Analysis Of Fault Data Research Articles

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

Research on a Fault Diagnosis Method for the Braking Control System of an Electric Multiple Unit Based on Deep Learning Integration

A fault diagnosis method based on deep learning integration is proposed focusing on fault text data to effectively improve the efficiency of fault repair and the accuracy of fault localization in the braking control system of an electric multiple unit (EMU). First, the Borderline-SMOTE algorithm is employed to synthesize minority class samples at the boundary, addressing the data imbalance and optimizing the distribution of data within the fault text. Then, a multi-dimensional word representation is generated using the multi-layer bidirectional transformer architecture from the pre-training model, BERT. Next, BiLSTM captures bidirectional context semantics and, in combination with the attention mechanism, highlights key fault information. Finally, the LightGBM classifier is employed to reduce model complexity, enhance analysis efficiency, and increase the practicality of the method in engineering applications. An experimental analysis of fault data from the braking control system of the EMU indicates that the deep learning integration method can further improve diagnostic performance.

Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery

Although the maritime industry has the potential to lead smart maintenance methodologies, current maintenance routines within the sector focus on either reactive or preventive maintenance approaches. These approaches are increasingly conservative and often prompted by an increase of large costs or unnecessary maintenance actions. Attempts are being made, however, to optimise these costs and actions through the application of novel practices, such as the employment of prognostic-based maintenance, which is an approach that aims to minimise risk while maximising the lifespan of a system. In this respect, Mar-RUL is introduced to support Operations & Maintenance (O&M) decision making and address some of the main challenges that the sector is currently experiencing, including the lack of fault data analysis and the formalisation of deep learning technologies for the implementation of the Remaining Useful Life (RUL) prediction. In response, a degradation data simulation module is developed in tandem with an ensemble model comprised of three distinct deep learning architectures: Markov-Convolutional Neural Network (CNN), 1D-CNN, and Long Short-Term Memory (LSTM) neural network. To evaluate the performance of Mar-RUL, a case study on the turbocharger of a diesel generator of a tanker ship is presented. The case study results demonstrated that the application of time series imaging and ensemble methods can provide promising outcomes for the enhancement of RUL prediction.

Application of Digital Technology in Military Equipment Management

The traditional military equipment management mode is difficult to meet the needs of today's rapid development, and digital application has become a necessary trend to improve the efficiency and quality of equipment management. At present, the mainstream application direction of digital technology in military equipment management mainly includes equipment fault monitoring, equipment maintenance, and data analysis and optimization. However, although the application of digital technology brings many benefits, the development of digital technology faces a series of difficulties and key problems. For the military, accelerating digital transformation, overcoming the practical challenges of digital technology landing in the military, and improving the application level of digital technology will become an important development direction in military equipment management in the future.

Multi-Source SAR-Based Surface Deformation Monitoring and Groundwater Relationship Analysis in the Yellow River Delta, China

Land motions are significantly widespread in the Yellow River delta (YRD). There is, however, a lack of understanding of the delta-wide comprehensive deformation mode and its dynamic mechanism, especially triggered by groundwater extraction. This paper adopts an integrated analysis of multidisciplinary data of image geodesy, geophysics, geology and hydrogeology to provide insights into Earth surface displacement patterns and dynamics in the YRD. Delta-scale land motions were measured for the first time using L-band ALOS images processed using multi-temporal InSAR, illustrating multiple obvious surface sinking regions and a maximum annual subsidence velocity of up to 130 mm. Then, the InSAR-constrained distributed point source model with optimal kernel parameters, a smoothness factor of 10 and a model grid size of 300 m was established and confirmed to be rational, reliable and accurate for modeling analysis over the YRD. Remarkable horizontal surface displacements, moving towards and converging on a sinking center, were recovered by means of modeling and measured using InSAR, with a maximum rate of up to 60 mm per year, which can trigger significant disasters, such as ground fissures and building damage. In addition, the annual total water storage variation at the delta scale, the most meaningful outcome, can be calculated and reaches a total of approximately 12,010 × 103 m3 in Guangrao city, efficiently filling the gap of GRACE and in situ investigations for delta-wide aquifer monitoring. Finally, a comparative analysis of time series InSAR measurements, modeling outcomes, and fault and groundwater data was conducted, and the strong agreement demonstrates that faults control aquifer distribution and hence the spatial distribution of groundwater-withdrawal-related regional land subsidence. Moreover, the obvious asymmetric displacements, demonstrating a northeasterly displacement trend, further reveal that faults control aquifer distribution and Earth surface deformation. These findings are useful for understanding the land motion patterns and dynamics, helping to sustainably manage groundwater and control disasters in the YRD and elsewhere worldwide.

Improved MLP Energy Meter Fault Diagnosis Method Based on DBN

In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis.

