Traditional Sensors Research Articles

Enhancing Industrial Automation Through AI-driven Sensors: A Comprehensive Study on Efficiency, Safety, and Predictive Maintenance

The fast development of the industry of Industry 4.0 is reshaping the manufacturing landscape. The key to this transformation is the integration of sensors with artificial intelligence, making traditional sensors capable of processing data and making decisions. This paper will concentrate on how AI-driven smart sensors-such as temperature, pressure, infrared, and optical sensors are redefining industrial automation. Followed by case studies across sectors like manufacturing and energy demonstrate how sensors reduce downtime, optimize performance, and anticipate failures.

Fiber optic sensing technology in underground pipeline health monitoring: a comprehensive review

Pipelines play a critical role in transporting water, oil, and gas and are indispensable for urban development. However, monitoring underground pipelines is challenging due to the complex environment in which they are situated. Traditional sensors have limitations in all-round and real-time monitoring, while fiber optic sensors offer several advantages, including large coverage, high sensitivity, long sensing distance, and corrosion resistance. As such, fiber optic sensing technology (FOST) has emerged as a promising tool for underground pipeline monitoring. This review article provides a comprehensive overview of FOST, including its basic principles, classification, and research progress in underground pipeline monitoring. Specifically, this article focuses on the technology’s application in monitoring pipeline leakage, deformation, corrosion, and geological natural disasters. In addition, the article highlights the need for future research and development in FOST. For example, combining FOST with artificial intelligence can enhance the accuracy and efficiency of pipeline monitoring. Overall, this review article offers valuable insights into the potential of FOST for underground pipeline monitoring and provides essential guidance for future research in this field.

EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use.

Augmented deep transfer learning for SRP condition monitoring via physically-informed WGAN-GP approach

Abstract Traditional dynamometer card sensors are costly and complex, making them unsuitable for real-time sucker rod pumping (SRP) well diagnostics. Recently, SRP diagnosis models using motor power curves offer an alternative, but irregular power curves and limited labeled data present challenges. To address this, we propose a deep transfer model enhanced by Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation. First, a physics-driven model reconstructs polished rod torque from motor power, emphasizing fault features. Second, WGAN-GP generates faulty SRP torque-displacement samples to expand training data. Finally, a fully parameter-tuned deep transfer SRP diagnosis framework is established, which improves the automatic learning of advanced fault features and enhances diagnostic accuracy using the augmented dataset. Experiments confirm the model's superior performance and generalization.

Enhanced and Sustainable Indoor Carbon Dioxide Monitoring by Using Ambient Light to Power Advanced Biological Sensors

Enhancing carbon dioxide (CO2) detection is crucial for improving indoor air quality and environmental surveillance. Traditional CO2 sensors face drawbacks like high costs, large sizes, environmental impact, and reliance on external power, limiting their practicality for continuous indoor monitoring. In this research, an innovative indoor CO2‐sensing system using a self‐powered bio‐solar cell (BSC) platform is introduced. Utilizing cyanobacteria as a sensitive biocatalyst and sustainable power source, the system offers a cost‐effective, eco‐friendly, and maintenance‐free alternative to conventional sensors. It operates by monitoring electron‐transfer processes in cyanobacteria during photosynthesis, converting CO2 and water into oxygen and chemical energy, enabling accurate CO2 level monitoring. The system responds to CO2 fluctuations and issues alerts when levels are outside the recommended range of 500–1000 ppm for human health and productivity. A self‐sustaining configuration of eight BSCs—one for sensing and others for power generation—ensures continuous operation without external power. An integrated energy‐harvesting board efficiently manages power distribution to a microcontroller and display system for real‐time data visualization, with the array producing up to 400 μW. Additionally, a machine‐learning model interprets BSC outputs to accurately quantify CO2 levels, enhancing the sensor's adaptive performance.

Multifunctional Downhole Drilling Motor Speed Sensor Based on Triboelectric Nanogenerator.

