Industrial Instrument and Intelligent Measurement

Topic Information

Dear Colleagues,

Industrial instrumentation and measurement technology form the critical foundation for modern industrial systems, enabling precise monitoring, control, and optimization of production processes. With the rapid advancement of sensing technologies, data analytics, and artificial intelligence, the field of industrial measurement is undergoing a profound intelligent transformation. The integration of AI, machine learning, and the Industrial Internet of Things (IIoT) with traditional measurement instruments is giving rise to a new generation of intelligent measurement systems. These systems are capable of self-diagnosis, adaptive sensing, real-time data processing, and predictive decision-making, thereby significantly enhancing the efficiency, reliability, and safety of industrial operations across diverse sectors such as manufacturing, energy, transportation, and civil infrastructure.

This Topic, “Industrial Instrument and Intelligent Measurement,” aims to compile the latest research and innovative applications at the intersection of advanced instrumentation, intelligent sensing, and data-driven measurement science. We seek to explore how intelligent algorithms and novel sensor technologies are revolutionizing measurement paradigms, ranging from non-destructive testing (NDT) and structural health monitoring (SHM) to precision metrology and process control.

We invite researchers and engineers to contribute original research articles and comprehensive reviews that address both theoretical developments and practical challenges. Topics of interest include, but are not limited to:

• Novel designs, materials, and technologies for intelligent industrial sensors and instruments.
• The fusion of AI and machine learning with measurement systems for anomaly detection, predictive maintenance, and quality assessment.
• Advanced signal and data processing techniques for industrial measurement, including noise reduction, feature extraction, and real-time analytics.
• Intelligent NDT&E technologies (e.g., ultrasonic, electromagnetic, optical) and their applications in railways, aerospace, pipelines, and special equipment.
• UAV-based intelligent inspection platforms with multi-sensor payloads (e.g., LiDAR, electromagnetic, optical) for autonomous monitoring of pipelines, power lines, and critical infrastructure.
• Autonomous UAV path planning, obstacle avoidance, and terrain-following technologies for aerial measurement in complex and unstructured environments.
• Multi-sensor fusion and IIoT-based measurement networks for complex industrial environments.
• Smart calibration, self-validation, and reliability enhancement of measurement instruments.
• Case studies demonstrating the impact of intelligent measurement on industrial productivity, safety, and sustainability.
• Emerging trends, such as digital twins for measurement systems and edge computing for distributed sensing.

This collection will provide a platform for sharing cutting-edge insights and fostering discussions on the future of intelligent industrial measurement. We look forward to your valuable contributions.

Prof. Dr. Qibo Feng
Dr. Lisha Peng
Dr. Hongyu Sun
Dr. Luyao He
Dr. Xiang Gao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Acoustics acoustics	1.5	3.0	2019	21.7 Days	CHF 1600	Submit
Actuators actuators	2.4	4.3	2012	16.6 Days	CHF 2400	Submit
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Instruments instruments	1.4	2.8	2017	25.8 Days	CHF 1400	Submit
Metrology metrology	2.1	2.7	2021	29.1 Days	CHF 1200	Submit
NDT ndt	-	-	2023	16 Days	CHF 1000	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit

* Median value for all MDPI journals in the first half of 2026.

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Published Papers (2 papers)

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All Acoustics Actuators Applied Sciences Instruments Sensors Metrology NDT AI Sensors

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18 pages, 7278 KB  

Open AccessArticle

Laser Measurement Method and Model for Six-Degree-of-Freedom Relative Pose Deformation of Structures

by Ying Zhang, Fajia Zheng, Yue Qiu, Hongjun Fang, Qibo Feng, Bin Zhang, Hongyu Sun, Xin Xu, Fei Long and Lili Yang

Appl. Sci. 2026, 16(16), 8151; https://doi.org/10.3390/app16168151 - 15 Aug 2026

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Abstract

High-precision measurement of six-degree-of-freedom relative pose is a critical challenge for deformation control and stability improvement of structural components in precision assembly and aerospace structural components. To address incomplete pose-parameter measurement and the difficulty of pose-parameter decoupling in existing methods, this paper proposes [...] Read more.

High-precision measurement of six-degree-of-freedom relative pose is a critical challenge for deformation control and stability improvement of structural components in precision assembly and aerospace structural components. To address incomplete pose-parameter measurement and the difficulty of pose-parameter decoupling in existing methods, this paper proposes an error-modeling and crosstalk-compensation method for 6-DOF relative pose measurement based on the fusion of fiber-coupled heterodyne interferometry and laser collimation. First, a relative pose measurement model was established using homogeneous coordinate transformations and ray-tracing methods, wherein physically reasonable constraints on structural deformation were introduced to compensate for crosstalk caused by the coupling between angular and displacement errors based on COMSOL simulation, and this model was later validated via Zemax. Second, a measurement system was constructed, followed by calibration experiments and a 35 h stability test to characterize its measurement accuracy and long-term stability. Finally, three repeated plate loading and unloading cycles were conducted under structural deformation conditions. The comparative results show the maximum residuals of 0.92 μm, 0.79 μm, 1.00 μm, 0.54″, 2.53″, and 1.53″ for Δx, Δy, Δz, Δα, Δβ, and Δγ compared to reference instruments, while it drops from 61.13 μm to 1.00 μm for Δz after model compensation, reducing error by 96.79%. The proposed method provides an effective approach for high-precision measurement and decoupling of the 6-DOF relative pose of structural components under complex operating conditions. Full article

(This article belongs to the Topic Industrial Instrument and Intelligent Measurement)

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30 pages, 10631 KB  

Open AccessArticle

Trajectory Tracking of Reentry Vehicle Based on KalmanNet with Time-Varying Observation Matrix

by Xinmiao Liu, Wanchun Chen, Wengui Lei and Zijiao Wang

Actuators 2026, 15(7), 379; https://doi.org/10.3390/act15070379 - 6 Jul 2026

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Abstract

This paper proposes a trajectory-tracking algorithm for reentry vehicles based on KalmanNet with a time-varying observation matrix. First, a nonlinear state evolution model of the reentry vehicle and a radar measurement model are developed in the radar measurement coordinate system. Then, inspired by [...] Read more.

This paper proposes a trajectory-tracking algorithm for reentry vehicles based on KalmanNet with a time-varying observation matrix. First, a nonlinear state evolution model of the reentry vehicle and a radar measurement model are developed in the radar measurement coordinate system. Then, inspired by the computation process of the Kalman gain (KG) in the extended Kalman filter (EKF), the recurrent neural network (RNN) architecture of KalmanNet is improved. The gated recurrent unit (GRU) originally used to track process noise statistics is removed. Instead, the input features are redesigned to directly estimate the prior state covariance. Furthermore, another GRU is introduced to estimate the time-varying observation matrix, considering the nonlinear characteristics of radar measurements. The calculated observation matrix is fed into both the GRU responsible for estimating the covariance of the difference between the predicted observation and the observed value and the fully connected layer that computes the KG. Finally, the proposed method is compared with six representative algorithms, including EKF, particle filter (PF), unscented Kalman filter (UKF), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and the original KalmanNet. Simulation results demonstrate that the proposed method achieves the highest estimation accuracy, while its computational time remains nearly the same as that of the original KalmanNet. Monte Carlo simulations under three model-mismatch conditions are conducted to validate the robustness of the proposed method. Full article

(This article belongs to the Topic Industrial Instrument and Intelligent Measurement)

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