Intelligent Industrial Instrument Accuracy Enhancement and Process Optimization System Based on Embedded Signal Processing and Reliability Verification
DOI:
https://doi.org/10.71204/y8nm2358Keywords:
Industrial Instruments, Embedded Signal Processing, Intelligent Sensing, Process Optimization, Industrial Automation, Reliability Verification, Intelligent ManufacturingAbstract
Industrial instruments are critical sensing and control components in intelligent manufacturing systems, industrial automation platforms, precision inspection environments, and digital production lines. The measurement accuracy, operational stability, and environmental adaptability of industrial instruments directly affect production quality, process controllability, and equipment reliability. However, conventional industrial instruments still face several practical engineering challenges, including signal distortion caused by electromagnetic interference, insufficient sensing adaptability in complex industrial environments, unstable mass-production consistency, and limited reliability verification capability. To address these issues, this paper proposes an intelligent industrial instrument optimization framework integrating embedded signal processing, adaptive sensing technology, production process optimization, and reliability verification methods. The proposed system combines modular circuit architecture, adaptive sensor interface optimization, Kalman-filter-based signal processing, temperature compensation strategies, automated calibration mechanisms, and industrial reliability evaluation technologies into a unified engineering implementation platform. High-performance embedded processing units and industrial communication architectures are integrated to improve data acquisition stability, anti-interference capability, and dynamic response performance. A high-precision industrial pressure measurement instrument is selected as the engineering validation platform. Through process parameter optimization, dynamic calibration, automated production management, and environmental adaptability verification, the optimized instrument achieves measurement accuracy within ±0.1%FS and stable operation under vibration, electromagnetic interference, and wide-temperature industrial environments. Experimental results demonstrate that the proposed framework significantly improves signal stability, manufacturing consistency, production efficiency, and long-term operational reliability compared with conventional industrial instrument systems. The proposed research establishes a practical technical framework for intelligent industrial sensing systems and provides engineering support for digital manufacturing, industrial automation, embedded measurement systems, and intelligent quality-control applications.
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Copyright (c) 2025 Haijun Lei, Dengcheng Lu, Shishui Zhou, Chaohui Zhang, Lujie Ren, Jianming Mao (Author)
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