The measurement of downhole drilling motor rotational speed is crucial for optimizing drilling operations, improving work efficiency, and preventing equipment failures. However, traditional downhole rotational speed sensors suffer from power supply limitations, which can increase drilling costs. To address this issue, this study presents a novel multifunctional rotational speed sensor based on triboelectric nanogenerator (TENG) technology, enabling the self-powered measurement of rotational speed, direction, and angle. Our experimental results demonstrate that the sensor operates stably within a temperature range of 0 to 150 °C and a humidity range of 0 to 90%. It achieves rotational speed measurement with an accuracy of less than 2.5% error within a range of 0 to 1000 rpm, angular measurement with a resolution of 60 degrees and an error of less than 2% within a range of 0 to 360 degrees, and rotational direction measurement. Furthermore, the sensor exhibits self-powered functionality, achieving a maximum power output of 29.1 μW when the external load is 10 MΩ. Compared to conventional rotational speed sensors, this sensor possesses the unique advantage of integrating the measurement of rotational speed, angle, and direction, while simultaneously harnessing downhole working conditions for self-power generation. These characteristics make it highly suitable for practical downhole environments.

An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships.

Stretchable and High-Performance Fibrous Sensors Based on Ionic Capacitive Sensing for Wearable Healthcare Monitoring.

Electronic textiles with remarkable breathability, lightweight, and comfort hold great potential in wearable technologies and smart human-machine interfaces. Ionic capacitive sensors, leveraging the advantages of the electric double layer, offer higher sensitivity compared to traditional capacitive sensors. Current research on wearable ion-capacitive sensors has focused mainly on two-dimensional (2D) or three-dimensional (3D) device architectures, which show substantial challenges for direct integration with textiles and compromise their wearing experience on conformability and permeability. One-dimensional (1D) stretchable fiber materials serve as vital components in constructing electronic textiles, allowing for rich structural design, patterning, and device integration through mature textile techniques. Here, a stretchable functional fiber with robust mechanical and electrical performances is fabricated based on semi-solid metal and ionic polymer, which provided a high stretchability and good electrical conductivity, enabling seamless integration with textiles. Consequently, high-performance stretchable fiber sensors are developed through different device architecture designs, including pressure sensors with high sensitivity (7.21kPa-1), fast response (60ms/30ms), and excellent stability, as well as strain sensors with high sensitivity (GF = 1.05), wide detection range (0-300% strain), and excellent sensing stability under dynamic deformations.

Characterization and enhanced carbon dioxide sensing performance of spin-coated Na- and Li-doped and Co-doped cobalt oxide thin films.

Recognizing the substantial effects of carbon dioxide on human health and the environment, monitoring CO2 levels has become increasingly vital. Owing to energy constraints and the widespread application of CO2 gas sensors, it is important to design cost-effective, more efficient, and faster response CO2 gas sensors that operate at room temperature and involve a low-cost technique. This study aims to develop a cost-effective and efficient CO2 gas detector that functions at room temperature and uses less power than traditional high-temperature CO2 sensors. In this study, we achieved this by employing innovative Co3O4 thin films with optimized spinel-structured p-type semiconductors through spin-coating, facilitated by Li and Na doping as well as Li/Na codoping. Doping with 3% Li/Na reduced the crystallite size from 92.4 to 8.03 nm and increased the band gap from 3.31 to 3.69 eV. At room temperature (30 °C), the sensor response improved significantly, increasing from 50% to 345.01% for 3% Li-Co3O4 upon the addition of 3% Na at a concentration of 9990 ppm. This performance surpasses that of most metal-oxide-based CO2 sensors reported in the literature. Additionally, this optimized sensor demonstrated a very short response time of 18.8 s and a recovery time of 16.4 s at a CO2 concentration of 9990 ppm diluted with air. It outperformed other films in terms of sensitivity, stability, response and recovery times, and performance across a wide range of relative humidity levels (43-90%). The sensor exhibited superior selectivity for CO2 than for N2, H2, and NH3. Overall, the 3% Li, Na-Co3O4 sensor is well-suited for climate change mitigation and industrial applications.

Electrochemical Reduction of Graphene Oxide on Flexible Interdigitated Electrodes and Its Application as Strain Sensors

This study presents the electrochemical reduction of graphene oxide (GO) to reduced graphene oxide (rGO) directly onto flexible interdigitated gold electrodes and its application as a strain sensor. GO reduction is achieved using cyclic voltammetry (CV). Remarkably, only a single CV cycle transformed GO into rGO to obtain low‐resistance (≈100 Ω) devices. The reduction of GO is confirmed using Raman spectroscopy and electrical measurements. rGO on flexible interdigitated gold electrodes exhibits graphene‐like characteristics when used as electrolyte‐gated field‐effect transistors. The fabricated devices display a gauge factor (GF) of ≈3.7 in the 0–794 με range. This value represents a substantial improvement, ≈60% higher than traditional metallic strain sensors, which have a GF of ≈2.1. Most importantly, the rGO strain sensor exhibits fast response (1.1 s) and recovery (0.92 s) times. The rGO sensor is mounted on a clamp with an adjustable diameter, which reveals exceptional flexibility and bendability without damage or degradation. These results highlight the potential of rGO for advancing strain‐sensing applications, offering promising opportunities for the future of this technology.

