Sensor For On-line Monitoring Research Articles

An oil wear particle identification method based on Wasserstein generative adversarial network and improved CNN using a custom-built optical imaging sensor

The accurate classification of wear particles in lubricating oil holds significant importance for understanding the wear characteristics of lubricated components. However, there are prominent issues, such as blurred particle images, limited identification efficiency, and low generalization capabilities. To solve such issues, this research initially devises a novel online monitoring sensor to collect particle images based on the optical method. Subsequently, an improved Wasserstein Generative Adversarial Network (WGAN) is developed to ameliorate the categorization performance, which is hampered by data imbalance. Lastly, a MobileNetV2-SENet-based Convolutional Neural Network (MSCNN) is proposed to autonomously discover the associations between images and types of wear particles. Experimental results illustrate that our method achieves a recognition accuracy rate of 92.5%. The analysis outcomes between several comparative networks show that our approach can effectively improve recognition performance when limited samples are available. This work offers an effective solution for automatically identifying wear particles in oil and can be a powerful predictive maintenance tool for rotating mechanical equipment.

Large-Area Thickness Measurement of Transparent Films Based on a Multichannel Spectral Interference Sensor

Thickness measurement of thin films is essential for quality control in the manufacturing process of the semiconductor and display industries. Real-time monitoring of film thickness during production is an urgent technical problem to be solved. In this study, a method for large-area thickness measurement of transparent films based on a multichannel spectral interference sensor is proposed. The sensor simultaneously acquires multichannel spectral interference signals through a combination of fan-out fiber optic bundles, detection probes, and an imaging spectrometer. The spectral data are calibrated and transformed into the wavenumber dimension, and then the power spectral density estimation method is used to demodulate the data frequency to swiftly derive the film thickness. The thickness measurement capacity of the proposed system is successfully validated on two standard film samples with a relative deviation of less than 0.38% and a relative standard deviation of less than 0.044%. The total spectral acquisition and calculation time for a single multichannel measurement was approximately 7.5 ms. The experimental results on polyimide films show that the measurement efficiency of the system is at least 4 times higher than that of the traditional system, indicating the potential of the multichannel spectral interference sensor for online monitoring in film production.

Efficient Discrimination of Hazardous Organophosphate Flame Retardants via Cataluminescence-Based Multidimensional Ratiometric Sensing.

Emerging contaminants have recently evolved into a severe worldwide environmental issue. Organophosphate flame retardants (OPFRs) with neurotoxicity, genotoxicity, and reproductive and developmental toxicity are a class of notorious emerging contaminants that cause great concern. The development of high-efficiency and portable sensors for rapid online monitoring of OPFRs has become the primary demand for the exploration of the environmental migration and transformation of OPFRs. In this work, interestingly, the cataluminescence (CTL) phenomenon of OPFRs is first observed, and an ingenious multidimensional ratiometric CTL sensing strategy is developed for the recognition of multiple OPFRs. Three characteristic ratios are extracted from the multipeak CTL spectral curves based on energy transfer of single Tb/Eu-modified MgO sensing material, and thus a novel three-dimensional (3D) code recognition could be mapped out. This obtained 3D coordinate is found to be a unique characteristic for a given OPFR, just like an exclusive person's ID number, which can successfully discriminate and detect 10 kinds of OPFR vapors, including homologous series and isomers. More importantly, CTL mechanism investigations for OPFRs demonstrate that OPFRs undergo a series of chemical reaction processes, e.g., oxidative pyrolysis and hydroxylation, and different high-energy excited intermediates are generated, which trigger discrepant energy-transfer efficiency toward rare earth ions, leading to multipeak spectral profiles. Briefly, this proposed CTL analytical platform for OPFRs recognition initiates a new sensing principle for the efficient identification of emerging contaminants and shows significant prospects on rapid on-site detection.

Use of near-infrared spectroscopy for the online monitoring of natural gas composition (hydrocarbons, water and CO2 content) at high pressure

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO2 removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO2 and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO2, methane/water and methane/CO2/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO2 content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO2 content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R2) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO2 and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

Rotary Wind-driven Triboelectric Nanogenerator for Self-Powered Airflow Temperature Monitoring of Industrial Equipment.

