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Advanced Sensor Fusion in Industry 4.0

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 8975

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Special Issue Editor

Prof. Dr. Luigi Rovati

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Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy
Interests: optical optic sensor; instrumentation and measurement methods; lidar; industrial smart measurements; IoT; front-end electronics, sensor fusion

Special Issue Information

Dear Colleagues,

The technological transformations fostered by Industry 4.0 (and the forthcoming Industry 5.0) paradigms deeply rely on a data-centric vision, where smart and advanced sensing technologies, together with innovative measurement paradigms, need to seamlessly cooperate to ensure reliability, accuracy, and timeliness.

A significant option nowadays is represented by the deployment of integrated and multiple sensors, whose outputs can be intelligently combined to provide meaningful, highly accurate, and more representative information about a physical process. This sensor fusion approach is becoming increasingly appealing in several fields and for many Industry 4.0 applications, such as additive manufacturing scenarios.

Moreover, the current technological advancement facilitates real-time data analysis and manipulation, potentially paving the way for the definition of novel approaches to sensor fusion based on Machine Learning and Artificial Intelligence approaches.

Potential topics include but are not limited to:

Hardware and software architectures for collaborative multi-sensor fusion;
Advance sensors and sensors systems for Industry 4.0;
Power management and energy harvesting in sensor systems;
Wired and wireless sensor networks;
Hardware and software IoT architectures;
Sensors for measurements, testbeds, calibration, and validation;
Data fusion algorithms for measurement processes;
Machine learning and artificial intelligence application in sensor fusion;
Cloud and edge computing for sensor data fusion.

Prof. Dr. Luigi Rovati
Guest Editor

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Published Papers (5 papers)

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Research

20 pages, 9790 KiB

Open AccessArticle

Research on Wearable Devices for Pedestrian Navigation Based on the Informer Model Zero-Velocity Update Architecture

by Shuai Zhang, Haotian Gao and Fushengong Yang

Sensors 2025, 25(8), 2587; https://doi.org/10.3390/s25082587 - 19 Apr 2025

Viewed by 118

Abstract

When natural disasters such as earthquakes occur, accurate navigation and positioning information may not be available, making a purely inertial pedestrian navigation system particularly important for rescuers. In this paper, researchers propose a zero-velocity update architecture for pedestrian navigation based on the Informer model, which is integrated into wearable devices. This architecture modifies the fully connected layer of the Informer model to be used for the binary classification task of the zero-velocity update method (ZUPT), allowing for accurate identification of gait information at each moment using only inertial measurement data. By wearing the device on the foot during natural disasters like earthquakes, the location of the pedestrian can be more accurately determined, facilitating rescue efforts. During the experimental process, a Kalman filter model was constructed to achieve zero-velocity updating of the pedestrian’s motion trajectory. A 2000 m walking experiment and a 210 m mixed-gait experiment were conducted to accurately identify gait information at each moment, thereby reducing the cumulative error of the inertial system. Subsequently, a convolutional neural network (CNN) model and a model combining CNN with a long short-term memory network (CNN + LSTM) were introduced as comparative experiments to verify the performance of the proposed architecture. The experimental results demonstrate that the proposed architecture enhances the adaptability of the zero-velocity update algorithm in underground or sheltered spaces, with all results outperforming the other two models. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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23 pages, 3083 KiB

Open AccessArticle

Anomaly Detection Method for Industrial Control System Operation Data Based on Time–Frequency Fusion Feature Attention Encoding

