed the problem, with the ULFM extension and Reinit proposal being the two works receiving the most attention. While powerful and effective, they require expertise from the user and significantly impact the application code. For this reason, we presented the Legio fault resilience framework, which can introduce graceful degradation properties in MPI applications with minimal changes. This work summarises the development and evolution of Legio, highlighting successful use cases and providing some tips for users who want to leverage it for their executions.

Stacked AutoEncoder based diagnosis applied on a Solar Photovoltaic System

This paper deals with the modeling of a photovoltaic system connected to a grid for the simulation of normal and faulty operations and the generation of a data-set for learning a fault detection algorithm based on a Stacked Autoencoder. To evaluate the effectiveness of the proposed approach, a Mean Squared Error is used. This method enables early fault detection, enhancing system relability and efficiency while addressing the need for proactive fault management in the system under normal conditions. Obtained results under different radiation and temperature conditions highlight the relevance of the proposed model and the effectiveness of the fault detection algorithm.

Intelligent agents for advanced power system protection schemes

This study explores the incorporation of intelligent agents to improve power system safety, using several computational models including machine learning, rule-based systems, neural networks, and fuzzy logic. The research assesses the effectiveness and efficiency of these agents in promptly identifying, categorizing, and responding to faults in the power system architecture using empirical analysis. The results demonstrate the higher performance of agents based on neural networks, with an average improvement in fault prediction accuracy of 38% compared to systems based on rules. Furthermore, the evaluation of power system devices demonstrates a direct relationship between greater voltage ratings and increased expenses for both installation and maintenance, underscoring their crucial importance within the system. An examination of fault severity reveals that greater severity failures have a direct and significant influence on system downtime. These problems lead to longer interruptions, which emphasizes the need of implementing effective fault management systems. Intelligent agents' actions have different costs and reaction times. Actions based on neural networks have lower average costs and shorter response times, demonstrating their cost-effectiveness and efficiency in addressing faults. The study of percentage change highlights the importance of using various kinds of intelligent agents and higher-rated devices. This research offers insights into performance differences and the consequences for optimizing protection measures. This research provides a thorough understanding of how intelligent agents may enhance power system protection. It also offers guidance for future improvements in creating power grid infrastructures that are robust, dependable, and adaptable.

Online Condition Monitoring of Industrial Loads Using AutoGMM and Decision Trees

Condition monitoring and fault management approaches can help with timely maintenance planning, assure industry-wide continuous production, and enhance both performance and safety in complex industrial operations. At the moment, data-driven approaches for condition monitoring and fault detection are the most attractive being conceived, developed, and applied with less of a need for sophisticated expertise and detailed knowledge of the addressed plant. Among them, Gaussian mixture model (GMM) methods can offer some advantages. However, conventional GMM solutions need the number of Gaussian components to be defined in advance and suffer from the inability to detect new types of faults and identify new operating modes. To address these issues, this paper presents a novel data-driven method, based on automated GMM (AutoGMM) and decision trees (DTree), for the online condition monitoring of electrical industrial loads. By leveraging the benefits of the AutoGMM and the DTree, after the training phase, the proposed approach allows the clustering and time allocation of nominal operating conditions, the identification of both already-classified and new anomalous conditions, and the acknowledgment of new operating modes of the monitored industrial asset. The proposed method, implemented on a commercial cloud-computing platform, is validated on a real industrial plant with electrical loads, characterized by a daily periodic working cycle, by using active power consumption data.

Towards an interpretable data-driven switch placement model in electric power distribution systems: An explainable artificial intelligence-based approach

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%.

Design of an intelligent decision model for power grid fault location and isolation based on topology analysis

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

Study on The Applicability of Drones in The Process of Forming Towers of 500KV Transmission and Substation Lines in Highland Areas

With the continuous development of science and technology in China, China's aviation field has also been expanding, the rapid development of aviation technology, China has gradually achieved leapfrog development in UAV equipment, and gradually tends to civilization, practicality and commercialization. At present, more and more UAVs are not only opening up in the domestic market, but also have flowed to overseas markets, and the modern advanced technology represented by UAVs has gradually become the current trend development trend. In the field of electric power engineering construction, the use of drone technology is short and wide, and there are drones in both high-altitude areas and plain areas. In terms of the engineering field, the UAV technology involves the construction, monitoring and management of power grids, fault and equipment maintenance of power grids and transmission line inspection, etc.

