Research Article
Measurement Uncertainty Assessment of Torque Sensor in Robot Joints
Issue:
Volume 14, Issue 1, March 2026
Pages:
1-5
Received:
7 October 2025
Accepted:
4 January 2026
Published:
9 January 2026
Abstract: Torque sensors are critical perception components in robot joints, providing essential feedback for precise motion control and operational safety. To ensure their reliability, a comprehensive metrological evaluation, including uncertainty assessment, is imperative. This paper establishes a measurement and analysis methodology based on the deadweight torque standard machine, following the national verification regulation JJG 995-2005. The indication error of a torque sensor is measured, and the primary sources of measurement uncertainty—including repeatability, the standard machine's inherent uncertainty, and the sensor indicator's resolution—are quantitatively analyzed. The combined standard uncertainty and expanded uncertainty (with a coverage factor k=2) are calculated. For a 10 Newton-meter (Nm) measurement point, the relative expanded uncertainty is determined to be 0.08%. The results confirm the sensor's compliance with specifications and provide a validated framework for the uncertainty assessment of torque sensors in robotic applications, thereby supporting the pursuit of higher-precision robot control. This study not only demonstrates the practical application of uncertainty evaluation in torque sensors for robotics but also offers a methodological reference for manufacturers and researchers to enhance the accuracy and reliability of torque sensing systems in advanced robotic applications. The findings contribute to the development of more precise and safer robotic systems, particularly in fields such as medical robotics and high-precision assembly.
Abstract: Torque sensors are critical perception components in robot joints, providing essential feedback for precise motion control and operational safety. To ensure their reliability, a comprehensive metrological evaluation, including uncertainty assessment, is imperative. This paper establishes a measurement and analysis methodology based on the deadweigh...
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Research Article
Design of a Relay-Free Driving Module for DC Signal Machines in Distributed Railway Systems
Shumin Ge*
,
Meili Xing,
Yimin Wang,
Binbin Huang
Issue:
Volume 14, Issue 1, March 2026
Pages:
6-13
Received:
27 November 2025
Accepted:
5 February 2026
Published:
21 February 2026
Abstract: The centralized control architecture for railway signal drivers often experiences unstable supply voltage and insufficient drive current due to the extended length of signal cables. Furthermore, the high inrush currents during the operation of conventional electromechanical relays can damage signal machines and reduce their service life. To address these issues, this paper presents a relay-free drive module for DC signal machines based on a distributed system. The module integrates remote communication and safety control technologies, enabling the deployment of safety execution units trackside. It utilizes power-driven DC-DC conversion technology, replacing relay-based control circuits to ensure safe driving of DC signals. Based on the "fail-safe"principle, the module incorporates circuit self-testing and closed-loop drive control to guarantee secure and reliable operation. The intelligent control core employs a heterogeneous dual-channel (2oo2) architecture using System-on-Chip (SoC) technology to enhance security and mitigate common-cause failures. Experimental verification confirms that the proposed module delivers highly stable and programmable DC output voltage across a wide input range, demonstrating excellent voltage regulation and controllability via Pulse Width Modulation (PWM). The all-electronic design eliminates contact arcing and inrush current, while integrated real-time monitoring and self-diagnostic capabilities ensure operational safety and reliability. These results underscore the module's strong potential for practical application in modern distributed trackside control systems, offering advantages in system simplification, maintenance reduction, and enhanced equipment longevity.
Abstract: The centralized control architecture for railway signal drivers often experiences unstable supply voltage and insufficient drive current due to the extended length of signal cables. Furthermore, the high inrush currents during the operation of conventional electromechanical relays can damage signal machines and reduce their service life. To address...
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Research Article
Data-driven Fault Detection and Diagnosis of Power Transformers Using Dissolved Gas Analysis and Random Forest Classifiers
Daniel Kumi Owusu*
Issue:
Volume 14, Issue 1, March 2026
Pages:
14-28
Received:
16 February 2026
Accepted:
27 February 2026
Published:
12 March 2026
DOI:
10.11648/j.acis.20261401.13
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Abstract: Power transformers are critical and high-value assets in electric power systems, and their unexpected failure can lead to severe economic losses, safety hazards, and prolonged service interruptions. Dissolved Gas Analysis (DGA) is widely used for transformer condition monitoring. However, conventional interpretation techniques rely on fixed thresholds and heuristic rules. These methods often struggle under complex, overlapping, or incipient fault conditions. This study proposes a data-driven framework for transformer fault detection and diagnosis. The framework integrates DGA with a Random Forest classification model. Its purpose is to improve diagnostic reliability and interpretability. Historical, labelled DGA data comprising hydrogen, carbon monoxide, ethylene, and acetylene concentrations were analysed and classified into normal operation, partial discharge, overheating, and arcing fault categories. To enhance model robustness, multicollinearity was mitigated through feature selection, while class imbalance was addressed using the Synthetic Minority Over-sampling Technique. The Random Forest classifier was trained with optimised hyperparameters and evaluated using precision, recall, F1-score, confusion matrix analysis, and out-of-bag error estimation. The results demonstrate high diagnostic accuracy for normal operating conditions and partial discharge faults, with strong precision and recall, while moderate performance was observed for overheating and arcing faults due to inherent overlap in gas generation patterns. Feature importance analysis further revealed the relative contributions of key dissolved gases, enhancing model transparency and engineering insight. The findings confirm that ensemble learning can effectively capture nonlinear relationships in DGA data that are not addressed by conventional methods. This work contributes an interpretable and practical diagnostic framework that supports predictive maintenance, informed decision-making, and improved reliability of transformer condition monitoring in modern power systems.
Abstract: Power transformers are critical and high-value assets in electric power systems, and their unexpected failure can lead to severe economic losses, safety hazards, and prolonged service interruptions. Dissolved Gas Analysis (DGA) is widely used for transformer condition monitoring. However, conventional interpretation techniques rely on fixed thresho...
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