Open Fault Research Articles

HOOST: A novel hyperplane-oriented over-sampling technique for imbalanced fault detection of aero-engines

In general, training fault samples of aero-engines are very rare and only collected under one or a few operating conditions. However, due to diverse operating conditions and fault severities, testing fault samples may have higher diversity and larger distribution region than training fault samples, leading to a high missing detection rate. To address this issue, a hyperplane-oriented over-sampling technique (HOOST) is developed to synthesize training fault samples with higher diversity and larger distribution region. To be specific, HOOST not only adopts the interpolation strategy to synthesize in-distribution samples, but also adopts the extrapolation strategy to synthesize out-of-distribution samples. Moreover, the sampling factors in the extrapolation strategy are automatically set under the guidance of the initial classification hyperplane, rather than randomly selected, in order to improve the reasonableness of synthetic samples. Finally, the developed HOOST is integrated with a self-attention encoder-decoder. Extensive experiments are conducted, which not only validate the performance of the developed HOOST on an actual aero-engine dataset, but also demonstrate its generalizability on three other public industrial datasets.

Earthing System Analysis for Steel Tower Carrying 33kV Line

This study examines the critical analysis of earthing systems connected to steel towers that carry 33 kV power lines. Ensuring the safety and dependability of power transmission infrastructure becomes crucial in light of the growing demand for energy. By utilizing sophisticated computational methodologies and simulation approaches, this research carefully investigates how well the earthing system performs in various operational circumstances and fault scenarios. A thorough modeling technique is used in the analysis, which considers a number of variables including fault currents, tower design, soil resistivity, and grounding electrode configurations. In order to give a comprehensive understanding of the behavior of the system and its consequences for operational reliability, the study simulates many situations, including normal operation and fault occurrences. By using sophisticated numerical simulations and sensitivity analysis, the research pinpoints important factors affecting the earthing system's efficiency and suggests creative optimization techniques. These optimization techniques could involve changing the location of the grounding electrode, improving the materials used in the conductor, or putting additional safety precautions in place. The researcher's conclusions have important ramifications for the engineering community since they provide practical advice on how to strengthen the security and robustness of power transmission networks. The study adds to the continuous efforts to improve the efficiency and dependability of electrical grids by addressing potential weaknesses in the architecture of the earthing system. This thorough study advances the field of earthing system engineering by offering a solid platform for next studies and real-world implementations. Through this work, we can better understand the dynamics of earthing systems and optimization methodologies, which will help build more resilient and sustainable power transmission infrastructure that can adapt to society's changing energy needs.

Sensitivity analysis of commutation failure in multi-infeed HVDC systems: Exploring the impact of ac system representations and fault types

This paper investigates commutation failure (CF) in line commutated HVdc converters within multi-infeed HVDC systems and thereby provides valuable insights for the operation and design of HVdc systems. CF susceptibility across various operational scenarios, fault types, and ac system representations at different frequencies is explored. An Electromagnetic Transients (EMT) model is constructed and parameter changes are applied using Monte-Carlo Simulation. The process is fully automated with the use of a controlling program written in Python, which varies parameter values and facilitates result retrieval and post-simulation analysis. A typical analysis results in several hundred thousands of simulation runs, requiring the procedure to be implemented on a parallel computing platform. The results show that two-phase faults are the most critical CF triggers, an aspect often overlooked in previous literature focused on three-phase or single-phase-to-ground faults. Furthermore, the frequency dependent characteristics of the ac network can result in very different CF susceptibilities.

Fault Diagnosis of Hydropower Units Based on Gramian Angular Summation Field and Parallel CNN

To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Transfer learning-based updating method of transient stability assessment model

An updating method for data-driven based transient stability assessment model of power systems is proposed in this paper. To cope with potential faults in the real-time operation of power systems, the parameters of models can be modified online through the proposed method. Firstly, according to the long-short term memory (LSTM) network, transient stability assessment models are initially trained offline by expected faults. Then, the feature distribution differences between expected faults and potential faults are evaluated by the time-sequence maximum mean discrepancy (TMMD). Compared with the traditional maximum mean discrepancy (MMD), the proposed TMMD approach takes the temporal properties of transient stability into account, which can reflect the relationship between fault samples and time series better. Finally, the parameters of models are adjusted by transfer learning to reduce the feature distribution differences, by which the updated models can be more suitable for the different but related potential faults. Therefore, through the proposed model updating method, the extensibility of evaluation models is greatly enhanced, which is more in line with the practical conditions of power systems. In this paper, the effectiveness of the proposed method has been demonstrated by the evaluation results in the IEEE 39-bus system and a realistic power system.