Improved Bayesian network-based for fault diagnosis of air conditioner system

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

Acoustic Feature Extraction Method of Rotating Machinery Based on the WPE-LCMV

Fault diagnosis plays an important role in the safe and stable operation of rotating machinery, which is conducive to industrial development and economic improvement. However, effective feature extraction of rotating machinery fault diagnosis is difficult in the complex sound field with characteristics of reverberation and multi-dimensional signals. Therefore, this paper proposes a novel acoustic feature extraction method of the rotating machinery based on the Weighted Prediction Error (WPE) integrating the Linear Constrained Minimum Variance (LCMV). The de-reverberation signal is obtained by inputting multi-channel signals into the WPE algorithm using an adaptive optimal parameters selection function with the sound field changes. Then, the incident angle going from the fault source to the center of the microphone array is calculated from the full-band sound field distribution, and the signal is de-noised and fused using the LCMV. Finally, the fault feature frequency is extracted from the fused signal envelope spectrum. The results of fault data analysis of the centrifugal pump test bench show that the Envelope Harmonic Noise Ratio (EHNR) is more than twice that of the original signal after the WPE-LCMV processing. Compared to the Recursive Least Squares and the Resonance Sparse Signal Decomposition (RLS-RSSD) and the parameter optimized Variational Mode Decomposition (VMD), the EHNR has a higher value for all types of faults after applying the WPE-LCMV processing. Furthermore, the proposed method can effectively extract the frequency of bearing faults.

Evolution of protective relays in 1-Φ, 25kV, 50Hz railway traction system

This paper presents the evolution of protective relays used for the protection of Power Transformer, Over Head Equipment (OHE) and Shunt Capacitor Bank in Indian Railways Electrical Distribution Systems. Initially during electrification Indian Railways adopted 1-Φ, 25KV, 50Hz AC system and electromagnetic relays (EM Relays) for feeder protection of OHE available at that time. But over the period of time with the drastic growth in traffic and sharp increase in demand for more electrical power to heavy goods trains haulage, the above said protective system doesn’t catered the requirements like speed, sensitivity, reliability, post fault data analysis. Hence Indian Railways over the years have undergone a paradigm shift in protective relay schemes. The journey of evolution from EM relays and static relays to the latest state of art integrated Numerical relays which were approved by RDSO are dealt here.

Life Prediction Method of Missile Based On Environmental Load Spectrum of Shipborne Tilt Launching

The fault time data of missile is an important information source to reflect the reliability and life index of missile. However, most of the failure time data are used for the life evaluation of missile, and there is no method to predict the life of missile by using the data of fault time. According to engineering experience and statistical analysis of fault data, the life characteristics of missiles are closely related to the storage environment. In this chapter, the relationship between the failure time data of missile and environmental load is established, the influence of environmental load on missile life is explored, and the life index of missile is predicted according to its service history and storage environment.

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

Selective ground-fault protection is greatly valued for the safe and reliable operation of power systems. With the wide adoption of fault indicator in distribution network, the amount of available fault data increases dramatically. The in-depth investigation of fault recording data helps improve the accuracy of faulty line identification. To perform fault data analysis with higher efficiency, a single-phase-to-ground fault identification model based on the k-Nearest Neighbor (kNN) classification algorithm is proposed in the paper. In this model, the eigenvectors consist of wavelet energy ratio, wavelet coefficients variance and wavelet power obtained by the decomposition of transient components. Furthermore, through the theoretical analysis and experimental comparison of three parameter adjustment algorithms, Bayesian Optimization algorithm is selected to find the optimal parameters of fault identification model, and realize the adaptive adjustment of model parameters. Finally, the validity and feasibility of the model are verified by the experimental data, and the accuracy and efficiency of fault identification are improved by using Bayesian Optimization algorithm.

The study on the Mal-operation of the transformer current differential protection during external faults cause by CT saturation

This paper analyses the unusual mal-operation of the differential protection for transformer current differential protection during the external fault. The mal-operation reason is researched in detail, based on the mathematical analysis, the simulation, and the field recorded fault data analysis. It is disclosed that the CT saturation leads to the maloperation. Based on this, this paper analyses the improvement for the CT saturation identification method, and discusses the CT selection principles. This paper is aimed at attracting the attention on the problem of the CT saturation and proposing the reference of the analysis method the countermeasure for this problem.

Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

Monitoring data-based automatic fault diagnosis for the brake pipe of high-speed train

As a key part of a railway system, the brake pipe is essential for brake system, and its fault may cause serious consequences, and even threaten lives. The generally employed approach for the fault diagnosis of brake pipe is manual inspection during the parking time, which is time-consuming, laborious, and immensely dependent on the self-experience of the inspectors. In view of these problems, an automatic fault diagnosis analysis for the brake pipe of high-speed train based on monitoring data is proposed in this paper. Based on the concept of big data, the characteristics of monitoring data are analysed, and the fault features are extracted, then the fault diagnosis rules are established. Specifically, the main steps are as follows: first step is the data pre-processing, including correcting the singular zero points and populating the missing points; second step aims to eliminate the noise and measurement errors; third step includes the establishment of the fault diagnosis rules, data analysis and fault diagnosis. Lastly, the data from an actual train line is analysed, and the analysis results demonstrate the effectiveness and feasibility of the proposed method.