Passive RFID integrated acetone sensor and ammonia sensor based on LaFeO3/ZnO/rGO composites

In recent years, with the rapid development of information technology such as the Internet of Things, gas sensors, as gas signal collection devices, have played a vital role in environmental monitoring, industrial and agricultural production, food safety management, medical health diagnosis and air quality monitoring. In industrial production, acetone and ammonia are common chemicals that can leak or exceed safe concentrations, necessitating timely detection and alarm systems to ensure worker safety. Therefore, it is imperative to detect the content of acetone and ammonia, which are widely used and toxic gases. In this paper, a wireless gas sensor based on lanthanum ferrite (LaFeO3)/zinc oxide (ZnO)/reduced graphene oxide (rGO) was proposed and designed to detect acetone and ammonia concentrations at room temperature. The gas sensor with perforations is optimized using HFSS software, and the antenna is fabricated using an engraving machine. Subsequently, the nanocomposite material is coated onto the antenna to complete the gas sensor. The microstructure of lanthanum ferrite/zinc oxide/graphene is characterized and analyzed. Test results demonstrate that the sensor operates within a range of 300 ppm ∼ 1000 ppm for acetone concentration, exhibiting an amplitude change of 16.28 dB and a sensitivity of 0.023 dB/ppm. Furthermore, when operating stably within a range of 150 ppm ∼ 800 ppm for ammonia concentration, the sensitivity of the sensor reaches 0.328 MHz/ppm. Compared to traditional gas sensors, this gas sensor offers advantages such as high detection accuracy, minimal environmental interference factors, excellent selectivity, and prolonged lifespan; presenting promising prospects for detecting toxic gases in laboratories and chemical factories.

Wheatstone Bridge MEMS Hydrogen Sensor with ppb-Level Detection Limit Based on the Palladium-Gold Alloy.

On some occasions, such as new energy clinics, monitoring the trace hydrogen at the ppb level is necessary. The traditional resistive hydrogen sensors based on the Pd alloys are very difficult to realize such an extremely low detection limit. To achieve a detection limit at the ppb level and also ensure good stability, a MEMS hydrogen sensor was designed in a suspended Wheatstone bridge structure, with all four resistive arms defined on a sputtered Pd-Au alloy thin film. For the Wheatstone bridge sensor, absolute response (Ra) and relative response (Rs) are defined to describe the sensitivity of the sensor, and the effect of annealing temperature on baseline drift is investigated using the baseline zero drift parameter (DBZD). By testing the sensors across a hydrogen concentration range of 20 ppb to 3 v/v%, the optimal annealing temperature (250 °C) and operating temperature (60 °C) were identified. Under these conditions, the sensor exhibited a detection limit as low as 20 ppb with a power consumption of only 4.6 mW. At the same time, the response and recovery times of the sensor were 6 and 19 s, respectively, toward 3 v/v% hydrogen. After testing over a 100-day period, Ra fluctuated only 0.0026%, indicating that the hydrogen sensor had good long-term stability for low-concentration detection. More results also showed that the sensor has good repeatability, selectivity, and humidity resistance. With the wide measurement range (20 ppb to 3 v/v%), the sensor has the potential to meet hydrogen detection requirements in multiple scenarios.

