Sensor Concept Research Articles

Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearings Faults in Induction Motors.

The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed in which sensors are the key to providing relevant information to verify the system. Recently, the smart sensor concept is common, in which the sensors are integrated with a data processing unit executing dedicated algorithms used to generate meaningful information about the system in situ. Additionally, infrared thermography has gained relevance in monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing, the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way in industrial applications. Therefore, in this manuscript, the structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: an infrared primary sensor, a preprocessing module, an image processing module, classification of faults, and a user interface. The infrared primary sensor considers a low-cost micro thermal camera for acquiring the thermal images. The processing modules and the classification module implement the data processing algorithms into digital development boards, enabling smart system characteristics. Finally, the interface module allows the final users to require the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be provided by the system. The smart sensor is validated in a real experimental test bench, demonstrating its capabilities in different case studies.

Tetrazolium-Based Visually Indicating Bacteria Sensor for Colorimetric Detection of Point of Contamination.

Protective equipment for detecting bacterial contamination has been in high demand with increasing interest in public health and hygiene. Herein, a fiber-based visually indicating bacteria sensor (VIBS) embedded with iodonitrotetrazolium chloride is developed for the general purpose of detecting live bacteria, and its chromogenic effectiveness is investigated for Gram-negative Escherichia coli and Gram-positive Micrococcus luteus. The developed color intensity is measured by the light absorption coefficient to the scattering coefficient (K/S) based on the Kubelka-Munk equation, and the colorimetric sensitivities of different membranes are examined by calculating the limit of detection (LOD) and the limit of quantification (LOQ). The results demonstrate that the interactions between VIBS and bacteria depend on the wetting properties of membranes. A hydrophobic membrane shows excessive interactions at high concentrations of Gram-negative E. coli bacteria, whose cell membrane is lipophilic. The membrane blended with hydrophobic and hydrophilic polymers displays linear colorimetric responses for both Gram-negative and Gram-positive bacteria strains, demonstrating a reliable sensing capability in the range of the tested bacteria concentration. This study is significant in that explorative experimentations are performed to conceive a proof of concept of a fiber-based bacteria sensor, which is readily applicable in various fields where bacteria pose a threat.

The Silicon Electron Multiplier sensor

The Silicon Electron Multiplier (SiEM) is a novel sensor concept for minimum ionising particle (MIP) detection which uses internal gain and fine pitch to achieve excellent temporal and spatial resolution. In contrast to sensors where the gain region is induced by doping (LGADs, APDs, SPADs), amplification in the SiEM is achieved by applying an electric potential difference in a composite electrode structure embedded within the silicon bulk using MEMS fabrication techniques. Since no gain-layer deactivation is expected with radiation damage, such a structure is expected to withstand fluences of up to 1016 neq/cm2. Various geometries and biasing configurations are studied, within the boundaries imposed by the fabrication process being considered. The effective gain, the field in the sensor, the leakage current and breakdown conditions are studied for cell sizes in the range of 6–15µm. Simulations show that gains in excess of 10 can be achieved, and studies of the time structure of the induced signals from a charge cloud deposited in the middle of the sensor show that time resolutions similar to other sensors with internal gain can be expected. Plans for the manufacture of a proof-of-concept sensor and for its subsequent characterisation are discussed.

Wearable Biosensor Standardization: How to Make Them Smarter

The availability of low-cost plug-and-play devices may contribute to the diffusion of methods and technologies for the personalized monitoring of physiological parameters by wearable devices. This paper is focused on biosensors, which represent an interesting enabling technology for the real-time continuous acquisition of biological or chemical analytes of physio-pathological interest, e.g., metabolites, protein biomarkers, and electrolytes in biofluids. Currently available commercial biosensors are usually referred to as customized and proprietary solutions. However, the efficient and robust development of e-health applications based on wearable biosensors can be eased from device interoperability. In this way, even if the different modules belong to different manufacturers, they can be added, upgraded, changed or removed without affecting the whole data acquisition system. A great effort in this direction has already been made by the ISO/IEC/IEEE 21451 standard that introduces the concept of smart sensors by defining the main and essential characteristics that these devices should have. Following the guidelines provided by this standard, here we propose a set of characteristics that should be considered in the development of a smart biosensor and how they could be integrated into the existing standard.

