Low-level Fault Research Articles

3D seismic Fault Detection via Contrastive-Reconstruction Representation Learning

Fault detection is a critical step in structural modeling and characterizing reservoirs. 3D seismic fault labeling is almost impossible to obtain, while networks trained on synthetic fault data have limited generalization to real data. We use self-supervised representation learning to enable networks to learn seismic field data features during pre-training, improving generalization. However, widely popular ViT/SwinViT-based methods cannot capture low-level fault features well. CNN-based have limited capability in transferring to downstream tasks. Tackle this, we designed the Tiny Self-Attention and extensively embedded it into the HRNet. It merges the advantages of convolutional and self-attention, allowing for in-depth learning of seismic representation information during the representation learning phase, thus achieving better inter-class separation in downstream tasks. We crafted two proxy tasks. The contrastive task aims to minimize the projected feature distance of overlapping areas from two distinct views. To tackle the issue of memory overflow and training suspension caused by the high spatial and temporal complexity of 3D high-resolution feature matching, we introduced sparse distance matching and an adaptive feature aggregation. In reconstruction task, we designed a masking strategy tailored to the unique attributes of 3D seismic data. It process named “Fault Detection via Contrast-Reconstruction Representation Learning” (FaultCRL). Experimentally, we compared the latest fault detection methods, and representation learning techniques like MAE and SwinUNETR, showcasing the notable advantage of FaultCRL in fault detection tasks. The appendix provides all qualitative results from mainstream geophysical journals regarding The Netherlands-F3 data in recent years, indicating that our approach has reached the current state-of-the-art level.

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

Operating in the “Danger Zone”: Contactor Dropout Versus Fuse Clearing Time

The most common and economical arrangement for medium-voltage motor controllers is to self-supply from the primary motor circuit. In this arrangement, a short circuit collapses the line voltage, causing the contactor to “drop out.” For low-level faults that nevertheless exceed the interrupting rating of the contactor, it is common for the dropout time to be faster than the clearing time for the fuse. Although this fault current is extremely unlikely to occur, no provisions exist in current standards to permit the application of contactors in this manner. To prevent the contactor from attempting to clear a fault current above its rating, less than optimal fuses must be selected, and complex and expensive supplemental power supply circuits are added to ensure that the holding coil remains energized until the fuse clears the fault. The tradeoffs of the short circuit clearing time versus the rated current to achieve a takeover point below the interrupting rating of a contactor are illustrated. In response to a customer request, an alternative test procedure to demonstrate capability where contactor dropout precedes fuse clearing was performed; actual results of such tests are included.

Low-order fault identification method of Putaohua oil layer of X block in Songliao Basin

With the exploration and development of oil fields to the later stage, the distribution of remaining oil is highly scattered, and the oil control area becomes smaller. It is necessary to carry out fine identification and description of small geological interfaces, so as to improve the accuracy of reservoir description. Therefore, low-level fault identification has become an extremely important link. However, due to the influence of seismic data resolution, it is often difficult to directly interpret small faults on conventional seismic profiles. In conventional seismic data, fault interpretation is often based on seismic attribute analysis. At present, there are many kinds of seismic attributes used for fault interpretation, such as coherence class, curvature body attribute, ant attribute body and so on. By comprehensively utilizing the time slices and seismic profiles of these seismic attributes, small faults can be identified more effectively. In the process of studying the three-dimensional work area of Putaohua oil layer of X block in Songliao Basin, the above methods are used in combination with various information such as seismic profiles to identify small faults that are difficult to identify on conventional seismic data volume and coherence volume, and good application results have been achieved.

Modular Supervisory Control for the Coordination of a Manufacturing Cell with Observable Faults

In the present paper, a manufacturing cell in the presence of faults, coming from the devices of the process, is considered. The modular modeling of the subsystems of the cell is accomplished using of appropriate finite deterministic automata. The desired functionality of the cell as well as appropriate safety specifications are formulated as eleven desired languages. The desired languages are expressed as regular expressions in analytic forms. The languages are realized in the form of appropriate general type supervisor forms. Using these forms, a modular supervisory design scheme is accomplished providing satisfactory performance in the presence of faults as well guaranteeing the safety requirements. The aim of the present supervisor control scheme is to achieve tolerance of basic characteristics of the process coordination to upper-level faults, despite the presence of low-level faults in the devices of the process. The complexity of the supervisor scheme is computed.

