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The study presents a differential scheme for microgrid protection using time-frequency transform such as S-transform. Initially, the current at the respective buses are retrieved and processed through S-transform to generate time-frequency contours. Spectral energy content of the time-frequency contours of the fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid-connected and islanded mode. The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and high-impedance faults in the microgrid with radial and loop structure. It is observed that a set threshold on the differential energy can issue the tripping signal for effective protection measure within four cycles from the fault inception. The results based on extensive study indicate that the differential energy-based protection scheme can reliably protect the microgrid against different fault situations and thus, is a potential candidate for wide area protection.

Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of variable multi-scale morphological filtering (VMSMF) and average multi-scale double symbolic dynamic entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal-scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the turn domain resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

Purpose With the deepening integration of rail transit systems–encompassing urban rail, regional railways, trunk lines and medium-low capacity transportation–the four-network integration imposes higher demands on operation and maintenance systems regarding cross-modal coordination, full-element interconnectivity and dynamic responsiveness. Design/methodology/approach This paper, based on policy directives and engineering practices, analyzes the operational maintenance characteristics of urban rail traction systems from perspectives including device interconnectivity and fault data mining. A non-intrusive high-frequency diagnostic device independent of vehicle control is proposed, informed by practical onboard operation experience. This innovation significantly enhances diagnostic accuracy for components requiring high sampling frequency, while integrating “Flash” storage with far greater capacity than conventional control chips. Findings This article will systematically introduces the key points and diagnostic methods for typical faults in urban rail traction systems. Through rational diagnostic algorithms combined with high-precision, high-storage diagnostic instrumentation, the overall safety and reliability of urban rail traction systems have been improved. The proposed non-intrusive high-frequency diagnostic solution has been validated across multiple rail lines. Originality/value This paper introduces an innovative non-intrusive diagnostic device with a dual-channel design for multi-system compatibility and a high-speed acquisition architecture enabling 400 kHz sampling. Its originality stems from the independent, high-fidelity capture of microsecond-level transient faults like IGBT shoot-through and pantograph arcing; Validated in operational environments, this approach provides a significant leap in diagnostic precision, directly enhancing traction system availability and operational safety by enabling precise fault localization and intelligent, adaptive protection strategies.

The combination of multi-phase extension and pre-existing fault reactivation results in a complex fault pattern within hydrocarbon-bearing basins, affecting hydrocarbon exploration at different stages. We used high-resolution 3D seismic data and well data to reveal the impact of multi-phase extension and pre-existing fault reactivation on Cenozoic fault pattern changes over time in the Jiyang Depression of eastern China. The results show that during the Paleocene, a portion of NW-striking pre-existing faults reactivated under NS extension and controlled the basin structure (type 1). Other parts of the NW-striking pre-existing faults stopped activity and served as weak surfaces, and a series of NNE-striking faults were distributed in an en-echelon pattern along the NW direction at shallow depths (type 2). In areas unaffected by pre-existing faults, NE-striking faults formed perpendicular to regional stresses. During the Eocene, the regional stresses shifted clockwise to near-NS extension, and many EW-striking faults developed within the basin. The NE-striking faults and the EW-striking faults were hard-linked, forming the ENE-striking curved faults that controlled the structure in the basin (type 3). The NNE-striking faults were distinctly strike-slip at this time, with the ENE-striking faults forming a horsetail pattern at their tails. Many ENE-striking faults perpendicular to the extension direction were formed in areas where the basement was more stable and pre-existing faults were not developed (type 4). There were also developing NS-striking faults that were small in scale and appeared in positions overlapping different main faults (type 5). Additionally, different fault patterns can guide different phases of hydrocarbon exploration. Type 1, type 2, and type 3 faults are particularly suitable for early-stage exploration. In contrast, type 4 and type 5 faults are more appropriate for mature exploration areas, where they may reveal smaller hydrocarbon reservoirs.

This paper introduces a modified soft margin optimal hyperplane (MSMOH) algorithm, which enhances the linear separating properties of support vector machines (SVMs) by placing higher penalties on large misclassification errors. This approach improves margin symmetry in both balanced and asymmetric data distributions. The research is divided into two main stages. The first stage evaluates MSMOH for synthetic data classification and its application in heart disease diagnosis. In a cross-validation setting with unknown data, MSMOH demonstrated superior average performance compared to the standard soft margin optimal hyperplane (SMOH). Performance metrics confirmed that MSMOH maximizes the margin and reduces the number of support vectors (SVs), thus improving classification performance, generalization, and computational efficiency. The second stage applies MSMOH as a novel synthesis algorithm to design a neural associative memory (NAM) based on a recurrent neural network (RNN). This NAM is used for fault diagnosis in fossil electric power plants. By promoting more symmetric decision boundaries, MSMOH increases the accurate convergence of 1024 possible input elements. The results show that MSMOH effectively designs the NAM, leading to better performance than other synthesis algorithms like perceptron, optimal hyperplane (OH), and SMOH. Specifically, MSMOH achieved the highest number of converged input elements (1019) and the smallest number of elements converging to spurious memories (5).

This paper considers the verification of decentralized fault pattern diagnosability for discrete event systems, where the pattern is modeled as a finite automaton whose accepted language is the objective to be diagnosed. We introduce a notion of codiagnosability to formalize the decentralized fault pattern diagnosability, which requires the pattern to be detected by one of the external local observers within a bounded delay. To this end, a structure, namely a verifier, is proposed to verify the codiagnosability of the system and the fault pattern. By studying an indeterminate cycle of the verifier, sufficient and necessary conditions are provided to test the codiagnosability. It is shown that the proposed method requires polynomial time at most. In addition, we present an approach to extend the proposed verifier structure so that it can be applied to centralized cases.

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method.

Resource leaks in Android apps are pervasive. They can cause serious performance degradation and system crashes. In recent years, many resource leak detection techniques have been proposed to help Android developers correctly manage system resources. Yet, there exist no common databases of real-world bugs for effectively comparing such techniques to understand their strengths and limitations. This paper describes our effort towards constructing such a bug database named DroidLeaks. To extract real resource leak bugs, we mined 124,215 code revisions of 34 popular open-source Android apps. After automated filtering and manual validation, we successfully found 292 fixed resource leak bugs, which cover a diverse set of resource classes, from 32 analyzed apps. To understand these bugs, we conducted an empirical study, which revealed the characteristics of resource leaks in Android apps and common patterns of resource management mistakes made by developers. To further demonstrate the usefulness of our work, we evaluated eight resource leak detectors from both academia and industry on DroidLeaks and performed a detailed analysis of their performance. We release DroidLeaks for public access to support future research.

The notion of an instruction sequence fault is considered as a theoretical concept, for which the justification of the qualification of a fragment as faulty is mathematical instead of pragmatic, the latter approach being much more common. Starting from so-called Laski faults a range of patterns of faults and changes thereof for instruction sequences is developed.