by Jiayi Liu, Yun Sha, Wenchang Zhang, Yong Yan and Xuejun Liu

Sensors 2024, 24(18), 6131; https://doi.org/10.3390/s24186131 - 23 Sep 2024

Viewed by 1941

Abstract

Anomaly detection in industrial control system (ICS) data is one of the key technologies for ensuring the security monitoring of ICSs. ICS data are characterized as complex, multi-dimensional, and long-sequence time-series data that embody ICS business logic. Due to its complex and varying periodic characteristics, as well as the presence of long-distance and misaligned temporal associations among features, current anomaly detection methods in ICS are insufficient for feature extraction. This paper proposes an anomaly detection method named TFANet, based on time–frequency fusion feature attention encoding. Considering that periodic variations are more concentrated in the frequency domain, this method first transforms the time-domain data into the frequency domain, obtaining both amplitude and phase data. Then, these data, together with the original time-series data, are used to extract features from two perspectives: long-term temporal changes and long-distance associations. Finally, the six features learned from both the time and frequency domains are fused, and the feature weights are calculated using an attention mechanism to complete the anomaly classification. In multi-classification tasks on three ICS datasets, the proposed method outperforms three popular time-series models—iTransformer, Crossformer, and TimesNet—across five metrics: accuracy, precision, recall, F1 score, and AUC-ROC, with average improvements of approximately 19%, 37%, 31%, 35%, and 22%, respectively. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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20 pages, 6529 KiB

Open AccessArticle

Gas Detection and Classification Using Multimodal Data Based on Federated Learning

by Ashutosh Sharma, Vikas Khullar, Isha Kansal, Gunjan Chhabra, Priya Arora, Renu Popli and Rajeev Kumar

Sensors 2024, 24(18), 5904; https://doi.org/10.3390/s24185904 - 11 Sep 2024

Cited by 4 | Viewed by 2557

Abstract

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long–short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client’s data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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16 pages, 2907 KiB

Open AccessArticle

Performance Evaluation Method for Intelligent Computing Components for Space Applications

by Man Xie, Lianguo Wang, Miao Ma and Pengfei Zhang

Sensors 2024, 24(1), 145; https://doi.org/10.3390/s24010145 - 27 Dec 2023

Viewed by 1535

Abstract

The computational performance requirements of space payloads are constantly increasing, and the redevelopment of space-grade processors requires a significant amount of time and is costly. This study investigates performance evaluation benchmarks for processors designed for various application scenarios. It also constructs benchmark modules and typical space application benchmarks specifically tailored for the space domain. Furthermore, the study systematically evaluates and analyzes the performance of NVIDIA Jetson AGX Xavier platform and Loongson platforms to identify processors that are suitable for space missions. The experimental results of the evaluation demonstrate that Jetson AGX Xavier performs exceptionally well and consumes less power during dense computations. The Loongson platform can achieve 80% of Xavier’s performance in certain parallel optimized computations, surpassing Xavier’s performance at the expense of higher power consumption. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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15 pages, 3175 KiB

Open AccessArticle

Standalone Sensors System for Real-Time Monitoring of Cutting Emulsion Properties with Adaptive Integration in Machine Tool Operation

by Jozef Peterka, Frantisek Jurina, Marek Vozar, Boris Patoprsty, Tomas Vopat, Vladimir Simna and Pavol Bozek

Sensors 2023, 23(13), 5794; https://doi.org/10.3390/s23135794 - 21 Jun 2023

Cited by 1 | Viewed by 1863

Abstract

This paper presents a novel cutting fluid monitoring sensor system and a description of an algorithm framework to monitor the state of the cutting emulsion in the machine tool sump. One of the most frequently used coolants in metal machining is cutting emulsion. Contamination and gradual degradation of the fluid is a common occurrence, and unless certain maintenance steps are undertaken, the fluid needs to be completely replaced, which is both un-economical and non-ecological. Increasing the effective service life of the cutting emulsion is therefore desired, which can be achieved by monitoring the parameters of the fluid and taking corrective measures to ensure the correct levels of selected parameters. For this purpose, a multi-sensor monitoring probe was developed and a prototype device was subsequently created by additive manufacturing. The sensor-carrying probe was then placed in the machine tool sump and tested in operation. The probe automatically takes measurements of the selected cutting emulsion properties (temperature, concentration, pH, level height) in set intervals and logs them in the system. During the trial run of the probe, sensor accuracy was tracked and compared to reference measurements, achieving sufficiently low deviations for the purpose of continuous operation. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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