This paper addresses robust fault detection observer design for a class of discrete-time Takagi-Sugeno fuzzy systems with finite-frequency specifications. A novel design method is presented based on finite-frequency H/H indices and peak-to-peak analysis. The finite-frequency H and H indices are utilized to characterize fault sensitivity and disturbance robustness, respectively. Peak-to-peak analysis is used to derive a dynamic threshold. To further reduce the conservatism caused by predefined parameters, an iterative algorithm is developed. Both theoretical proof and simulation results show that the performance of the proposed method is not worse than the existing works.
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http://dx.doi.org/10.1016/j.isatra.2024.10.010 | DOI Listing |
Sci Rep
December 2024
Business Segment Networks, Stadtwerke Flensburg GmbH, 24939, Flensburg, Germany.
In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks.
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December 2024
School of Electronic Information, Central South University, Changsha, 410075, China.
Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection.
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December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
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December 2024
Department of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South Africa.
Solar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD) of thermal images and ensuring they are publicly available.
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December 2024
Department of Power Engineering and Transportation, University of Life Sciences in Lublin, Gleboka 28, 20-612, Lublin, Poland.
Engine oil is a valuable source of information on the technical condition of the drive unit. Under the influence of many factors, including operating conditions, time, high temperature, and various types of contamination, the oil gradually degrades, which can result in serious engine damage. The subject of the article focuses on an attempt to answer the questions of how engine failure affects the degradation of engine oil and whether we can use this knowledge to detect potential problems in public transport vehicles at an early stage.
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