The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis.
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http://dx.doi.org/10.3390/e26090806 | DOI Listing |
ISA Trans
January 2025
State Key Laboratory of Computer-Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Intelligent Rescue Equipment for Collapse Accidents, Ministry of Emergency Management, Hangzhou, 310030, China; Zhejiang Laboratory, Hangzhou, 311121, China. Electronic address:
Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement.
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December 2024
GEELY Automobile Research Institute Co. Ltd, Ningbo, Zhejiang 315699, China. Electronic address:
The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.
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January 2025
Centre de Recherche en Automatique de Nancy-Lorraine University, 2 avenue de la Forêt de Haye, BP, Vandoeuvre Lès Nancy 54516, France. Electronic address:
This paper explores a novel challenge regarding bidirectional Automated Guided Vehicles (AGVs): supervisory control amidst potential sensor faults. The proposed approach uses an event-based control architecture, guided by Supervisory Control Theory (SCT), to achieve non-blocking routing of AGVs. Unlike most routing approaches assuming full event observability, this paper investigates scenarios where events might become unobservable due to sensor faults or disturbances, which may affect the supervisor efficiency.
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January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
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January 2025
Automation School Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.
Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies.
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