Fault Analysis Research Articles

Electrical Power Generator Faults Analysis Using Fault Tree and Bayesian Network

Abstract This paper presents a model to predict Electrical Power Generator (EPG) faults. The fault tree (FT) model is developed and used to help maintenance engineers in fault analysis procedure of this rotating machine. By identifying the main, intermediate and basic events it’s possible to construct the FT with logical reasoning. The top dreaded event is defined. By using a Bayesian network (BN) as a complementary tool, fault prediction of the EPG becomes possible and easy. By using the developed BN, the probability of occurrence of the top event (EPG failure) is calculated. Also, by this approach, we can process complex information that causes system faults in an easy and simple way. The essential elements to do this analysis are the reliable and good exploitation of the information previously stored in the system. The use of the BN in combination with the FT gives the possibility of qualitative and quantitative analysis, diagnosis, and prediction of faults from the same Bayesian model. The flexibility of the proposed BN model in this paper allows better and precise decision making. Also, priorities regarding maintenance job are defined and resources are a priori prepared.

A simplified predictive framework for cost evaluation to fault assessment using machine learning

<span lang="EN-US">Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.</span>

Evaluation of Medication Errors by Prescription Audit at a Tertiary Care Teaching Hospital

Objectives The prescription errors and prescribing fault analysis was assessed, the rationality of the prescriptions was checked, and the medication error was categorized according to the NCC MERP Index. Materials and Methods A cross-sectional, observational study was designed as per STROBE guidelines and conducted for 2 months in the pharmacy stores after approval of the Institutional Review Board. Patients’ written informed consent was taken before getting their prescriptions, and each of the prescriptions procured in this way was photographed for record. The completeness of 320 prescriptions of outpatients of all age groups regarding the details about the doctor and the patient and clinical diagnosis/indication was analyzed. The rationality of prescription was based on WHO core drug use indicators. Descriptive analysis was done by using Microsoft Excel. Results A total of 320 prescriptions were analyzed from eight departments. Information about patients and prescribers was mentioned in 100% of prescriptions. The diagnosis (40%), an indication was written in 195 prescriptions. Instructions for dispensing drugs (89%), instructions to patients (90%), duration of treatment (100%), follow-up visits (19%), and non-pharmacological instructions (13%) were mentioned. In total, 82% of prescriptions were legible. In a total of 1004 drugs, 92% of drugs were prescribed with a generic name, 100% from the essential drug list. The route and frequency of drug administration were mentioned for all drugs. According to NCCMERP, the category of medication errors falls under category B. Conclusion To reduce medication errors, we can implement an electronic system, involve clinical pharmacologists, utilize prescription charts, and organize nationwide workshops on rational prescription writing. We should encourage regular prescription audits and reporting to improve the healthcare system in the country.

Parameter estimation and control of a fuel cell air supply system based on an improved extended state observer

ABSTRACT Tracking control of the oxygen excess ratio (OER) and keeping the cathode air pressure relatively stable are essential for the operation of polymer electrolyte membrane fuel cell (PEMFC). In automotive fuel cell air supply systems, the cathode air pressure and OER are usually unmeasurable parameters, which makes the air supply system control and fault analysis very difficult. To solve this problem, this paper proposes an extended state observer based on the hyperbolic tangent function (HTF-ESO) to estimate the cathode air pressure of the fuel cell gas supply system, and indirectly obtains the OER according to the estimated cathode pressure, and designs the sliding mode control (SMC) and a first-order active disturbance rejection control (ADRC) for the tracking control of the OER. The simulation results show that the improved HTF-ESO effectively mitigates the initial differential peak phenomenon and reduces the initial estimation error of the cathode air pressure, and the RMSE of the cathode pressure and supply manifold pressure observation errors are reduced by 68% and 20%, respectively, compared with the present unimproved ESO. Compared with the PI, the RSME of the control error after convergence of the designed SMC and ADRC is reduced by 4.3%, and the OER can be restored to the reference value within 1 second, with good dynamic response.

Fault Analysis and Treatment of High-Pressure Backwash Water Pump

Fault Analysis and Treatment of High-Pressure Backwash Water Pump

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

A Current Differential Protection Scheme for Distribution Networks with Inverter-Interfaced Distributed Generators Considering Delay Behaviors of Sequence Component Extractors

The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs.

Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

The goal of this study is to assess the application of Multilayer Perceptron Artificial Neural Networks in fault classification within photovoltaic panels, focusing on key characteristics such as the number of neurons and layers, activation functions, training techniques, and the resulting accuracy. The study employs a comparative analysis approach, examining various characteristics and hyperparameters applied to Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The research methodology involves reviewing publications from the past decade to gather data on these characteristics and their impact on fault analysis in photovoltaic generation systems. This study contributes to the originality of the field by providing a comprehensive comparison of various parameters and techniques used in Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The findings offer valuable insights for researchers and practitioners in the renewable energy sector, aiding in the development of more efficient and reliable fault diagnosis systems for photovoltaic generation.