A survey of episode mining

AbstractEpisode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

A Hybrid Model-Based and Data-Driven Framework for Automated Spacecraft Fault Detection

Traditional fault management can be an onerous task and robust automated solutions are increasingly necessary to accommodate the complexities of modern space systems and mission operations. The present work proposes a hybrid framework for performing automated spacecraft fault detection by leveraging the benefits of both model-based and data-driven approaches. The framework uses a system model to generate residual data that are subsequently fed into a data-driven residual analysis stage. The framework was verified by using data from a hardware-in-the-loop test campaign in which faults were injected into a spacecraft attitude control system, and successfully identified. The fault detection approach implemented using this framework outperformed results obtained from expert-tuned fault detection parameters. Overall, the proposed framework is a promising alternative for sustainable fault detection and mission operations suitable for complex space systems.

Eksekusi Jaminan Pada Pembiayaan Mudharabah Karena Force Majeur Ditinjau Dari Fatwa Dewan Syariah Nasional NO :07 DSN-MUI/IV/2000

BSI Kcp. Gunungtua carries out its business activities based on sharia principles. One of its business activities is mudharabah financing. In the implementation and resolution of mudharabah financing, there are still problems, practice in the field is still far from what is stated by the DSN-MUI fatwa. BSI Kcp. Gunung Tua has not really implemented the DSN-MUI Fatwa in its entirety, so that business actors receive capital financing from BSI Kcp. Gunung Tua is still obliged to return the capital in full, even if the capital manager (mudharib) experiences an accident or event beyond the management's fault (Overmacht). This research aims to determine (1) The concept of customer responsibility in mudharabah financing caused by Force Majeure based on the Fatwa of the National Sharia Council No: 07 DSN-MUI/IV/2000, (2) Implementation of collateral execution in mudharabah financing due to Force Majeure at BSI Kcp.Gunung Tua and (3) Is execution of collateral in mudharabah financing permitted due to force majeure based on National Sharia Council Fatwa No: 07 DSN-MUI /IV/2000. Mudharabah financing is permitted as long as it does not conflict with sharia principles. If a sharia financial institution uses a Mudharabah contract, the sharia financial institution must follow the provisions of the DSN Fatwa. The DSN Fatwa regarding mudharabah financing is DSNMUI Fatwa No.07/DSN-MUI/IV/2000. The research method used is a descriptive method, namely describing the implementation of collateral execution. in mudharabah financing, the primary data source is the manager of BSI Kcp.Gunung Tua, the research technique used is interviews. The results of this research can be concluded that (1) The concept of customer responsibility for mudharabah financing implemented at BSI Kcp. Gunung Tua is that customers must fulfill their obligations to make payments unless it is caused by an unintentional error (Force Majeure), (2) Execution of guarantees in mudharabah financing due to Force Majeure at BSI Kcp. Gunung Tua and (3) Is the execution of guarantees in mudharabah financing permitted due to force majeure based on National Sharia Council Fatwa No: 07 DSN-MUI /IV/2000.

A Robust Model-Based Strategy for Real-Time Fault Detection and Diagnosis in an Electro-Hydraulic Actuator Using Updated Interactive Multiple Model Smooth Variable Structure Filter

Abstract In industries where harsh environments and stringent safety requirements are prevalent, the widespread use of applications has made it essential to focus on fault detection and diagnosis (FDD) in hydraulic actuators. To achieve this, model-based FDD techniques are utilized, which employ estimation tools like observers and filters. However, for many applications, particularly in fluid power systems, observability, and parameter uncertainty pose constraints to extracting information and estimating parameters. To address these issues, an efficient form of interactive multiple model (IMM), called updated IMM (UIMM), is applied to an electro-hydraulic actuator (EHA) to detect and isolate persisting friction and leakage faults. UIMM method progresses through a series of models that correspond to the fault condition's progression instead of considering all models at once (as is done in IMM). This reduces the number of models running simultaneously, providing two significant benefits: enhanced computational efficiency and avoidance of the combinatorial explosion. The smooth variable structure filter with variable boundary layer (SVSF-VBL) is used for state and parameter estimation in conjunction with UIMM. SVSF-VBL is a reliable suboptimal estimation method that performs better than the Kalman filter regarding uncertainties related to the system and modeling. The performance of the UIMM method is validated by the simulation of fault conditions for a typical EHA. A fault tolerable control system (FTCS) has been designed to demonstrate the use of the proposed FDD strategy for fault management in a closed-loop system.