Heat dissipation performance is crucial for the operational reliability of industrial equipment, which can be monitored by detecting the wind or airflow temperature of the radiator. The emergence of triboelectric nanogenerators (TENGs) provides new routes for wind energy harvesting and self-powered sensing. Herein, a rotary wind-driven triboelectric nanogenerator (RW-TENG) with soft-contact working mode is newly designed to achieve tunable contact areas by utilizing the reliable thermal response of NiTi shape memory alloy (SMA) to air/wind temperature. The RW-TENG can generate different triboelectric outputs under air stimulation with different speeds or temperatures, which is demonstrated as a power source for online monitoring sensors, self-powered wind speed sensing, and airflow temperature monitoring. Specifically, a self-powered sensor of wind speed is demonstrated with a sensitivity of 0.526µAm-1 s between 2.2 and 19.6 m s-1, and a self-powered monitoring device of high airflow temperature, which show relatively short response time (109s), strong anti-interference ability and outstanding long-term durability. This study introduces an innovative route for real-time detection of airflow temperature in wind-cooled industrial equipment, showing broad application prospects for information perception and intelligent sensing of the industrial IoTs.

A Microwave Sensor for Real-Time Online Monitoring and Measuring the Blade Tip Clearance of Aero-Engine

A Microwave Sensor for Real-Time Online Monitoring and Measuring the Blade Tip Clearance of Aero-Engine

A highly sensitive ion-selective chemiresistive sensor for online monitoring of lead ions in water.

Dissolved lead is a serious but common health hazard in drinking water, yet there is still no practical way to monitor its levels continuously in the distribution system or at the point of use. Here we propose using a lead-selective membrane on top of a chemiresistive device to continuously measure Pb2+ ion concentrations in real time. The detection limit was lowered by stabilizing the surface of the resistive film with sodium hydroxide and 15-crown-5 ether and optimizing the sensor geometry to maximize the effective surface area. The detection mechanism is based on the complexation of the Pb2+ ions by the lead ionophores within the membrane, thus modulating the interactions between the ionophores and the chemiresistive film. The limit of detection of the fabricated devices was reliably below 2 μg L-1, with concentrations up to 3 mg L-1 routinely quantifiable over several measurement cycles. The chemiresistive sensors can thus achieve lower detection limits than potentiometric devices while being more robust and simpler to fabricate by omitting the reference electrode. Ion-selective membrane-covered chemiresistors can therefore be deployed to continuously monitor drinking water sources and detect harmful levels of lead in real time.

A Sensor for Real-Time Online Monitoring of the Rotor Axial Displacement of Aeroengine Based on Microwave Coaxial Resonator

A Sensor for Real-Time Online Monitoring of the Rotor Axial Displacement of Aeroengine Based on Microwave Coaxial Resonator

Favoring the Selective H2O2 Generation of a Self-Antibiofouling Dissolved Oxygen Sensor for Real-Time Online Monitoring via Surface-Engineered N-Doped Reduced Graphene Oxide.

Dissolved oxygen (DO) is a key parameter in assessing water quality, particularly in aquatic ecosystems. The oxygen reduction reaction (ORR) has notable prevalence in energy conversion and biological processes, including biosensing. Nevertheless, the long-term usage of the submersible DO sensors leads to undesirable biofilm formation on the electrode surface, deteriorating their sensitivity and stability. Recently, the reactive oxygen species (ROS), such as the two-electron pathway ORR byproduct, H2O2, had been known for its biofilm-degradation activity. Herein, for the first time, we reported N-doped reduced graphene oxide (N-rGO) for H2O2 selectivity as the self-antibiofouling DO sensor. Introducing foreign atom doping could reorient the electron network of graphene by the electronegativity gap, which facilitated highly selective and efficient two electron pathway of ORR. Mitigating the N content of N-rGO had enhanced the H2O2 selectivity (57.5%) and electron transfer number (n = 2.84) in neutral medium. Moreover, the N-rGO could be integrated to a wireless DO monitoring device that might realize an applicable device in the aquatic fish farming.