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Power equipment fault diagnostics is a critical aspect of ensuring the stability of the power grid system. However, it presents substantial challenges in obtaining labeled data that is spatial-temporal, multi-scale, and multi-domain, low noise for effective fault analysis and diagnosis. To address this issue, we propose a novel approach called hierarchical dynamic aggregation graph (HDAG) modeling for self-supervised fault diagnosis of power transformers using vibration data. Firstly, HDAG focuses on modeling the spatial and temporal correlations within the fault vectors, before converting them into time–frequency images for visualization. Secondly, our proposed fault diagnosis approach, (comprising the ST-sparse swin-transformer and multi-domain transformer fusion module), is integrated into the methodology. The ST-sparse swin transformer incorporates soft threshold modules, enabling the retention of relevant information while discarding irrelevant information. The multi-domain transformer fusion is designed to utilize the intra-domain and inter-domain signal characteristics to achieve a comprehensive feature representation. Finally, we present case studies based on experimental data that demonstrate the feasibility and effectiveness of our approach. Comparative evaluations against eight state-of-the-art techniques validate the improved information representation and diagnostic capabilities of our recommended strategy.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Deciphering the formation period and geological implications of shale tectonic fractures: a mini review and forward-looking perspectives

In complex geological areas, the chronology of tectonic fracture formation is pivotal for the conservation and enhancement of shale gas reservoirs. These fractures, evolving over different geologic epochs, critically influence the modifications in hydraulic fracturing. The review sheds light on an integrated methodology that bridges conventional geological evaluations with experimental diagnostics to decipher the intricate evolution of such fractures in complex geological areas. Shale tectonic fractures, predominantly shear-induced, are delineated into four distinct levels (I, II, III, IV) based on observational scales. Understanding the geometric interplay across these scales provides insight into fracture distribution. Recognizing the constraints of isolated approaches, this study amalgamates macroscopic geological assessments, such as structural evolution and fault analysis, with microscopic techniques, including fluid inclusion studies, isotopic testing, rock AE experiments (U-Th)/He thermochronology, and AFT analysis, etc. This combined approach aids in accurately determining the tectonic fracture’s genesis and its geological time. Future research endeavors should refine this framework, with an emphasis on enhanced geochemical profiling of fracture fillings.

Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.

Natural language processing (NLP) and association rules (AR)-based knowledge extraction for intelligent fault analysis: a case study in semiconductor industry

Natural language processing (NLP) and association rules (AR)-based knowledge extraction for intelligent fault analysis: a case study in semiconductor industry

Uplift Dynamics of the Obducted Northeastern Continental Margin of the Arabian Peninsula, Sultanate of Oman

AbstractEustatic sea level changes and vertical tectonic movements are producing uplifted paleoshorelines. Along subduction zones, uplifted terraces are used to study fault activities and, overall, allow to interpret the tectonic history of plate convergence. Northeastern Oman is experiencing plate convergence following the late Cretaceous obduction of the Semail Ophiolite. Post‐obduction shallow‐marine carbonates have been uplifted to different elevations from 133 to >2,000 m. The present study employs a multidisciplinary approach to elucidate the variability in relief and to introduce a geodynamic model that extends beyond the temporal constraints imposed by the late Quaternary age of the sediments found on the uplifted terraces. Stratigraphic and fault analyses produced a post‐obductional geodynamic model to advance the existing regional models in the framework of the subduction of the Arabian Plate in the Makran Zone. In addition, we rely on imaging geodesy, geomorphology and dating to explain the late Quaternary uplift scenario. Overall, analyses of geomorphology, stratigraphy, and fault patterns reveal spatially heterogeneous post‐late Cretaceous uplift in the region. Compartmentalization by major faults created individual blocks and relief variability. Within the timeframe of marine terrace formation (late Quaternary), we also observed spatially varied displacements. Ground displacements by Interferometric Synthetic Aperture Radar document an ongoing spatial heterogenous uplift at approximately 1.3 mm/a. Finally, temporal variability was evident during the late Quaternary by unusually high late Pleistocene (<40 ka) uplift rates averaging ≥2 mm/a in younger terraces, while for older terraces (>40 ka) the uplift rate is distinctly lower (<1 mm/a).

Incorporating Modern Fault Ride-Through Standards into the Short-Circuit Calculation of Distribution Networks.

Modern fault ride-through (FRT) standards in many countries require distributed generators to remain connected for a specified period during the fault by providing reactive current, to support voltage and prevent a massive renewable outage. As a result, short-circuit current is not constant, but it varies depending on the current and disconnection order of distributed generators (DGs). This time-varying short-circuit current complicates the estimation of the time it will take for an overcurrent relay or fuse to trip. The existing short-circuit calculation algorithms usually assume that the fault current is constant throughout the whole period of fault. This assumption may result in incorrect conclusions regarding the tripping time of protective devices in networks with high renewable penetration. This paper incorporates modern FRT standards into the fault analysis by considering the influence of fault current variations on the protective devices (relays, fuses), significantly increasing the accuracy of the estimated tripping time. Simulations carried out in a 13-bus and the IEEE 8500-node network indicate that the traditional short-circuit calculation approaches may miscalculate the tripping time of protective devices, with deviations up to 80 s, when applied to networks complying with modern FRT standards.