Smart grid technologies and application in the sustainable energy transition: a review

ABSTRACT The smart grid is a product of the advances in computer and communication technology and power electronics that creates a more resilient, reliable and one that supports a two-way flow of electricity and information. The smart grid enables more uptake of the variable renewables like wind, solar and variable loads like the plug-in cars and improves the efficiency of power systems and facilitate several products and services supported by the grid like automatic healing and re-routing of power in case of a fault and demand side management. The deployment and use of smart grids will enhance the realization of shared goals of grid stakeholders, promote energy security, enable economic growth, and help in the mitigation of climate change.

Fault detection and state estimation in robotic automatic control using machine learning

In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.

A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems

One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model.

Design and application of a fault diagnosis system for machine tool angle heads based on Boolean matrix filtering and an optimized BP neural network

In this paper, a fault diagnosis method based on Boolean matrix filtering and optimizing backpropagation (BP) neural networks is proposed for angle heads of computer numerical control (CNC) machine tools. The matrix filtering is first carried out with the fault case database and the fault cause symptom Boolean matrix according to the fault types and characteristics of the machine tool angle head. On the basis of the combination of multiple fault causes obtained from the initial filtering, the Euclidean distance method is used to narrow the results of fault cause filtering. The BP neural network model with weight vector is established and optimized to perform an accurate diagnosis. Finally, the fault diagnosis and management system of the angle head of a CNC machine tool, integrating with the Boolean matrix filtering method, Euclidean distance method, and BP neural network model, is developed and implemented with the Python language and QT development framework.

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

A Modular DC-DC Converter as a Hybrid Interlink Between Monopolar VSC and Bipolar LCC-Based HVDC Links and Fault Management

High voltage dc (HVdc) transmission has gradually gained its popularity in long distance transmission of power. The possibility of multi-terminal HVdc network has further projected HVDC as a potential mode of power trading across geographical boundaries. The emergence of modular multilevel converter (MMC)-based dc-dc converters has led to the interconnection of HVdc networks across voltage levels, converter technologies and line topologies. This paper puts forward a topological modification in an existing MMC-based dc-dc converter topology, acting as a hybrid interconnect between different converter technologies, for interface of an asymmetric monopolar voltage source converter (VSC) and a bipolar line commutated converter (LCC)-based HVdc links. The proposed operation of the topology is validated experimentally in a down-scaled laboratory prototype. The proposed topology is further investigated for short-circuit fault conditions in any of the poles of HVdc bus and appropriate fault mitigation strategies are adopted for short-cicuit fault in both LCC and VSC bus. The fault mitigation strategies are validated by simulation in Matlab/Simulink platform.

Fault Detection and Localization in Industrial IoT Systems using Deep Learning

In the ever-evolving landscape of Industrial Internet of Things (IoT) systems, the need for robust fault detection and localization has become paramount. Existing methods, while commendable, have faced certain limitations that necessitate the development of more sophisticated solutions. This work embarks on a journey to address these challenges and presents a novel approach to fault detection and localization, leveraging the formidable power of Deep Learning process. The limitations of existing methodologies primarily revolve around their inability to provide the required precision and accuracy in detecting and localizing faults within complex IoT deployments. These methods often struggle to handle the diverse contextual datasets that characterize modern industrial settings. Additionally, their computational efficiency often leaves much to be desired, hindering real-time fault response mechanisms. In response to these limitations, our proposed model introduces a hybrid framework. It combines Vector AutoRegressive Moving Average with Exogenous Variables (VARMAx) for precise fault localization and Convolutional Neural Networks (CNN) for robust fault detection. This fusion harnesses the strengths of both approaches, providing a comprehensive solution for fault management in industrial IoT systems. The advantages of this model are twofold. First, VARMAx offers exceptional accuracy in pinpointing the exact location of faults within the system, facilitating swift corrective actions. Second, the integration of CNN enhances fault detection by effectively capturing patterns and anomalies in the data, resulting in a highly responsive fault detection system. Notably, this model not only outperforms existing methods with a 3.9% boost in precision, 4.5% increase in accuracy, 4.9% higher recall, 8.5% improved AUC, but also boasts a remarkable 5.9% enhancement in computational speed, ensuring real-time responsiveness. The profound impact of this work extends to the realms of industrial automation and IoT system reliability. By addressing the limitations of existing approaches and introducing a robust fault detection and localization model, this paper paves the way for safer and more efficient industrial operations. The improved precision, accuracy, and speed of fault detection will minimize downtime, reduce maintenance costs, and ultimately elevate the performance and reliability of industrial IoT systems to new heights.