Monitoring data-based automatic fault diagnosis for the brake pipe of high-speed train

As a key part of a railway system, the brake pipe is essential for brake system, and its fault may cause serious consequences, and even threaten lives. The generally employed approach for the fault diagnosis of brake pipe is manual inspection during the parking time, which is time-consuming, laborious, and immensely dependent on the self-experience of the inspectors. In view of these problems, an automatic fault diagnosis analysis for the brake pipe of high-speed train based on monitoring data is proposed in this paper. Based on the concept of big data, the characteristics of monitoring data are analysed, and the fault features are extracted, then the fault diagnosis rules are established. Specifically, the main steps are as follows: first step is the data pre-processing, including correcting the singular zero points and populating the missing points; second step aims to eliminate the noise and measurement errors; third step includes the establishment of the fault diagnosis rules, data analysis and fault diagnosis. Lastly, the data from an actual train line is analysed, and the analysis results demonstrate the effectiveness and feasibility of the proposed method.

A Study of Analysis of Fault Data in AC Electrical Railway Power System Based on COMTRADE Using PSCAD/EMTDC

A Study of Analysis of Fault Data in AC Electrical Railway Power System Based on COMTRADE Using PSCAD/EMTDC

Application of Arrester Simulation Device in Training

Combining with the arrester simulation device put into use successfully, this paper introduces the application of arrester test in the insulation resistance measurement, counter test, Leakage current test under DC 1mA voltage and leakage current test under 0.75U1mA. By comparing with the existing training, this paper summarizes the arrester simulation device’s outstanding advantages including real time monitoring, multi-type fault data analysis and acousto-optic simulation. It effectively solves the contradiction between authenticity and safety in the existing test training, and provides a reference for further training.

Enabling Remote Fault Diagnosis through Data-driven Grid Computing

Due to the complexity of modern manufacturing and mechanical equipments, it is difficult for equipment users or maintainers to accomplish fault diagnosis independently. In this paper, the development history of fault diagnosis technology is surveyed and investigated, especially remote fault diagnosis system based on Internet in detail. Then, a remote fault diagnosis system based on grid computing technology is proposed to enable collaborative resource sharing and problem solving among multiple equipment suppliers and equipment users by integrating all kinds of diagnostic resources. The architecture of this fault diagnosis system is presented, as well as the Client-Master-Worker computation model and the diagnostic workflow. Finally, a prototype system is implemented using the data-driven middleware-BitDew, in which multiple fault diagnosis grid services are integrated in a unified Web portal. The case study of data-driven fault data analysis is conducted in our prototype which has proved the effectiveness of the system.

Kinematic Evolution of the Eastern Chalk Draw Fault during Basin and Range Extension: Evaluating the Role Played by Preexisting Structural Fabrics in Along-Strike Displacement Patterns

Basin and Range extensional structures in Trans-Pecos Texas have been interpreted to be strongly influenced by preexisting structures, including igneous centers in the Big Bend region. These igneous centers are interpreted to have deflected propagating faults, as they are rheologically strong relative to the surrounding rocks. We conducted detailed field mapping and analysis of fracture and fault data along the eastern segment of the Chalk Draw Fault in Big Bend National Park to evaluate its kinematic evolution and interaction with Paleogene plutons. Our primary results document the following key relationships: (1) the Rosillos Laccolith and other Paleogene plutons in the study area are pervasively fractured, dominantly by northwest-striking mode I fractures, which generally do not extend into adjacent sedimentary rock sequences; (2) throw on the eastern Chalk Draw Fault is strongly asymmetric, decreasing rapidly as it enters the Rosillos Laccolith; and (3) analysis of fault-slip data in the region documents dominantly N 52° E–directed regional extension along the northwest-striking segment of the Chalk Draw Fault and secondary structures. We interpret these relationships to indicate that the Rosillos Laccolith developed pervasive mode I fractures with a dominant northwest-southeast orientation during cooling under Basin and Range extensional stresses but that this occurred prior to extensional faulting within the region. We suggest that the highly fractured nature of the Rosillos Laccolith resulted in it being rheologically weaker than the surrounding Cretaceous sedimentary rocks, which resulted in (1) hindering southward propagation of the Chalk Draw Fault by dissipating stress at the fault tip within the highly fractured laccolith and (2) the strongly asymmetric distribution of slip due to a strong rheological contrast during fault propagation.

Study of Transport Vehicle Preventive Maintenance Cycle Based on Failure Rate

The fault data of 133 Xiali taxis in Xingtai Car Rental Company are collected and the preliminary sorting is conducted to them. The automobile preventive repair model is established and the analysis of the necessity and feasibility of preventive repair is performed. In the base of reliability analysis of fault data, the failure rule is obtained and the optimal repair cycle is found out.