Corrosion and friction resistance of TiVCrZrWNx high entropy ceramics coatings prepared by magnetron sputtering

High entropy alloy coatings have attracted much attention because of their high hardness, low-level fault energy, and chemical stability. Nevertheless, this type of coating would inevitably suffer from wear, corrosion, aging, and so on. Hence, a novel coating with corrosion and friction resistance would be constructed for broadening its application scenarios. In this work, TiVCrZrWNx high entropy ceramics coatings were prepared by reactive magnetron sputtering. The microstructure, mechanical properties, friction, and corrosion resistance of the coatings deposited at different nitrogen flow rates have been studied. The microstructure of TiVCrZrWNx coatings is strongly dependent on the nitrogen flow rate and forms a stable FCC structure when the nitrogen flow rate reaches 24 sccm. The pure TiVCrZrW coating is 15.65 GPa, with the increase of nitrogen flow rate (24 sccm), the coating hardness reaches 21.27 GPa. The corrosion resistance of the coatings also increases continuously. According to the results of the impedance spectrum and polarization curve, the charge transfer resistance value of the coating gradually increases with the content of nitrogen, the current density rapidly decreases to a minimum as the potential increases. In terms of tribological behavior, the formation of V2O5 during the sliding in seawater could significantly reduce the coefficient of friction from 0.603 to 0.383. Therefore, TiVCrZrWNx HECs coatings simultaneously possess high hardness, toughness, and excellent resistance to friction and corrosion, which is expected to provide a new and reliable method for the research field of coatings in the maritime field.

AI Enabled Data Quality Monitoring with Hydra

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.

Optimal Protection Coordination Scheme for Radial Distribution Network Considering ON/OFF-Grid

Technology advancement for renewable energy resources and its integration to the distribution network (DN) has garnered substantial interest in the last few decades. Integrating such resources has proven to reduce power losses and improve the reliability of DN. However, the growing number of these resources in DN has imposed additional operational and control issues in voltage regulation, system stability, and protection coordination. Incorporation of various types of distributed generators (DG) into DN causes significant changes in the system. These including new fault current sources, new fault levels, a blinding effect in the protection scheme, reduction in the reach of relays, and decrement in the detection of low-level fault currents for existing relays. Such changes will jeopardize the effectiveness of the entire protection scheme in the DN. This research aims to propose a robust protection scheme in which the relay coordination settings are optimized based on the network layout. The potential impacts of DGs on the DN are mitigated by utilizing a user-defined overcurrent-based relay characteristic to obtain the minimum operating time while satisfying protection coordination constraints. A hybrid optimization algorithm based on Metaheuristic and Linear Programming that has the capability to attain the optimal solution and reduces computational time is proposed in this work. The performance of the proposed technique is tested on radial DN integrated with microgrid (MG). The results obtained show the proposed technique has successfully reduced the relay operating time while meeting the protection coordination requirements for dynamic operating modes of a network.

Synthetic Hybrid-Integral-Threshold Logic-Based Position Fault Diagnosis Scheme for SRM Drives

Reliable rotor position measurement is quite necessary for ensuring accurate commutation control in many kinds of motor drives, such as switched reluctance motor (SRM) and brushless dc motor (BLDC). Traditionally, the Hall sensors and photoelectric switch sensors are widely used in most of these low-cost motor drive systems. In these systems, the rotor position needs to be decoded from the discrete voltage pulses measured by the sensors. Thus, to develop an online position pulse signal fault diagnosis scheme with fast fault-monitoring capability is quite vital for avoiding the abnormal driving control and improving the overall system reliability. In this article, a position pulse signal fault diagnosis scheme based on synthetic hybrid-integral-threshold logic is proposed for SRM drives. In this method, the complementary position pulse signals of each sensor are used as the independent reference signals. By applying special integral calculations on each reference signals and capturing their rising and falling edges, the position pulse fault cases like high-level and low-level faults can be diagnosed immediately with the synthetic thresholds’ comparison logic, respectively. To verify the validity of the proposed method, a 1-kW 12/8 structure SRM prototype with three independent Hall sensors installed is used as a test machine. The experimental results show that this method can diagnose the position fault of each position sensor independently and ensure very fast position fault detection even under dynamic operations with speed transients. Furthermore, the method can be implemented without additional hardware and complex computation, which is quite suitable for constructing a reliable position measurement module to assist the rotor position sensors.

