Design and analysis of singal conditioning circuit for vibration sensor

A piezoelectric sensor charge/voltage conversion circuit is designed based on the principle that piezoelectric sensor can convert the vibration or shock acceleration into the charge proportionally.Effect of temperature characteristic of feedback capacitor on the switching circuit output is analyzed based on the acquisition and measurement system in this paper.The characteristics of different filters are analyzed,and the corresponding filter circuit is configured according to the actual sensor bandwidth.Experiments show that the circuit can effectively filter out noises among the vibration signal and obtain vibration signal accurately.

Experimental Measurements of the Sensitivity of Fiber-optic Bragg Grating Sensors with a Soft Polymeric Coating under Mechanical Loading,Thermal and Magnetic under Cryogenic Conditions

The strain and temperature sensing performance of fiber-optic Bragg gratings （FBGs） with soft polymeric coating, which can be used to sense internal strain in superconducting coils, are evaluated under variable cryogenic field and magnetic field. The response to a temperature and strain change of coated-soft polymeric FBGs is tested by comparing with those of coated-metal FBGs. The results indicate that the coated-soft polymeric FBGs can freely detect temperature and thermal strain, their At variable magnetic field, the tested results indicate accuracy and repeatability are also discussed in detail. that the cross-coupling effects of FBGs with different matrixes are not negligible to measure electromagnetic strain during fast excitation. The present results are expected to be able to provide basis measurements on the strain of pulsed superconducting magnet/cable （cable- around-conduit conductors, cable-in-conduit conductors）, independently or utilized together with other strain measurement methods.

Design, Implementation and Simulation of Non-Intrusive Sensor for On-Line Condition Monitoring of MV Electrical Components

Non-intrusive measurement technology is of great interest for the electrical utilities in order to avoid an interruption in the normal operation of the supply network during diagnostics measurements and inspections. Inductively coupled electromagnetic sensing provides a possibility of non-intrusive measurements for online condition monitoring of the electrical components in a Medium Voltage (MV) distribution network. This is accomplished by employing Partial Discharge (PD) activity monitoring, one of the successful methods to assess the working condition of MV components but often requires specialized equipment for carrying out the measurements. In this paper, Rogowski coil sensor is presented as a robust solution for non-intrusive measurements of PD signals. A high frequency prototype of Rogowski coil is designed in the laboratory. Step-by-step approach of constructing the sensor system is presented and performance of its components (coil head, damping component, integrator and data acquisition system) is evaluated using practical and simulated environments. Alternative Transient Program-Electromagnetic Transient Program (ATP-EMTP) is used to analyze the designed model of the Rogowski coil. Real and simulated models of the coil are used to investigate the behavior of Rogowski coil sensor at its different stages of development from a transducer coil to a complete measuring device. Both models are compared to evaluate their accuracy for PD applications. Due to simple design, flexible hardware, and low cost of Rogowski coil, it can be considered as an efficient current measuring device for integrated monitoring applications where a large number of sensors are required to develop an automated online condition monitoring system for a distribution network.

Rapid Evaluation for Feed-axis Lubrication Condition Based on Soft Sensor

Stribeck effect is regarded as the most important feed-axis friction characteristics. According to the relationship between friction and lubrication,a rapid technology for feed-axis lubrication condition evaluation of computer numerical control( CNC) machine tools based on soft sensor is proposed. To obtain its state information,the static friction force,Coulomb friction force,and viscous coefficient are used as the key parameters of the soft sensor for tread analysis. Then the various amplitude and velocity triangular wave test curve, and a precise nonlinear model identification method are presented. The results of the experiments analysis show that this method is feasible and reliable for evaluating feed-axis lubrication condition,which lays the foundation for on-line condition monitoring and reliability evaluation for feed-axis lubrication of machine tools.

Optimal design and experiment research of an orthogonal-parallel six-axis force/torque sensor

A novel orthogonal-parallel six-axis force/torque sensor is studied based on a modified Stewart platform architecture,and the optimal design and experiment research of the sensor are discussed.Firstly,the model of orthogonal parallel six-axis force/torque sensor based on improved Stewart platform architecture and its static mathematical model are proposed.Secondly,according to the actual working condition of the sensor,the sensor is optimized and the optimal solution is obtained.Then,the experimental prototype and calibration system is developed.Finally,the superiority of the sensor structure and the effectiveness of the optimization method are verified by calibration experiments.The results of the proposed method are useful for the further research and application of the orthogonal-parallel six-axis force/torque sensor.