Building and Transforming to Achieve Social and Environmental Compliance in Global Value Chains

Multinational corporations (MNC) are under increasing pressure to improve social and environmental standards in their global value chains (GVC). “Cascading compliance,” the process by which MNCs distribute codes of conduct and request their suppliers to “cascade” those standards to their suppliers, has become the primary method of achieving improving compliance. Yet, even firms strongly motivated to improve compliance may be exposed to supplier compliance shortcomings, and we find that these MNCs have little guidance on how to improve their compliance systems. We build a dynamic capability framework to conceptualize higher-order routines MNCs need to orchestrate their GVC social and environmental compliance. We establish three key MNC roles—the internal prioritizer, external developer, and network transformer—that align with Teece’s (2007) concepts of sensor, seizer, and transformer, and we highlight four crucial capabilities associated with each role by drawing upon prior research in operations, sustainability, and supply chain management. Our framework has significant practical value for both scholars and managers as it delineates between levels of analysis as well as temporal considerations.

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

Innovative Online Measurement and Modelling Approach for Property-Controlled Flow Forming Processes

The production of complex multi-functional, high-strength parts is becoming increasingly important in the industry. Especially with small batch size, the incremental flow forming processes can be advantageous. The production of parts with complex geometry and locally graded material properties currently depicts a great challenge in the flow forming process. At this point, the usage of closed-loop control for the shape and properties could be a feasible new solution. The overall aim in this project is to establish an intelligent closed-loop control system for the wall thickness as well as the α’-martensite content of AISI 304L-workpieces in a flow forming process. To reach this goal, a novel sensor concept for online measurements of the wall thickness reduction and the martensite content during forming process is proposed. It includes the setup of a modified flow forming machine and the integration of the sensor system in the machine control. Additionally, a simulation model for the flow forming process is presented which describes the forming process with regard to the plastic workpiece deformation, the induced α’-martensite fraction, and the sensor behavior. This model was used for designing a closed-loop process control of the wall thickness reduction that was subsequently realized at the real plant including online measured feedback from the sensor system.

First results for the pLGAD sensor for low-penetrating particles

Silicon sensors are the go-to technology for high-precision sensors in particle physics. But only recently low-noise silicon sensors with internal amplification became available. The so-called Low Gain Avalanche Detector (LGAD) sensors have been developed for applications in High Energy Physics, but lack two characteristics needed for the measurement of low-energy protons (<60keV): a thin entrance window (in the order of tens of nm) and the efficient amplification of signals created near the sensor’s surface (in a depth below 1µm).In this paper we present the so-called proton Low Gain Avalanche Detector (pLGAD) sensor concept and some results from characterisation of the first prototypes of the sensor. The pLGAD is specifically designed to detect low-energy protons, and other low-penetrating particles. It will have a higher detection efficiency than non-silicon technologies, and promises to be a lot cheaper and easier to operate than competing silicon technologies.

Feasibility study of measuring instantaneous angular speed of helical gears with magnetoresistive sensors

The digitalization of industrial processes with e.g. the goal to increase the availability of production processes in general and to support the individualization of many products in particular leads to an increasing demand in sensor data. The state of the art for condition monitoring of involute gear trains is the measurement of structure-borne vibration with acceleration sensors. New approaches, such as measuring the instantaneous angular speed (IAS), are gaining in popularity. Machine manufacturers usually wish to use existing sensors or measurement points, where little or no effort is needed to implement the sensor concept. Magnetoresistive (MR) sensors fulfil this complex set of requirements to a high degree: They are comparatively easy to integrate, can be added as an optional component and provide sufficient accuracy.The authors have developed different sensor concepts using MR sensors for measuring IAS in a 1-stage helical gear box. Multiple tests with artificial tooth flank damages have been carried out to evaluate the damage detection potential of the sensor concepts. Finally, a spectrum analysis of the first gear mesh frequency and surrounding sidebands demonstrates the capability for detecting tooth flank damage with different MR sensor concepts.

Estimation of Leaf Area Index with a Multi-Channel Spectral Micro-Sensor for Wireless Sensing Networks.

The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies.

Tightly Coupled Multimodal Sensor Data Fusion for Robust State Observation With Online Delay Estimation and Compensation

Ego-vehicle state estimation can be achieved by utilizing classic vehicle and environmental sensors. However, each type of sensor has specific strengths and limitations regarding accuracy and robustness. Using existing sensor concept cannot fulfill the requirements for both accuracy and robustness against changing vehicle parameters and environments while driving. Therefore, we propose a framework to exploit the advantages of each individual sensor type seeking high accuracy and robustness. The uncertainty of state variables is estimated by the fusion of an inertial measurement unit, a global navigation satellite system, a radar sensor, and a lidar sensor. The multimodal sensor data are processed in a distributed manner in each sensor model at a low level, and the physical quantities from the sensor models are fused centrally by a probabilistic method, namely, the synchronized error-state extended Kalman filter, at a high level. We propose an event-based approach to estimate sensor system delay and compensate lagged signals using forward prediction fusion. The concept was implemented in a test vehicle and evaluated in field tests for dynamic driving and on public roads. The algorithm represents real-time estimation with high accuracy for different driving maneuvers and robustness against different disturbances caused by the environment and changing chassis parameters.