Implementation and Possibilities of Fuzzy Logic for Optimal Operation and Maintenance of Marine Diesel Engines

This paper explores the implementation and possibilities of utilizing fuzzy logic theory for optimal operation and early fault detection in marine diesel engines. It emphasizes its role in managing the complexity and ambiguity inherent in engine performance and preventive maintenance. Preventive maintenance is crucial for ensuring the reliability and longevity of marine diesel engines. Implementing fuzzy logic control (FLC) systems can enhance the preventive maintenance strategies for these engines, leading to reduced downtime, lower maintenance costs, and improved overall performance. Through a comprehensive literature review and analysis of a case study, this paper demonstrates the adaptability, effectiveness, and transformative potential of fuzzy logic systems. Focusing on applications such as engine speed control, performance improvements, and early fault detection, the paper highlights the implementation of fuzzy logic for enhanced predictive capabilities. The study aims to offer a flexible approach to engine management through fuzzy logic, laying the way for significant improvement in optimal marine diesel engine operation.

Dynamic investigation of the electromechanical coupling system in a permanent magnet direct-drive locomotive under rotor eccentricity faults

In the traction system of a direct-drive locomotive, the gearbox design is eliminated, enabling the direct transmission of output torque from the traction motor to the wheelset. Due to this unique structure, rotor eccentricity, which is a common fault in traction motors, can have a direct impact on the dynamic performance of key components such as the motor, bogie, and wheelset in locomotives. In order to investigate the dynamic response of the direct drive locomotive under rotor eccentricity faults, a comprehensive electromechanical coupling model that considers the dynamic motion of the rotor is established in this paper. The coupling effect between the mechanical system and the electrical system, as well as the dynamic forces induced by rotor eccentricity, are taken into account in the model. The simulation results reveal that ripple torque has an obvious effect on rotor dynamics behaviour, while rotor air-gap eccentricity and mass eccentricity exhibit varying degrees of influence on the dynamic performance of the vehicle system. Furthermore, the influence laws of rotor eccentric distance on the vibration responses of critical locomotive components are revealed. These findings provide valuable theoretical guidance for the operational maintenance and fault diagnosis of traction motors in direct-drive locomotives.

Voltage‐based fault arc detection based on PCA‐RF

SummaryThe arc fault characteristics of certain loads lack significance, making it difficult to efficiently detect the line current characteristics. This research presents a novel approach for detecting arc faults using a combination of principalc analysis (PCA) and Random Forest (RF) based on voltage measurements. The time‐domain eigenvalues of the load terminal voltages of single and mixed loads are initially extracted during both arc fault and normal operation. Principal component analysis is then conducted on a subset of these eigenvalues. The skewness and magnitude features of the resulting principal components and load terminal voltages are utilized as inputs for the Random Forest algorithm. After training the model, classification results are obtained. Ultimately, it is contrasted with techniques such as rime optimization algorithm‐multilayer perceptron (RIME‐MLP), convolutional neural network‐gated recurrent unit‐SE attention (CNN‐GRU‐SE), and Kepler optimization algorithm‐support vector machine (KOA‐SVM). The results demonstrated that the approach exhibits superior accuracy and a reduced false alarm rate.

Batch Job Active-Active Resiliency System and Method

This manuscript presents a robust solution for orchestrating batch job workflows in an active-active resiliency mode across multiple regions. The proposed system abstracts the complexities of job and step/task state management, provides automated and customizable retries, and ensures auditability and observability. The system maintains operational continuity and fault tolerance, leveraging lock marker files and a configurable scheduler, even during regional failures. This approach addresses the challenges of asynchronous workflow management, offering enhanced visibility, control, and efficiency in processing batch jobs.

Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning

This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model’s adaptability and robustness, facilitating precise fault identification under varied conditions. Key to ATASML’s approach is its novel use of adversarial examples and data-augmentation techniques, including noise injection and temporal warping, which extend the model’s exposure to diverse operational scenarios and fault manifestations. This enriched training environment significantly boosts the model’s ability to generalize from limited data, a critical advantage in industrial applications where anomaly patterns frequently vary. The framework’s performance was rigorously evaluated on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Skoltech Anomaly Benchmark (SKAB), which are representative of complex industrial systems. The results indicate that ATASML outperforms conventional meta-learning models, particularly in scenarios characterized by few-shot learning requirements. Notably, ATASML demonstrated superior accuracy and F1 scores, validating its effectiveness in enhancing fault-diagnosis capabilities. Furthermore, ATASML’s strategic incorporation of task sequencing and adversarial tasks optimizes the training process, which not only refines learning outcomes but also improves computational efficiency. This study confirms the utility of the ATASML framework in significantly enhancing the accuracy and reliability of fault-diagnosis systems under diverse and challenging conditions prevalent in industrial processes.

Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey

Abstract Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

Preprocessing and Modeling Approach for Gearbox Pitting Severity Prediction under Unseen Operating Conditions and Fault Severities

Gear pitting is a common gearbox failure mode that can lead to unplanned machine downtime, inefficient power transmission and a higher risk of sudden catastrophic failure. Consequently, there is strong incentive to create machine learning models that are capable of detecting and quantifying the severity of gearbox pitting faults. The performance of machine learning models is however highly dependent on the availability of training data and since training data for a wide variety of different operating conditions and fault severities is rarely available in practice, machine learning models must be designed to be robust to unseen operating conditions and fault severities. Furthermore, models should be capable of identifying data outside of the training data distribution and adjusting the confidence in a prediction accordingly. This work presents a strategy for pitting severity estimation in gearboxes under unseen operating conditions and fault severities in response to the PHM North America 2023 Conference Data Challenge. The strategy includes the design of dedicated validation sets for quantifying model performance on unseen data, an investigation into the most appropriate preprocessing methods, and a specialized convolutional neural network with an integrated out of distribution detection model for identifying samples from foreign operating conditions and fault severities. The results show that the best models are capable of some generalization to unseen operating conditions, but the generalization to unseen pitting severities is more challenging.

Review of Recent Patents on Smart Bearing

Background: Smart bearing is based on the traditional bearing, which is integrated by sensing devices and control devices, formating a unique bearing structure unit. It uses information processing, automatic control, and other technologies to achieve real-time online monitoring of bearing operating conditions, fault detection, and state control. Compared with ordinary bearings, smart bearings have the characteristics of self-awareness, self-decision, and self-regulation. By analyzing the composition of various types of smart bearings and their working principles, it is beneficial to prospect the future development direction of bearings. Objective: We analyze and organize the patents on smart bearings in recent years, summarize the problems encountered in the development of smart bearings, and propose the development direction of smart bearings in the future to provide a reference for future scientific research. Methods: This paper reviews the patents related to smart bearings and analyzes the composition and structure of smart bearings. The main smart bearings include rolling bearing, sliding bearing, magnetic bearing, and rubber bearing. The principle and structure of these bearings are analyzed. Their advantages and shortcomings are discussed. Results: Through the analysis of relevant patents and papers on smart bearings, the current development status and problems of smart bearings are summarized, the future design of smart bearings is proposed, and the research ideas and directions are proposed. Conclusion: The development of smart bearings is conducive to the timely detection of bearing conditions in the operation process. Except for detection, smart bearings have the functions of judgment, feedback, processing, etc. There are many factors that interfere with the accuracy of the smart bearing judgment and information transfer, so it is necessary to improve the structure and working principle of smart bearings through a variety of measures. In the future, more smart bearings will be invented.

Temperature field analysis and equal-amplitude temperature rise constraint current control method under open-circuit fault-tolerant operation for five-phase permanent magnet motor

The phase redundancy and freedom of the five-phase permanent magnet motor (FPPMM) enhance the reliability of the motor system. However, the temperature rise and torque reduction issues of the motor during open-circuit fault operation need to be further studied. In order to study the temperature distribution characteristics and the output capability of the motor under fault operation subject to temperature rise constraints, a 3-D global temperature field model containing a complex heat dissipation structure has been established. And the equal-amplitude temperature rise constraint current control method is proposed under open-circuit fault-tolerant operation to improve its output capability. The temperature distribution of the motor during steady state operation under rated operating conditions is analyzed with emphasis. Based on this, the temperature field changes of the motor during single-phase and two-phase open-circuit fault-tolerant operation are investigated. Then, the proposed equal-amplitude temperature rise constrained current method is introduced in detail to ensure the safe operation of the motor. An experimental platform is built, which verifies the accuracy of the temperature field calculation, as well as the correctness of equal-amplitude temperature rise constrained current method.

