Research on Sintering Machine Axle Fault Detection Based on Wheel Swing Characteristics

In software engineering, empirical studies on automated fault localization (FL) methods mainly focus on general software, and substantial progress has been made. However, the applicability and efficacy of these methods in specialized, domain-specific software like industrial software remains under-explored. Such specialized software is usually characterized by complex inputs and iterative computing paradigms, which could significantly influence the effectiveness of existing FL methods. To address this gap, this study takes a typical categorical of industrial software (i.e., computer-aided engineering (CAE) projects) as a case study, to investigate the feasibility and effectiveness of state-of-the-art FL methods within CAE projects. Through the reproduction of 76 real-world bugs from three widely used CAE projects (i.e., FDS, deal.II, and MFEM), we find that even the most precise FL methods require developers to examine on average 467.18 statements before finding bugs and can take 208.13 hours to execute. The complex inputs and long-term computation characteristics of CAE projects further increase the difficulty of FL. Moreover, FL on CAE also faces challenges, such as insufficient differentiation of coverage information and missing CAE-specific FL features. Based on our findings, we improve FL on CAE projects by proposing a set of CAE main module-based features, which improve the best-performed FL method in this study (i.e., DeepFL) by 35.93% and 45%, in terms of MAR and MFR , respectively.

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

DC fault current is contributed by various distributed energy resources in DC distribution systems. Most DC distribution systems have low line impedance. The fault current increases fast because of the low impedance. Some converters in DC distribution systems include Fault Current Limiting (FCL) function. The controlled fault currents at different locations are very close. Thus, it is important to design a reliable and fast fault detection and location method for DC distribution systems. This paper proposes a novel local measurement-based fault location algorithm for DC distribution systems. The proposed fault location algorithm can estimate the equivalent inductance from a protective device to a fault in less than one millisecond. The performance of the developed protection algorithm was validated in both numerical simulation and hardware testing.

Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC) features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

A fault detection and location algorithm on three-terminal transmission line is proposed. Vast literature on fault location shows that to simplify the analysis, line charging currents are often ignored. To improve the accuracy of fault location, synchronized current and voltage measurements from three ends of the three-terminal line are utilized to detect and locate distance to fault while considering the line charging currents. The method first detects the zone at which the fault has occurred and then locates it from the respective line ends. Since three-terminal lines provide solutions for connecting existing system to the grid, facilitating bulk transfer of power, thus the fault detection and location on the same is important to maintain reliability and security of the system. Performance of the proposed algorithm is tested for various fault types, fault locations, fault resistances and fault inception angles. The 200-kV system is simulated in ATP/EMTP; however, the algorithm is developed in MATLAB. The extensive simulation studies demonstrate accuracy of the proposed scheme.

Aiming at the characteristic of Inter-turn short circuit (ITSC) fault of permanent magnet synchronous motor (PMSM), which is easily affected by load disturbance, this paper proposes a robust fault detection and location method for ITSC of PMSM based on voltage residual vector. Firstly, the change of motor back EMF caused by fault is analyzed by the motor voltage equation. Then the robust sliding mode observer (SMO) is used to predict the extended back EMF residual vector when the ITSC fault occurs to realize fault detection. Finally, the fault location is realized by generating a three-phase residual between the transformed three-phase back EMF and the reference back EMF. The simulation results show that this method can locate the ITSC fault and fault phase under sudden load and speed changes.

ABSTRACTFaults in power transmission systems pose significant challenges due to the complexity and length of transmission lines. Effective fault detection, classification and location are essential for preventing further damage to the power grid. While travelling wave‐based algorithms are commonly used for fault location, they often focus on identifying the fault's location without classifying the fault type. Accurate classification is crucial for enabling efficient and timely responses from protection systems. This paper introduces an integrated model for fault detection, classification and location using voltage signals from a single terminal of a series‐compensated transmission line with a static synchronous series compensator (SSSC). The Gabor Transform (GT) is utilised for feature extraction, enabling both fault detection and classification. Travelling wave theory is then applied to identify the faulty segment and estimate the fault location. Additionally, a novel technique adaptively calculates the threshold value during the protection algorithm's execution. The proposed method is validated through a comprehensive analysis of various fault scenarios and sensitivity analysis. Numerical simulations in MATLAB/Simulink show that the model achieves 100% accuracy for fault detection, classification, and faulty segment identification, with 99.7925% accuracy for fault location estimation, demonstrating its effectiveness in fault management.

Distance protection has been a challenging task due to the complexity of the power system. An effective protection scheme must perform fault detection, classification and location that help in quick restoration of the power supply. Although significant hybrid methods are available for fault detection, classification, and location, separately; there is always a need for an improved integrated approach performing fault detection, classification, and location. This chapter presents an integrated approach using autoregressive modeling and extreme learning machine (ELM). The performance of the scheme is investigated on 11 kV, 6 km medium voltage underground cable. The test system is simulated using PSCAD/EMTDC and the integrated protection scheme is implemented in MATLAB. The scheme uses half-cycle duration current samples. The data preprocessing is done using autoregressive (AR) modeling. The scheme detects the fault using the power of the critical pole. ELM binary classifier uses the AR coefficients to classify the type of fault. ELM in regression mode locates the fault distance. A total of 1260 data set is used to train the ELM fault classifier and ELM fault locator. The integrated protection scheme is tested with 10,800 test cases covering wide variation in operating conditions. The classification accuracy of the ELM fault classifier is 99.85% and the fault location error is less than 0.12% for the ELM fault locator, which is better than the other hybrid methods. Hence this integrated approach can be considered as quite suitable for protection of medium voltage underground cables.