Infrared spectroscopy for ageing assessment of insulating oils via dielectric loss factor and interfacial tension

Abstract Along with the increase in demand for electricity, dependability requirements for the power supply system also increase. Transformers are the backbone of the electrical system; thus, they must function reliably and safely. In this study, a method based on Fourier-transform infrared spectroscopy technology is proposed for assessing the ageing level of insulating oils. Copper-catalysed thermal ageing is applied to four distinct insulating oils at a temperature of 12 0 ° C 12{0}^{^\circ }{\rm{C}} . The insulating oil ageing peaks are promptly measured using infrared spectroscopy, and the peak area ratios are associated with the interfacial tension and dielectric loss factor. It is observed that the peak area ratio of the ageing peaks and interfacial tension are significantly correlated, and this relationship can be used to determine the ageing condition of the insulating oil. The findings of this study can be used to design sensors for online monitoring of insulating oil quality.

Assessment of Oil Chromatography Sensor Failure for Transformer Based on Multi-dimensional Data-driven

Online monitoring and analysis of transformer oil chromatography is of great significance for the assessment of internal insulation with transformer operation. The existence of numerous invalid monitoring sensors in the field makes the quality of online monitoring data degraded, which makes the online monitoring system unable to monitor the operating status of transformers in a timely and accurate manner. This paper propose a data-driven method for evaluating the effectiveness of oil chromatography monitoring sensors by selecting a fixed-length feature data set from online data for evaluation, and then using the distribution of abnormal values, the distribution of consecutive identical values, the variation of coefficient of variation and the variation of gas production rate in the feature data set as criteria to judge the feature data set and obtain the corresponding discriminant values. Afterwards, the discriminant values are assigned weights according to the focus of each criterion to obtain the sensor status values, which are compared with the preset tolerance level to obtain the sensor evaluation results. The method proposed evaluates the effectiveness of online monitoring sensors from several aspects, which can detect faulty sensors in time and is of great significance for improving the accuracy of online monitoring data and the reliability of monitoring device.

Integrating hybrid piezoelectric actuators with a single fiber Bragg grating sensor for online monitoring of structures

The application of fiber Bragg grating (FBG) sensors for detecting ultrasonic guided waves (GW) in a hybrid system with piezoelectric transducers (PZT) is a promising area for structural integrity analysis due to FBG’s potential for embedding in structures. However, the directional sensitivity of FBG sensors presents a challenge for signal processing and the direct application of damage mapping methods designed for omnidirectional PZT sensors is not feasible. The novelty of this study lies in the development of a proof-of-study damage mapping method that uses an improved cosine distance (CD) formulation to detect damage paths while accounting for the directional sensitivity of FBG sensors. Additionally, a sectorial elliptical code (SEC) based on CD was used to localize damages in a hybrid FBG-PZT network. This approach was validated with numerical simulations, demonstrating its effectiveness in accurately detecting and localizing damages in structures. The damage detection with the CD method located all the damage paths which help to identify the probable damage region. The SEC method localized damages not only in the inner sensor zone but also in the outer zone. The CD method successfully predicted the probable damage zones overcoming the directional sensitivity issue. The error predicted with the SEC method was as low as 0.7 cm in the experimental and 0.1 cm in numerical studies.

Tailored Ti3C2 MXene/SnS2 nanocomposites to realize both sensitive photoelectrochemical determination and efficient photocatalytic detoxification of Cr(VI)

We proposed a one-pot hydrothermal method for the synthesis of Ti3C2 MXene/SnS2 nanocomposites series and explored the possibility of utilizing them for sensitive photoelectrochemical (PEC) determination of Cr(VI) followed by efficient photocatalytic detoxification. Our research demonstrated that the resultant Ti3C2 MXene/SnS2 nanocomposites with 0.4 wt% of Ti3C2 MXene (labeled as Ti3C2 MXene0.4%/SnS2) possessed the most excellent PEC performance than the monomers (Ti3C2 MXene and SnS2 nanoparticles) and other Ti3C2 MXene/SnS2 nanocomposites with Ti3C2 MXene content of 0.2 wt%, 0.8 wt%, or 1.4 wt%. The developed PEC sensor based on Ti3C2 MXene0.4%/SnS2 nanocomposites showed a good linear response to Cr(VI) in the concentration range of 1.0 pM∼0.1 mM with a low limit of detection of 0.51 pM (S/N = 3). And the Ti3C2 MXene0.4%/SnS2 nanocomposites exhibited the best photocatalytic detoxification efficiency for Cr(VI) after exposure to simulated sunlight for 1 h, reaching as high as 90.2%, whereas 56.9% and 51.7% detoxification efficiency was obtained for pure SnS2 and Ti3C2 MXene, respectively. Our study provides a new material having broad application prospects that are capable of scaled-up for developing online monitoring sensors for Cr(VI) determination as well as the photocatalytic reduction reactor for detoxification of Cr(VI) in various water matrices.