Waterproof Design of Soft Multi-Directional Force Sensor for Underwater Robotic Applications

Directional force sensing is an intrinsic feature of tactile sensing. As technologies of exploratory robots evolve, with special emphasis on the emergence of soft robotics, it is crucial to equip robotic end-effectors with effective means of characterizing trends in force detection and grasping phenomena, while these trends are largely derived from networks of tactile sensors working together, individual sensors must be built to meet an intended function and maintain functionality with respect to environmental operating conditions. The harshness of underwater exploration imposes a unique set of circumstances onto the design of tactile sensors. When exposed to underwater conditions a tactile sensor must be able to withstand the effects of increased pressure paired with water intrusion while maintaining computational and mechanical integrity. Robotic systems designed for the underwater environment often become expensive and cumbersome. This paper presents the design, fabrication, and performance of a low-cost, soft-material sensor capable of multi-directional force detection. The fundamental design consists of four piezo-resistive flex elements offset at 90∘ increments and encased inside of a hemispherical silicone membrane filled with a non-compressive and non-conductive fluid. The sensor is simulated numerically to characterize soft-material deformation and is experimentally interrogated with indentation equipment to investigate sensor-data patterns when subject to different contact forces. Furthermore, the sensor is subject to a cyclic loading test to analyze the effects of hysteresis in the silicone and is submerged underwater for a 7-day period to investigate any effect of water intrusion at a shallow depth. The outcome of this paper is the proposed design of a waterproofed, soft-material tactile sensor capable of directional force detection and contact force localization. The overall goal is to widen the scope of tactile sensor concepts outfitted for the underwater environment.

Stretchable printed device for the simultaneous sensing of temperature and strain validated in a mouse wound healing model

Temperature and strain are two vital parameters that play a significant role in wound diagnosis and healing. As periodic temperature measurements with a custom thermometer or strain measurements with conventional metallic gauges became less feasible for the modern competent health monitoring, individual temperature and strain measurement modalities incorporated into wearables and patches were developed. The proposed research in the article shows the development of a single sensor solution which can simultaneously measure both the above mentioned parameters. This work integrates a thermoelectric principle based temperature measurement approach into wearables, ensuring flexibility and bendability properties without affecting its thermo-generated voltage. The modified thermoelectric material helped to achieve stretchability of the sensor, thanks to its superior mechano-transduction properties. Moreover, the stretch-induced resistance changes become an additional marker for strain measurements so that both the parameters can be measured with the same sensor. Due to the independent measurement parameters (open circuit voltage and sensor resistance), the sensing model is greatly attractive for measurements without cross-sensitivity. The highly resilient temperature and strain sensor show excellent linearity, repeatability and good sensitivity. Besides, due to the compatibility of the fabrication scheme to low-temperature processing of the flexible materials and to mass volume production, printed fabrication methodologies were adopted to realize the sensor. This promises low-cost production and a disposable nature (single use) of the sensor patch. For the first time, this innovative temperature-strain dual parameter sensor concept has been tested on mice wounds in vivo. The preliminary experiments on mice wounds offer prospects for developing smart, i.e. sensorized, wound dressings for clinical applications.

A Resonant Graphene NEMS Vibrometer.

Measuring vibrations is essential to ensuring building structural safety and machine stability. Predictive maintenance is a central internet of things (IoT) application within the new industrial revolution, where sustainability and performance increase over time are going to be paramount. To reduce the footprint and cost of vibration sensors while improving their performance, new sensor concepts are needed. Here, double-layer graphene membranes are utilized with a suspended silicon proof demonstrating their operation as resonant vibration sensors that show outstanding performance for a given footprint and proof mass. The unveiled sensing effect is based on resonant transduction and has important implications for experimental studies involving thin nano and micro mechanical resonators that are excited by an external shaker.

Research Progress on Enzyme-free Blood Glucose Sensor

Blood glucose monitoring is of great significance to diabetic patients, and the development of rapid, accurate and real-time glucose detection technology has become a research hotspot nowadays. This study introduces the concept and classification of the enzyme-free glucose sensor, expounds enzymefree glucose sensor electrode characterization methods and the application progress of different materials in enzyme-free blood glucose sensors. Meanwhile, some problems of enzyme-free glucose sensor existing in the current research and its future application prospects also will be discussed.