Behavior of Overlying Strata at the Working Face of in a Multi-fault Zone

Fault is one of the common geological structures in coal mining, the concentrated development of several faults has changed the movement form and structure shape of overlying strata, which caused the instability of overburden strata easily and induced dynamic disasters. Taking the 10,416 working face of Yangliu coal mine as the research object, the plane numerical model of overburden structure with two faults (a reverse fault and a normal fault) was established by using UDEC numerical simulation software. The dip angle of normal fault is 50°, the dip angle of normal fault is 70°. The distances between the two faults are 30 m, 60 m, 90 m. The evolution characteristics of overburden structure in working face are studied respectively from hanging wall to footwall and from footwall to hanging wall, and the dynamic hazard law induced by the activation of different faults is analyzed. The results prove that: when the working face is mined from footwall to hanging wall, the overlying strata on the working surface form “trapezoid” and “triangle” structures. When the working face is mined from hanging wall to footwall, the overlying strata form the structure of “inverted trapezoid” and “inverted triangle”. The low-level fault activation caused by overburden movement is easy to induce dynamic response. Finally, the research results are verified by using the microseismic activity of 10,416 working face in Yangliu coal mine.

HIF Detection and Classification in Distribution Systems using Wavelet Transforms

High Impedance Faults (HIF) are generally occurs on distribution line. HIFs are, by and large, hard to recognize through traditional assurance, for example, distance or over current relays. This is primarily because of hand-off inhumanity toward the low level fault currents as well as constraints on other hand-off settings forced by HIFs. Regular assurance hand-off framework won't have the option to distinguish the HIFs and excursion the security transfer. HIFs on electrical transmission and dissemination systems include arcing as well as nonlinear attributes of flaw impedance which cause repeating example and contortion. Subsequently, the goal of most discovery plans is to recognize extraordinary highlights in examples of the voltages and current related with HIFs. Most traditional flaw discovery strategies for HIF for the most part include preparing data dependent on the component extraction of post HIF current and voltage. Wavelet transform is most appropriate for HIF location and for fault classification. This paper depicts another shortcoming location procedure which includes catching the present sign created in a framework under HIFs. The identification procedure depends on ascertaining the total entirety of the wavelet transform detail coefficients for one period. Wavelet transform is utilized for the disintegration of sign and highlight extraction.

Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks

The growth of intermittent distributed energy sources (DERs) in distribution grids is raising many new operational challenges for utilities. One major problem is the back feed power flows from DERs that complicate state estimation for practical problems, such as detection of lower level fault currents, that cause the poor accuracy of fault current identification for power system protection. Existing artificial intelligence (AI)-based methods, such as support vector machine (SVM), are unable to detect lower level faults especially from inverter-based DERs that offer limited fault currents. To solve this problem, a current tracing method (CTM) has been proposed to model the single distribution feeder as several independent parallel connected virtual lines that traces the detailed contribution of different current sources to the power line current. Moreover, for the first time, the enhanced current information is used as the expanded feature space of SVM to significantly improve fault current detection on the power line. The proposed method is shown to be sensitive to very low level fault currents which is validated through simulations.

SWIFT: Switch-Level Fault Simulation on GPUs

Current nanometer CMOS circuits show an increasing sensitivity to deviations in first-order parameters and suffer from process variations during manufacturing. To properly assess and support test validation of digital designs, low-level fault simulation approaches are utilized to accurately capture the behavior of CMOS cells under parametric faults and process variations as early as possible throughout the design phase. However, low-level simulation approaches exhibit a high computational complexity, especially when variation has to be taken into account. In this paper, a high-throughput parallel fault simulation at switch level is presented. First-order electrical parameters are utilized to capture CMOS-specific functional and timing behavior of complex cells allowing to model faults with transistor granularity and without the need of logic abstraction. Furthermore, variation modeling in cells and transistor devices enables broad and efficient variation analyses of faults over many circuit instances for the first time. The simulation approach utilizes massive parallelization on graphics processing units by exploiting parallelism from cells, stimuli, faults, and circuit instances. Despite the lower abstraction levels of the approach, it processes designs with millions of gates and outperforms conventional fault simulation at logic level in terms of speed and accuracy.