Fast and selective DC fault detection and location schemes are needed for fault protection of DC Grids. In this paper, the frequency domain characteristics of DC voltage besides the current limiting reactors are analyzed and used to put forward a new fault detection method for DC grids. Hilbert-Huang transform is applied on the DC voltage waves to extract the transient frequency and transient amplitude of the DC voltage. The influence of fault type, fault distance and fault resistance of DC faults and the noise on the frequency domain transient characteristics on voltage waves are then studied. Support vector machine learns the mapping between the transient frequency and transient amplitude of the DC voltage and desired outputs, fault type and fault location through training process. Then, a fast and accurate fault detection scheme based on frequency-domain characteristics of DC voltage is proposed by SVM for DC fault detection and location in MTDC systems. This fault detection scheme requires no telecommunication, has anti-interference ability and good sensitivity and selectivity for detection of long-distance fault and high resistance grounding fault. Based on the four-terminal HVDC system built by PSCAD/EMTDC, simulations under different fault conditions are carried out to verify the effectiveness of the proposed DC fault detection scheme.

The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded B-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch's state. By monitoring each cell's output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method.

Fault detection, classification, and location estimation are paramount in transmission lines to ensure uninterrupted power supply, enhance reliability, and safety. Faster and more accurate fault localization is critical for prompt repair and efficient power restoration, thus minimizing economic losses. This paper presents a novel Fault Detection Isolation and Localization (FDIL) scheme, a machine learning (ML)-based framework, to increase the accuracy of fault location (FL) prediction. The proposed framework utilizes the phasor voltage, current, and frequency data obtained from optimally placed Phasor Measurement Units (PMUs) for fault detection, isolation, and localization. This paper also proposes a hybrid deep learning-based approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks and an attention mechanism for FL prediction. Extensive simulations using the IEEE 9-bus system, with different types and locations of faults, are performed on the OPAL-RT simulator-based testbed to generate and train the ML models. The performance of the proposed scheme is evaluated by calculating the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) metric. The results demonstrate that the proposed approach achieves high accuracy in FL prediction, with an MAE of 0.0228 km, an MSE of 0.001, and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9894. Compared to the best-performing technique reported in the literature (CNN-LSTM, MAE = 0.211km), the proposed approach achieves an 89.2% reduction in localization error. The proposed scheme and model outperform the contemporary model, as reported in various literature, making it an effective solution for real-time fault monitoring in transmission systems.

Small current grounding power system is widely used in overhead transmission line in medium voltage distribution network in China. Because of its long lines, branch, space truss structure is complex, low level of automation, It is easily influenced by outside force and the natural environment, and single-phase grounding failure rate is high. It is an important topic to quickly and accurately detect the location of the fault line, and improve the reliability of power supply and distribution system.This paper has carried on the induction and the comparison of various fault detection and location method of overhead transmission line which is currently used in single phase to ground, and analyzed their advantages and disadvantages,given the corresponding conclusions, provided the reference for finding out the reasonable single-phase earth fault location method or improvement.

The integration of DC networks including DC microgrids (DCMGs) into power systems is rapidly increasing. This is notably attributed to the distinctive fault current characteristics arising from inverter-based distributed generation resources in DCMGs, which differentiate them from conventional networks. As a result, the protection of DCMGs presents considerable challenges. Leveraging the recent strides in artificial intelligence, this paper introduces a novel multi-agent-based protection scheme for DC microgrids. Subsequently, three fault classification approaches are proposed in an intelligent protection scheme platform, employing diverse machine learning-based methods as a backup protection for fault detection and the main protection for fault location. The proposed protection scheme uses three main protection layers—namely, equipment, substation, and system—each endowed with specialized agents. In this way, the first and second fault classification approaches employ classifiers based on machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree (DT), to ascertain the microgrid status. Subsequent fault location is accomplished through various neural networks dedicated to the fault location. In the third approach, three Deep Neural Networks (DNNs) are proposed for fault classification, prompting the exclusion of classifiers from the substation layer due to the heightened training proficiency of DNNs. Intelligent Electronic Devices (IEDs) are placed at the beginning of the lines and send voltage and current information to the substation layer. Communication among agents and layers is performed by the IEC-61850 protocol. Comprehensive simulations and analyses are conducted using DIgSILENT, MATLAB, and Python (TensorFlow platform and Keras library) software. The findings underscore the efficacy of the proposed scheme and Fault Detection and Location (FDL) approaches, affirming their capability for precise fault classification and location determination.

A new method is presented for the detection and location of high resistance earth faults in medium voltage distribution networks. The systems considered are with unearthed or compensated neutral and the fault resistances covered in the range 5-160 kohm.