Identification method for inter-turn faults in transformers based on digital twin concept

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization—ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers.

Open Fault Detection in Variable Phase-Pole Machines Based on Harmonic Plane Decomposition

Open Fault Detection in Variable Phase-Pole Machines Based on Harmonic Plane Decomposition

INFLUENCE OF PLEISTOCENE GLACIATION ON PETROLEUM SYSTEMS AND GAS HYDRATE STABILITY IN THE OLGA BASIN REGION, BARENTS SEA

This study presents the results of a 2D numerical basin and petroleum systems model of the Olga Basin in the NW Barents Sea offshore northern Norway, a frontier exploration area in which there are abundant seafloor oil and gas seepages. The effects of Pleistocene ice sheet advances on rock properties and subsurface fluid migration in this area, and on seafloor hydrocarbon seepage, are not well understood. The 2D numerical model takes account of recurrent ice advances and retreats, together with related erosional and temperature effects, and investigates the influence of these parameters on fluid migration. Model results show that Pleistocene glaciations reduced the temperature in the sedimentary succession in the Olga Basin by up to 20 °C, for example in the uppermost Cretaceous and Jurassic sediments which underlie the seafloor down to a depth of 0.5 to 1 km. The decrease in temperature was in general predominantly related to the intensity of glacial erosion, which was set in this study to a depth of 600 m based on previous studies. Hydrocarbon fluids expelled from potential thermogenic source rocks of Carboniferous to Triassic ages on the SW margin of the Olga Basin migrated to the seafloor through permeable carrier beds. However, fluid migration to the surface in the NE of the study area took place along fault conduits. In a closed fault model scenario, only 0.3 Mt of hydrocarbons are modelled to have migrated along the 0.5 km wide model section; in a second scenario with partially open faults, about 22 Mt of hydrocarbons, representing about 11% of the total hydrocarbons generated by potential thermogenic source rocks in the study area, were lost to the surface during the Pleistocene. The potential for microbial methane generation in the Olga Basin was limited both during the Pleistocene and at the present day due to the significant reduction in temperature during glacial episodes, and due to the intense glacial‐related erosion of the Mesozoic to Cenozoic stratigraphy. During glacial stages, the gas hydrate stability zone beneath the ice sheet is modelled to have extended to a depth of up to 900 m for a pure methane composition, and to a depth of up to 1100 m for a possible thermogenic‐sourced mixed gas composition of 90% methane, 7% propane and 3% ethane. Gas hydrates with this mixed composition are modelled to have been stable in the Olga Basin during the last three glacial advances and into the present. These modelling results provide an insight into the key factors controlling the migration and surface leakage of hydrocarbon fluids in the Olga Basin region, and into the effects of glaciations on rock properties in a glaciated basin.

SPLSTM: A Prediction Model for Operational Faults in Rotating Machinery

SPLSTM: A Prediction Model for Operational Faults in Rotating Machinery

Anomaly Identification for Photovoltaic Power Stations Using a Dual Classification System and Gramian Angular Field Visualization

With the increasing scale of photovoltaic (PV) power stations, timely anomaly detection through analyzing the PV output power curve is crucial. However, overlooking the impact of external factors on the expected power output would lead to inaccurate identification of PV station anomalies. This study focuses on the discrepancy between measured and expected PV power generation values, using a dual classification system. The system leverages two-dimensional Gramian angular field (GAF) data and curve features extracted from one-dimensional time series, along with attention weights from a CNN network. This approach effectively classifies anomalies, including normal operation, aging pollution, and arc faults, achieving an overall classification accuracy of 95.83%.

Advanced Fault Diagnosis in Power Electronics: Switch Open Faults in DC-Link Shunt Sensor-Less Drives

Advanced Fault Diagnosis in Power Electronics: Switch Open Faults in DC-Link Shunt Sensor-Less Drives