Multi-level timing and fault simulation on GPUs

In CMOS technology first-order parametric faults during manufacturing can exhibit severe changes in the timing as well as in the functional behavior of cells. Since these faults are hard to detect by conventional tests, the accurate simulation of these low-level faults plays an important role for test validation. However, pure low-level fault simulation approaches impose a high computational complexity that can quickly become inapplicable to larger simulation problems due to limitations in scalability.In this paper, the first parallel multi-level fault simulation approach on graphics processing units (GPUs) is presented. The approach utilizes both logic level and switch level descriptions concurrently in a mixed-abstraction timing simulation. The abstraction is lowered in user-defined so-called regions of interest that locally increase the modeling accuracy enabling low-level first-order parametric fault injection. Resulting signal waveforms are transformed between the different abstractions transparently. This way a fast, versatile and efficient multi-level fault simulation approach on GPUs is created that scales for designs with millions of cells while achieving high simulation throughput with runtime savings of up to 84% compared to full switch level simulations.

渤海中深层复杂断块油藏低序级断层的识别与应用—以JX油田为例

渤海中深层复杂断块油藏低序级断层的识别与应用—以JX油田为例

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

Bus Differential Protection in Industrial Systems With Generators Connected Directly to the Main Distribution Bus

In industrial facilities with on-site generation, there are two major approaches to connect generators to the main distribution busbar. One is to connect generators through step-up delta-wye transformers with low-resistance grounded wye on the busbar side. The second common method is to connect generators directly to the main distribution busbar and use low-resistance grounding or hybrid grounding in the generator neutral. A grounding resistor in the step-up transformer’s wye side neutral or in the generator neutral will limit the ground fault in the main distribution system. When busbar differential protection is used on the main busbar, sensitivity for bus ground faults has to be evaluated to ensure detection of limited low-level ground faults. While step-up transformer grounding resistors can be selected to allow ground faults of higher magnitudes detectable by bus differential protection, the choice of grounding resistor is more complex when generators are connected directly to the main busbar. In such systems, a grounding resistor in the generator neutral limits the generator fault to protect the alternator core from excessive damage. At the same time, busbar differential protection needs to detect a limited low-magnitude ground fault on the bus. This paper discusses considerations to select the main distribution busbar differential protection type, CTs, and relaying scheme to enable detection of busbar ground faults limited by the generator low-resistance grounding resistor.

인휠 독립 구동 전기 자동차의 구동 모터 통합 고장 진단 알고리즘

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through Carsim® and MATLAB/ Simulink® cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/ Simulink® interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

Performance of a Polycrystalline SmBaCuO Superconducting Fault Current Limiter

This study has assessed the performance of a resistive superconducting fault current limiter based on polycrystalline SmBa2Cu3O7−d (Sm-123) subjected to 750 short-circuit events with acting time 80 ms. Preliminary results indicate that polycrystalline Sm-123 cuprates can be used to build superconducting fault current limiters. The results of this study have demonstrated that after 616 short-circuit repetitions, the superconducting element was able to limit the fault current without degradation on low-level fault current density J < 3.7 kA/cm2.

Low-level logic fault testing ASIC simulation environment

A low-level logic fault test simulation environment targeted towards application-specific integrated circuits (ASICs) in particular is proposed in this paper. The simulation environment emulates a typical built-in self-testing (BIST) environment with test pattern generator (TPG) that sends its outputs to a circuit (core) under test (CUT) and the output streams from the CUT are fed into an output response analyzer (ORA). The developed simulator is very suitable for testing embedded digital intellectual property (IP) cores-based systems. The paper describes the total test architecture environment, including the application of the logic fault simulator. Results on simulation on some specific International Symposium on Circuits and Systems (ISCAS) 85 combinational and ISCAS 89 sequential benchmark circuits are provided as well for appraisal.