Flexible Thermoelectric BiSbTe/Carbon Paper/BiSbTe Sandwiches for Bimode Temperature‐Pressure Sensors

AbstractBimode temperature‐pressure sensors hold significant promise in personal health monitoring, wearables and robotic signal detection. Traditional bimode sensors typically combine two independent sensors, leading to fabrication complexity. This study develops a bimode temperature‐pressure sensor by using a facile electrodeposition method to create sandwiched BiSbTe/Carbon Paper/BiSbTe thin films and stacking them to a vertical structure. It demonstrates high sensitivity for temperature sensing, capable of detecting temperature difference as low as 1 K, and a rapid response time of 0.92 s due to a vertical structure. Utilizing its thermoelectric mechanism, the sensor achieves self‐powered sensing for finger touch and respiration states. Furthermore, its island‐like contact surface ensures high sensitivity with an extremely fast response time of 0.17 s, by rapidly changing contact resistance under pressure, allowing it to detect various human behaviors, including body movements and micro‐expressions. Beyond its sensing capabilities, the film excels in flexibility, electromagnetic interference shielding, and stability, presenting significant potential for integration into self‐powered electronic skin systems for health monitoring, wearables, artificial intelligence, and other electronic skin applications.

High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring

Traditional sensors for mechanical equipment monitoring often face challenges such as high cost, difficult installation and removal, and reliance on power sources. Herein, we introduced a polyvinylidene fluoride (PVDF) nanofiber membrane doped with BaTiO3 for constructing a triboelectric acoustic sensor (PB-TAS), innovatively designed for monitoring and recognizing the working conditions of mechanical equipment. This represents the first application of a triboelectric acoustic sensor for mechanical equipment monitoring. The PB-TAS demonstrates an ultra-broad frequency response range of 20–20,000 Hz, effectively covering the characteristic frequency spectrum (within 2000 Hz) during piston pump operation. It also exhibits extraordinarily sensitivity of 4.40 V Pa−1 at 85 dB, and ultra-high frequency resolution down to 0.1 Hz, enabling precise matching of the piston pump's acoustic characteristic frequencies. Integrated with deep learning techniques, the PB-TAS achieves real-time recognition of 15 distinct working conditions of a piston pump, with an impressive accuracy of 95.2 %. The PB-TAS, boasting exceptional sensitivity and frequency resolution, offers a cost-effective, miniaturized, self-powered, non-contact, and highly accurate solution for intelligent monitoring of hydraulic piston pumps.

Ultra high sensitive long range surface plasmon resonance sensor for chemical and biochemical sensing

In this work, we proposed a long-range surface plasmon (LRSPR) sensor using a dielectric (Cytop) –Gold (Au)- graphene oxide (GO) layer to enhance the sensor’s detection accuracy (DA), figure of merit (FoM) and imaging sensitivity (Simag). The proposed sensor’s effectiveness for long-range is numerically demonstrated. The GO layer is used to protect the unwanted oxidation of Au and to improve the imaging sensitivity. By comparing its FoM and Simag to that of the traditional surface plasmon sensor (cSPR), the proposed sensor takes advantage of its high adsorption efficiency and high resolution. The Au-GO layer in the cSPR sensor and the cytop-Au-GO layer in the proposed sensor interfaces enhance the electric field intensity and penetration depth (PD). We attain the highest DA, FoM and Simag of 100/°, 1400/RIU, and 4183.9/RIU with 2000 nm of cytop thickness. For chemical and biological applications, the proposed LRSPR sensor design may be advantageous.

Passive wireless RFID strain sensor for directional-independent structural deformation monitoring

The advancement of Radio-frequency identification (RFID) sensor technology presents a novel approach to monitoring the health of structures. However, the traditional RFID antenna sensor encounters issues with limited sensitivity and strain measurement capabilities in a single direction. In this investigation, we propose, model, analyze, and test an enhanced wireless and passive RFID strain sensor that offers improved sensitivity to strains and quantifiable measurements in orthogonal directions. Theoretical analysis based on multiphysics elastodynamics and electromagnetics illustrating the resonant response and distribution of current field density between two sets of orthogonally installed sensors under applied stress. Our findings reveal that the RFID strain sensor exhibits characteristics to electromagnetically induced transparency like (EIT-like) effect, enabling independent and intensified measurement of strains both horizontally and vertically. Experimental data demonstrates a linear correlation between the shift in resonant frequency of the sensor antenna and transverse strains as well as longitudinal strains, with sensitivity coefficients measuring −1.76 kHz με−1 for transverse strain and 2.17 kHz με−1 for longitudinal strain respectively. Consequently, by accurately analyzing variations in resonant frequency it becomes possible to deduce both magnitude and direction of strains on the antenna effectively laying groundwork for future engineering applications.

Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis.