Portable TDLAS Sensor for Online Monitoring of CO2 and H2O Using a Miniaturized Multi-Pass Cell.

We designed a tunable diode laser absorption spectroscopy (TDLAS) sensor for the online monitoring of CO2 and H2O concentrations. It comprised a small self-design multi-pass cell, home-made laser drive circuits, and a data acquisition circuit. The optical and electrical parts and the gas circuit were integrated into a portable carrying case (height = 134 mm, length = 388 mm, and width = 290 mm). A TDLAS drive module (size: 90 mm × 45 mm) was designed to realize the function of laser current and temperature control with a temperature control accuracy of ±1.4 mK and a current control accuracy of ±0.5 μA, and signal acquisition and demodulation. The weight and power consumption of the TDLAS system were only 5 kg and 10 W, respectively. Distributed feedback lasers (2004 nm and 1392 nm) were employed to target CO2 and H2O absorption lines, respectively. According to Allan analysis, the detection limits of CO2 and H2O were 0.13 ppm and 3.7 ppm at an average time of 18 s and 35 s, respectively. The system response time was approximately 10 s. Sensor performance was verified by measuring atmospheric CO2 and H2O concentrations for 240 h. Experimental results were compared with those obtained using a commercial instrument LI-7500, which uses non-dispersive infrared technology. Measurements of the developed gas analyzer were in good agreement with those of the commercial instrument, and its accuracy was comparable. Therefore, the TDLAS sensor has strong application prospects in atmospheric CO2 and H2O concentration detection and ecological soil flux monitoring.

A Novel Energy Harvesting Method for Online Monitoring Sensors in HVdc Overhead Line

Due to the lack of alternating electric fields and alternating magnetic fields in HVdc transmission system, there has been no sustainable and stable energy harvesting method compatible with them up to now, hindering the application of HVdc sensors. In this letter, we proposed a novel dc energy harvesting method using corona current, and an energy harvesting circuit is constituted with space ion flow generated by dc corona. The feasibility of the proposed method is increased by adding an artificial corona electrode and placing the load before the corona electrode. In addition, it can flexibly operate at any position in an HVdc transmission system according to actual demand. Then the output power is further maximized by adopting a capacitor with high working voltages. The detailed measurement results based on the experimental system validate the effectiveness of the proposed method, with a stable output power as high as 10.15 mW in a dc system of 22 kV. A detailed discussion of the overall performance is finally carried out.

Application of Condition Monitoring for Hydraulic Oil Using Tuning Fork Sensor: A Study Case on Hydraulic System of Earth Moving Machinery.

In this study, we focus on the correctness of oil condition monitoring, specifically of a tuning forks sensor in hydraulic systems. We also aim to analyze the correlation between the online monitoring sensor signal and offline oil analysis by periodically sampling the hydraulic oil. In recent years, condition-based monitoring (CBM) of hydraulic oils has played a key role in extending earthmoving machinery uptime and reducing maintenance costs. We performed rig test and field test to develop a condition monitoring system based on oil analysis for construction equipment. Using the rig test, a reference line for the diagnosis of viscosity and dielectric constant for the new hydraulic oil was derived, and the characteristics of each sensor parameter for artificial contamination and oxidation were confirmed. In order to affirm the validity of oil diagnosis using oil sensors, the oil sensors were applied to four excavators to detect changes in oil conditions over 12 months. It was found that monitoring hydraulic oil with an oil sensor detecting the change in oil properties and contamination can provide reliable information for establishing diagnostic criteria. The finding allows us to predict the remaining oil life and to determine the oil change intervals based on the diagnosis of the oil condition.