Induced pluripotent stem cells as suitable sensors for fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome

BACKGROUND Fibromyalgia(FM)and myalgic encephalomyelitis/chronic fatigue syndrome(ME/CFS)are devastating metabolic neuroimmune diseases that are difficult to diagnose because of the presence of numerous symptoms and a lack of specific biomarkers.Despite patient heterogeneity linked to patient subgroups and variation in disease severity,anomalies are found in the blood and plasma of these patients when compared with healthy control groups.The seeming specificity of these“plasma factors”,as recently reported by Ron Davis and his group at Stanford University,CA,United States,and observations by our group,have led to the proposal that induced pluripotent stem cells(iPSCs)may be used as metabolic sensors for FM and ME/CFS,a hypothesis that is the basis for this indepth review.AIM To identify metabolic signatures in FM and/or ME/CFS supporting the existence of disease-associated plasma factors to be sensed by iPSCs.METHODS A PRISMA(Preferred Reported Items for Systematic Reviews and Meta-analysis)-based systematic review of the literature was used to select original studies evaluating the metabolite profiles of FM and ME/CFS body fluids.The MeSH terms“metabolomic”or“metabolites”in combination with FM and ME/CFS disease terms were screened against the PubMed database.Only original studies applying omics technologies,published in English,were included.The data obtained were tabulated according to the disease and type of body fluid analyzed.Coincidences across studies were searched and P-values reported by the original studies were gathered to document significant differences found in the disease groups.RESULTS Eighteen previous studies show that some metabolites are commonly altered in ME/CFS and FM body fluids.In vitro cell-based assays have the potential to be developed as screening platforms,providing evidence for the existence of factors in patient body fluids capable of altering morphology,differentiation state and/or growth patterns.Moreover,they can be further developed using approaches aimed at blocking or reversing the effects of specific plasma/serum factors seen in patients.The documented high sensitivity and effective responses of iPSCs to environmental cues suggests that these pluripotent cells could form robust,reproducible reporter systems of metabolic diseases,including ME/CFS and FM.Furthermore,culturing iPSCs,or their mesenchymal stem cell counterparts,in patient-conditioned medium may provide valuable information to predict individual outcomes to stem-cell therapy in the context of precision medicine studies.CONCLUSION This opinion review explains our hypothesis that iPSCs could be developed as a screening platform to provide evidence of a metabolic imbalance in FM and ME/CFS.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

Wireless Self-Powered Vibration Sensor System for Intelligent Spindle Monitoring

In recent years,high-end equipment is widely used in industry and the accuracy requirements of the equipment have been risen year by year.During the machining process,the high-end equipment failure may have a great impact on the product quality.It is necessary to monitor the status of equipment and to predict fault diagnosis.At present,most of the condition monitoring devices for mechanical equipment have problems of large size,low precision and low energy utilization.A wireless self-powered intelligent spindle vibration acceleration sensor system based on piezoelectric energy harvesting is proposed.Based on rotor sensing technology,a sensor is made to mount on the tool holder and build the related circuit.Firstly,the energy management module collects the mechanical energy in the environment and converts the piezoelectric vibration energy into electric energy to provide 3.3 Vfor the subsequent circuit.The lithium battery supplies the system with additional power and monitors’the power of the energy storage circuit in real-time.Secondly,a three-axis acceleration sensor is used to collect,analyze and filter a series of signal processing operations of the vibration signal in the environment.The signal is sent to the upper computer by wireless transmission.The host computer outputs the corresponding X,Y,and Z channel waveforms and data under the condition of the spindle speed of 50∼2500 r/min with real-time monitoring.The KEIL5 platform is used to develop the system software.The small-size piezoelectric vibration sensor with high-speed,high-energy utilization,high accuracy,and easy installation is used for spindle monitoring.The experiment results show that the sensor system is available and practical.

Force/Moment Isotropy of 8/4-4 Parallel Six-Axis Force Sensor Based on Performance Atlases

A six-axis force sensor with parallel 8/4-4 structure is introduced and its measurement principle is analyzed.Based on condition numbers of Jacobian matrix spectral norm of the sensor,the relationship between the force and moment isotropy and some structural parameters is deduced.Orthogonal test methods are used to determine the degree of primary and secondary factors that have significant effect on sensor characteristics.Furthermore,the relationship between each performance index and the structural parameters of the sensor is analyzed by the method of the atlas,which lays a foundation for structural optimization design of the force sensor.

Optical Fiber Grating Sensor for Force Measurement of Anchor Cable

The development of the sensor suitable for measuring large load stress to the anchor cable becomes an important task in bridge construction and maintenance. Therefore, a new type of optical fiber sensor was developed in the laboratory - optical fiber grating sensor for force measurement of anchor cable （OFBFMAC）. No similar report about this kind of sensor has been found up to now in China and other countries. This sensor is proved to be an effective way of monitoring in processes of anchor cable installation, cable cutting, cable force regulation, etc, with the accurate and repeatable measuring results. Its successful application in the tie bar cable force safety monitoring for Wuhan Qingchuan bridge is a new exploration of optical fiber grating sensing technology in bridge tie bar monitoring system.