Design and Development of Inductive Sensor Mini Trainer Based on Arduino

Sensor and Transducer is one of the subjects that must be followed by students of the Automotive Engineering Department, Faculty of Engineering, Padang State University, which weighs 2 credits. In accordance with the RPS for the Sensor and Transducer Course, there are several types of sensors that are the subject of study, one of which is an inductive sensor. Inductive sensor applications are applied in the automotive world such as Crankshaft Position Sensor (CKP), Camshaft Position Sensor (CMP), Vilocity Sensor. To make it easier to understand each sensor applied in the automotive world, students are required to master the basic concepts of the inductive sensor. One of the ways to increase student competence in the field of inductive sensors is the use of teaching aids during the learning process. Inductive sensor learning media in the Laboratory at the Automotive Engineering Department still uses analog processors, to support the development of digital technology today, so the authors provide an alternative by making teaching aids in the form of an Arduino-based Inductive Sensor Mini Trainer. This study uses the Research and Development (R&amp;D) method which consists of Define, Design, Development, and Desseminate. The assembled inductive sensor mini trainer was tested for accuracy, and repeatibility test as well as feasibility test from media experts and users. The results of the research on the product trials carried out, obtained an accuracy level of 96.21%, the sensor output has linearity, the repeatibility test is obtained with an average of 0.1%. The level of product feasibility from Media Experts, is 94.68% and 96.23% from Respondents. So it can be concluded that the Inductive Sensor Mini Trainer is very suitable to be used as a learning medium.

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion.

Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.

Calibration and Validation of Micromagnetic Data for Non-Destructive Analysis of Near-Surface Properties after Hard Turning

Abstract Micromagnetic non-destructive (NDT) methods offer a great potential for the analysis of near-surface properties after machining due to potential time and cost reduction as well as the option to be implemented into the machining process. As a result, the development of soft sensor concepts including micromagnetic NDT methods is in focus of current investigations in order to eventually improve the surface integrity of machined components and, thus, service life and reliability. However, a preceding calibration based on empirical data as well as a reliable validation is often referred to as one of the main challenges of micromagnetic NDT methods. The present study provides insights into the calibration and validation of a micromagnetic 3MA-II system for NDT analysis of the near-surface properties, with a focus on the residual stress depth profiles after hard turning of 51CrV4 specimens. Different calibration functions as well as a combination of different NDT methods are taken into consideration. The results and the potential of the 3MA system as well as open challenges are critically discussed.

Individual functionalization of fiber-reinforced profiles via pultrusion

Profile-based design is widely used because of its flexibility. A good possibility to increase the lightweight aspect can be the use of fiber reinforced profiles. These can be produced in a series suitable process - pultrusion. In this paper, the integration of functions in pultrusion profiles is focused. In one project, potential sensor concepts for robust condition monitoring of fiber reinforced plastic (FRP) profiles were developed to expand the range of applications significantly. One application could be the substitution of complex aluminum profiles by fiber reinforced profiles with the advantage like high lightweight potential and low thermal expansion. Another objective was the integration of sensors and light-emitting diodes (LED) directly into the pultrusion profile. The objective of a second research project was the integration, wiring and test of sensors into a curved roller ski profile to detect loads during training. Several types of sensors were investigated to evaluate the parameters accuracy, positioning, and wiring at the best economic efficiency. The focus of the research and development of both projects was the integration of functions into pultruded profiles made of FRP. Successfully integrated functionalizing elements include sensors for strain, lighting elements, and touch sensors as well as temperature and humidity modules. The objective of the research project “SmartSensSki” was the integration, wiring and test of sensors into a curved roller ski profile to detect loads during training. Several types of sensors were investigated to evaluate the parameters accuracy, positioning, and wiring at the best economic efficiency. The focus of the research and development of both projects was the integration of functions into pultruded profiles made of FRP. Successfully integrated functionalizing elements include sensors for strain, lighting elements, and touch sensors as well as temperature and humidity modules.

Surface Plasmon Resonance Sensor Based on Double-Sided Polished Microstructured Optical Fiber Coated With Graphene-on-Silver Layers

We propose a concept of surface plasmon resonance sensor based on double-sided polished microstructured optical fiber coated with graphene-on-silver layers. The silver films on the polishing planes are covered with multilayer graphene which can prevent the oxidation of silver and enhance the adsorption of biomolecules. The sensing performance of the proposed sensors with polishing planes angle of 0&#x00B0;, 60&#x00B0; and 120&#x00B0;, are investigated by using both wavelength and amplitude interrogations. Our numerical results demonstrate that increasing the size of the air hole in the core area can effectively improve the wavelength and maximum amplitude sensitivities. Moreover, growing number layer of graphene can increase wavelength sensitivity, whereas decrease the maximum amplitude sensitivity.