Self-powered sensing of power transmission lines galloping based on piezoelectric energy harvesting

Online monitoring sensors are a feasible solution for conductor galloping that greatly harms the stable operation of power transmission systems. However, power supply that drives sensors has become one of the bottlenecks restricting the development of distributed sensing systems. This work initially proposes harvesting the energy of conductor galloping and joint utilization to assess conductor galloping degree. A swinging piezoelectric energy harvester based on frequency boost conversion is also proposed. The output characteristics of harvester and the physical validation of the scale model of power transmission line galloping are further explored. Experiment results showed a maximum output voltage and current of 29.6 V and 29 µA, respectively, under the characteristic conditions of conductor galloping. The corresponding load capacity of the harvester reached a maximum power output of 155.58 µW under minimum resistance of 70 kΩ at 35 cm vibration amplitude and 1.3 Hz frequency. The conductor galloping testing platform indicated that the frequency boost conversion effect was weakened due to the occurrence of torsion movement during conductor galloping, and the output presented a nonlinear variation with the vibration amplitude and frequency. The degree and direction of conductor galloping can be preliminarily judged according to the output trend, and the maximum output power of load capacity of the harvester reached 101.5 μW under 107 MΩ resistance at 1.3 Hz vibration frequency, validating that the proposed energy harvesting system is promising for self-powered sensing applications in low-power monitoring sensors for conductor galloping.© 2017 Elsevier Inc. All rights reserved.

Fiber Bragg grating sensor for in situ substrate temperature measurement in a magnetron sputtering system

Fiber Bragg grating sensors are used to monitor physical parameters like temperature even in inaccessible, unconventional environments. One such application is monitoring the temperature of a substrate from the back surface like any conventional thermocouple sensor and simultaneously monitoring the temperature on the front surface, which is in contact with ion plasma in a magnetron sputtering system. Since thin film parameters depend on substrate temperature, precise measurement plays an important role and thermocouple-like sensors are erroneous when placed on the front side of a substrate exposed to ion plasma. The lateral variation of temperature on the front side for larger substrates also has been established at four different locations, exploiting the multiplexing capability of FBGs. In this report, we demonstrate and utilise the versatility and capability of fiber Bragg grating sensors for online monitoring of substrate temperatures during thin film deposition, for the first time to the best of our knowledge, on different types of substrates. FBG based approach is expected to significantly aid in better control of thin film devices.

An IoT-based intelligent irrigation system with data fusion and a self-powered wide-area network

Water resources have a great influence on human society, but saving water in irrigation still remains a challenge. This article proposes an intelligent irrigation system that integrates a data fusion model and a long-rang (LoRa) network for optimizing the watering schedule. A data fusion model is proposed, which first adopts the long short-term memory (LSTM) network to simulate and predict the proper watering demands by integrating multi-source heterogeneous data, that is, historical weather data, user irrigation logs, weather forecasts, and online monitoring sensor data. A self-powered wide-area network is implemented and deployed by using LoRa to facilitate multiple Internet of Things (IoT) application scenarios. It includes a gateway and two types of nodes: a valve node and a sensing node. The node is capable of energy autonomy through the scheme of waterflow-based power generation, thus realizing maintenance-free throughout the life cycle. A cloud platform is designed to provide network management, intelligent irrigation control, and the interface of the mobile application. The proposed system is evaluated through a case study of landscape watering. On average, the proposed system achieves a water-saving efficiency of 94.74% compared with the conventional manual setting solutions.

Reflective fiber-optic sensor for on-line nondestructive monitoring of Aspergillus on the surface of cultural paper relics.

A reflective fiber-optic sensor was created to realize on-line nondestructive monitoring of the growth process of Aspergillus on the surface of cultural paper relics. The sensor consisted of one tapered input and six output optical fibers. The operating principle of the device was established. The sensitivity of the sensor was checked. Sensors were used to monitor the growth of Aspergillus niger, Aspergillus flavus, and Aspergillus tamarrii on the papers. The morphology of Aspergillus was characterized. The sensor reveals a linear relationship between the output signal of the sensor and the thickness of Aspergillus biofilm with a detection limit of 10 µm.