Enabling Long-Term Operation of GaAs-Based Sensors

A coating scheme was developed for enabling the operation of a GaAs-based Molecular Controlled Semiconductor Resistor (MOCSER) under biological conditions. Usually GaAs is susceptible to etching in an aqueous environment. Several methods of protecting the semiconductor based devices were suggested previously. However, even when protected, it is very difficult to ensure the operation of a GaAs-based electronic sensor in aqua solution for long periods. We developed a new depositing scheme of (3-mercaptopropyl)-trimethoxysilane (MPTMS) on GaAs substrate consisting of two separate steps. The first involves chemisorption of a dense primary MPTMS layer on the substrate, whereas in the second, a thin MPTMS polymer layer is deposited on the already adsorbed layer, resulting in a 15 -?29 nm thick coating. We show that applying the new MPTMS deposition procedure to GaAs-based MOCSER devices allows up to 15 hours of continuous electrical measurements and stable performance of the sensing device in harsh biological environment. The new protection allows implementing GaAs technology in bioelectronics, particularly in biosensing.

MEMS Thermal Sensor在人感空调上的应用

介绍MEMS Thermal Sensor(微电子机械红外传感系统)的基本原理,基于Freescale单片机实现MEMS Thermal Sensor在人感空调上的应用以及具体实现方法。

Progress and trend of sensor technology for on-line oil monitoring

Oil monitoring constitutes an important and essential component of condition monitoring technologies and has distinguished advantages in revealing wear,lubrication and friction conditions of tribo-pairs.Newly developed on-line/in-line oil monitoring technologies extend the merits into real-time applications and demonstrate significant benefits in maintenance and management of equipment.This paper comprehensively reviews the progress of on-line/in-line oil monitoring techniques including sensor technologies,their scopes and industrial applications.Based on the existing developments and applications of the sensors for oil quality and wear debris measurements,the trends for future sensor developments are discussed with focuses on accurate,integrated and intelligent features along with exploring a fundamental issue,that is,acquiring the knowledge on degradation mechanisms which has not received sufficient attention until now.Current status of applications of on-line oil monitoring is also reviewed.Although limited reports have been found on this topic,increasing awareness and encouraging progress in on-line monitoring techniques are recognized in applications such as aircraft,shipping,railway,mining,etc.Key fundamental issues for further extending the on-line oil monitoring techniques in industries are proposed and they include long-term reliability of sensors in harsh conditions,and agreement with fault or maintenance determination.

Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators （Cls） are presented, and the optimal Cls set is selected by comparing their test statistics according to Mann-Kendall test. Afterwards, the selected CIs are used to generate a health indicator （HI） through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitor- ing of helicopter transmission systems, and it is effective to reduce the test cost and improve the system＇s reliability.

Etched Optical Fiber Vibration Sensor to Monitor Health Condition of Beam Like Structures

Using a center etched single mode optical fiber, a simple vibration senor is designed to monitor the vibrations of a simply supported beam. The sensor has high linear response to the axial displacement of about 0.8mm with a sensitivity of 32mV/10p, m strain. The sensor is tested for periodic and suddenly released forces, and the results are found to coincide with the theoretical values. This simple design, small in size and low cost sensor may find applications in industry and civil engineering to monitor the vibrations of the beam structures and bridges.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

Road surface condition sensor based on scanning detection of backward power

A method of detecting dry, icy and wet road surface conditions based on scanniag detection of single wavelength backward power is proposed in this letter. The detector is used to receive the backward scattered power which changes with the incidence angle. The relationship between backward power and incidence angle is used to find out the effective angle range and distinguish method. Experiment and simulation show that it is feasible to classifv these three conditions within incidence angle of 5.3 degree.

Robust Sensor Bias Estimation for Ill-Conditioned Scenarios

Sensor bias estimation is an inherent problem in multi-sensor data fusion systems. Classical methods such as the Generalized Least Squares （GLS） method can have numerical problems with ill-conditioned sets which are common in practical applications. This paper describes an azimuth-GLS method that provides a solution to the ill-conditioning problem while maintaining reasonable accuracy com- pared with the classical GLS method. The mean square error is given for both methods as a criterion to de- termine when to use this azimuth-GLS method. Furthermore, the separation boundary between the azi- muth-GLS favorable region and that of the GLS method is explicitly plotted. Extensive simulations show that the azimuth-GLS approach is preferable in most scenarios.

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review

As one of the most important terminals in machining, cutting tools have been widely used for components manufacturing in aerospace and other industries. The quality of these components and processing efficiency are closely linked to the performance of cutting tools. Therefore, it is essential and critical to inspect the cutting tools and monitor the condition during the stage of manufacturing and machining. This review aims to discuss and summarize the key problems, methods,and techniques from the perspective of the tool geometric and the physical quantities measurement,including machine vision, physical sensors and data processing. It is worth mentioning that we focus on the topic of precision measurement methods and discuss universal solutions by identifying the common characteristics of the measured quantities. Eventually, the challenges and future trends for the development of in-depth research and practical applications are concluded. The research and application of precise measurement techniques for geometric and physical quantities will better promote the development of intelligent manufacturing.