Multiscale structural analyses of Mesozoic shallow-water carbonates, Viggiano Mt., southern Italy

The high-angle faults bounding the High Agri Valley Basin eastwards are well-exposed at the Viggiano Mt., southern Italy. There, these faults crosscut Mesozoic shallow-water carbonates and permit the multiscale analyses of fault and fracture geometry and distribution. Aiming at assessing the control exerted by carbonate lithofacies and scales of observation, we combine field and digital structural analyses to compute the values of 1D and 2D fracture density and intensity, and the dimensional properties of single fault and fracture sets. As a result, from outcrop to reservoir scales we document a scale-variant geometry of faults and fractures, and the great variation of both fracture density and intensity among the studied carbonate lithofacies. Furthermore, we compute a P21 dimensional scaling factor of ca. 20 for over 3 order magnitude, whereas inconsistent results are achieved for the P20 values. Not considering the control exerted by the single carbonate lithofacies, at a reservoir scale we document that the small-scale faults greatly impact the computed P21 values.

Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm

Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.

Inverse transient analysis based calibration of surrogate pipeline model for fault simulation of axial piston pumps

Axial piston pumps are the key component in power hydraulic systems. Flow and pressure ripple signals, which can reflect the flow dynamics of the axial piston pump, are important information sources for condition monitoring. Therefore, the characteristics of flow and pressure ripple signals should be investigated for further condition monitoring and fault analysis. However, measuring high-frequency flow ripple directly can be difficult, and the pressure ripple is not only affected by pump dynamics but also affected by the transient pressure wave propagation. However, in practical applications, the hydraulic pipeline system is complex due to the space constraint of the equipment, and the influence of the pipeline system cannot be ignored when modeling pressure ripple. This paper proposes a novel two-stage method for fault simulation of the pumps that considers the influence of the pipeline system on the pressure ripple. Firstly, the surrogate pipeline model with free parameters is calibrated using ITA to capture the characteristics of the complex practical pipeline system. Secondly, the calibrated model is applied to the fault simulation of axial piston pumps. A detailed 3D CFD model of the pump is built up, and the flow ripple at the pump outlet from the numerical model is used as an approximation of the real flow ripple of the pump, which serves as one of the boundary conditions of the surrogate pipeline model. After calibrating the model, the CFD model of the pump is used to simulate internal failures in the pump. Two failure cases are studied: a faulty slipper and a faulty cylinder. The simulated signal of the proposed model matches the experimental signal well under the healthy and faulty conditions, which confirms the effectiveness of the proposed method.

A New Short-Circuit Current Calculation and Fault Analysis Method Suitable for Doubly Fed Wind Farm Groups.

The transient characteristics of wind farms in groups are quite different; in addition, there is a strong coupling between the wind farms and the grid, and these factors make the fault analysis of the grid with wind farm groups complicated. In order to solve this problem, a mathematical model of the converter is established based on the input-output external characteristics of the converter, and a transient model of a doubly fed wind turbine (DFIG) is presented considering the influence of the low-voltage ride-through control (LVRT) of the converter, and the effect mechanism of the LVRT strategy on the short-circuit current is analyzed. Finally, a short-circuit current calculation model of a doubly fed wind turbine with low-voltage crossing control is established. The interaction mechanism between wind farms during the fault is analyzed, and a short-circuit current calculation method of doubly fed wind farm groups is proposed. RTDS is used to verify the accuracy of the proposed short-circuit current calculation method for doubly fed field groups. On this basis, a method of power grid fault analysis after doubly fed field group access is discussed and analyzed.

Experimental investigation for fault diagnosis of a single stage worm gearbox using response surface methodology

Gearbox transmission system is a fundamental factor in the industrial applications. If gearbox stop working due to different faults, it may break normal machine operation and cause a production loss. This research paper focused on fault analysis of combination of worm wheel and bearing by using root mean square (RMS) and response surface method (RSM). Faults considered on outer race and inner race of worm wheel bearing, on worm wheel tooth. These faults and load were considered as an important independent factor to understand their effects on RMS response of worm gearbox. Worm gearbox experimental setup is for laboratory experimentation and RSM for analysis. Twenty-seven experimental trial conducted for three level of parameters based on design of experiment (DOE). Vibration based response measured in frequency domain and root mean square parameter extracted for the fault analysis. Box behnken design RSM is implemented to investigate independent parameters effect on output parameter i.e. RMS. The result shows that effect of faulty inner race bearing is more on worm wheel as compared to faulty outer race bearing and fault parameter influence RMS than load parameter.