Impact loads affect the operational performance and safety life of rolling equipment's connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. First, the fast Fourier transform is used to obtain the frequency domain information for the motor's current response signal from the rotor system load recognition test. Consequently, the required load response information can be presented more clearly using the singular value decomposition method to remove the power frequency components in the current signal. Then, wavelet packet decomposition is performed on the signal to generate energy analysis feature vectors. A qualitative recognition of the impact load on the system is achieved by learning vector quantization neural networks; the resulting load recognition results are good. These findings indicate that using the motor current as the analysis signal can solve the problem of the difficult layout for traditional vibration sensors in rolling sites. The preprocessing and recognition method of the current response signal can recognize the impact load, confirming the applicability and feasibility of the proposed method.

Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing

The adoption of image processing-based technologies in the textile sector is rising. This technology is commonly utilized to replace traditional sensor systems that are limited to a single function while also improving product quality control functions. Defects during the manufacturing process are a common problem in the textile business, particularly with fabric products. This study created a fabric quality control system that detects fabric problems using machine learning-based picture classification techniques. A D320p web camera detects rare and slap flaws, which are classified using open-source Google teaching machine software and processed on a Raspberry Pi 3B device. The laboratory-scale measurement was carried out on a prototype cloth rolling machine using the confusion matrix method. The test results reveal an average inference speed of 143.5 milliseconds, a frame rate of 6.45 fps, and a 98.56% accuracy rate. These results demonstrate that the proposed system is effective and efficient for detecting fabric defects, offering a promising solution for enhancing quality control in the textile industry. Future research could focus on scaling the system for industrial use and enhancing real-time performance.

3D-Printed Multi-Axis Alignment Airgap Dielectric Layer for Flexible Capacitive Pressure Sensor.

Flexible pressure sensors are increasingly recognized for their potential use in wearable electronic devices, attributed to their sensitivity and broad pressure response range. Introducing surface microstructures can notably enhance sensitivity; however, the pressure response range remains constrained by the limited volume of the compressible structure. To overcome this limitation, this study implements an aligned airgap structure fabricated using 3D printing technology. This structure, designed with a precisely aligned triaxial airgap configuration, offers high deformability under pressure, substantially broadening the pressure response range and improving sensitivity. This study analyzes the key structural parameters-the number of axes and pore size-that influence the compressibility and stability of the dielectric material. The results indicate that the capacitive pressure sensor with an aligned airgap structure, manufactured via 3D printing, exhibits a wide operating pressure range (50 Pa to 500 kPa), rapid response time (100 ms), wide limit of detection (50 Pa), and approximately 21 times enhancement in sensitivity (~0.019 kPa-1 within 100 kPa) compared with conventional bulk structures. Furthermore, foot pressure monitoring trials for wearable sensor applications demonstrated exceptional performance, indicating the sensor's suitability as a wearable device for detecting plantar pressure. These findings advocate for the potential of 3D printing technology to supplant traditional sensor manufacturing processes.

A Three-Dimensional Surface-Adaptive Stretchable Sensor for Online Monitoring of Composite Materials Curing.

Recently, rigid sensors have been commonly applied to online monitoring of the core curing processes of composite materials to prevent both overcuring and under-curing. However, conventional rigid sensors are prone to causing cracks and bubbles in composite materials during the curing process, thereby affecting both the mechanical performance and the overall reliability of the materials. Herein, stretchable interdigital dielectric sensors with flexible substrates and electrodes are designed to conform to complex 3D surfaces, thus enabling embedded nondestructive monitoring of composite curing processes. The sensors obtained can endure 1000 cycles of bending from 0° to 180° and 1000 cycles of stretching at 30% strain while still conforming perfectly to complex 3D surfaces, thus overcoming the inability of traditional curing monitoring sensors to bend. Additionally, sensor integration with an electronic circuit enables real-time data collection and transmission, which makes the device more portable, compact, and lightweight. Moreover, after atmospheric exposure for 5 months, the unit sensitivity of the sensor decreased by only 0.1%, thus demonstrating its excellent reliability and stability. Furthermore, during curing monitoring of the complex three-dimensional surfaces of the Fendouzhe deep-sea submersible, the unit's sensitivity is close to that of conventional planar monitoring equipment, decreasing by only 0.4%. The proposed online nondestructive monitoring technology demonstrates high sensitivity, high monitoring accuracy, and high reliability during surface monitoring, thus enabling long-term curing monitoring under complex nonplanar conditions.