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Conventional eddy-current sensors have the advantages of being contactless and having high bandwidth and high sensitivity. They are widely used in micro-displacement measurement, micro-angle measurement, and rotational speed measurement. However, they are based on the principle of impedance measurement, so the influence of temperature drift on sensor accuracy is difficult to overcome. A differential digital demodulation eddy current sensor system was designed to reduce the influence of temperature drift on the output accuracy of the eddy current sensor. The differential sensor probe was used to eliminate common-mode interference caused by temperature, and the differential analog carrier signal was digitized by a high-speed ADC. In the FPGA, the amplitude information is resolved using the double correlation demodulation method. The main sources of system errors were determined, and a test device was designed using a laser autocollimator. Tests were conducted to measure various aspects of sensor performance. Testing showed the following metrics for the differential digital demodulation eddy current sensor: nonlinearity 0.68% in the range of ±2.5 mm, resolution 760 nm, maximum bandwidth 25 kHz, and significant suppression in the temperature drift compared to analog demodulation methods. The tests show that the sensor has high precision, low temperature drift and great flexibility, and it can instead of conventional sensors in applications with large temperature variability.

The traditional pressure sensors are mostly based on piezoresistive, piezoelectric effect, and strain gauge, which are easily affected by ambient temperature. The structure of some sensors is rather complex to effectively acquire the signal or measure distribution force. Therefore, a pressure sensor based on the planar coil and elastic element was designed to solve this problem. The newly designed sensor has a spiral planar coil made by a printed circuit board and a polydimethylsiloxane (PDMS) thin film as the elastic element. The conductive layer is attached on the upper surface of PDMS to realise the eddy current effect. Meanwhile, a simulation model was established to find the relationship between deformation displacement and the pressure of the elastic element. By generating the experimental curves of the deformation displacement of the elastic element and the output frequency of the eddy current sensor, the pressure measurement on the surface of the elastic element can be identified. The preliminary experimental results showed that the eddy current pressure sensor based on the plane coil has good linearity and repeatability, and it can realise large area distributed pressure measurement, which is also suitable for accurate measurement of distribution force.

Lift-off of sensor affects the prediction of electromagnetic properties for both ferrous and non-ferrous steel plates. In this paper, we developed a strategy to address this issue for ferrous plates. With increased lift-off, the phase of the measured impedance for steel plates reduces. Meanwhile, the magnitude of the impedance signal decreases. Based on these facts, a phase compensation algorithm is developed which corrects the phase change due to lift-off considering the magnitude of the impedance signal. Further, a new magnetic permeability prediction technique is presented, which has been validated by analytical and measured results. With this new technique, the error in permeability prediction is less than 2% within the range of lift-offs tested.

This paper deals with a design of wireless proximity sensing between the stator and rotor of a steam turbine during a final steps of an assembly. The result of this assembly process is an optimal setting of the seal which affects overall turbine efficiency. An innovative approach including internet of things (IoT) principles and mesh network topology has been used to transmit critical data to the supervisor. A few working prototypes have been created and tested in real production in cooperation with an industrial partner.

Precision spherical joint is a spherical motion pair that can realize rotation with three degrees of freedom. This joint is widely used in robots, parallel mechanisms, and high-end medical equipment, as well as in aerospace and other fields. However, the rotation orientation and angle cannot be determined when the joint is in passive motion. The real-time determination of the rotation orientation and angle is crucial to the improvement of the motion control accuracy of the equipment where the joint is installed in. In this study, a new measurement method that utilizes eddy current sensors is proposed to identify the special features of the joint ball and realize angle measurements indirectly. The basic idea is to manufacture the specific shape features on the ball without affecting its movement accuracy and mechanical performance. An eddy current sensor array is distributed in the ball socket. When the ball head rotates, the features on the ball opposite to the sensor, as well as the output signal of every eddy current sensor, change. The measurement model that establishes the relationship between the output signal of the eddy current sensor array and the rotation direction and angle of the ball head is constructed by learning and training an artificial neural network. A prototype is developed using the proposed scheme, and the model simulation and feasibility experiment are subsequently performed. Results show that the root mean square angular error of a single axis within a range of &plusmn, 14&deg, is approximately 20 min, which suggests the feasibility of the proposed method.

A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons.

This paper proposes a linear eddy-current feature to determine the radius of a metallic ball in a non-contact manner. An electromagnetic eddy-current sensor with two coils is placed co-axially to the metal ball during measurement. It is well known that the distance between the sensor and test piece (i.e. lift-off) affects eddy-current signals. In this paper, it is found that the peak frequency feature of inductance spectrum is linear to the lift-off spacing between the centre of coil and ball. Besides, the slope of peak frequencies versus lift-offs is linked to the radius of ball. The radius of metallic balls is retrieved from the experimental and embedded analytical result of the slope. Measurements have been carried out on 6 metallic balls with different radii. The radius of the metallic ball can be retrieved with an error of less than 2 %.

International audience; The paper presents a new complete approach for Tool Condition Monitoring (TCM) in milling. The aim is the early detection of small damages so that catastrophic tool failures are prevented. A versatile in-process monitoring system is introduced for reliability concerns. The tool condition is determined by estimates of the radial eccentricity of the teeth. An adequate criterion is proposed combining mechanical model of milling and angular approach. Then, a new solution is proposed for the estimate of cutting force using eddy current sensors implemented close to spindle nose. Signals are analysed in the angular domain, notably by synchronous averaging technique. Phase shifts induced by changes of machining direction are compensated. Results are compared with cutting forces measured with a dynamometer table. The proposed method is implemented in an industrial case of pocket machining operation. One of the cutting edges has been slightly damaged during the machining, as shown by a direct measurement of the tool. A control chart is established with the estimates of cutter eccentricity obtained during the machining from the eddy current sensors signals. Efficiency and reliability of the method is demonstrated by a successful detection of the damage.

International audience; Eddy Current Testing (ECT) is a fast and effective method for detecting and sizing most of the default in conducting materials. The size estimation of an unknown defect from the measurement of the impedance variations is an important technique in industrial area. This paper considers to solve this problem by the novel combination of the Least Square Support Vector Machines (LS-SVM) and Finite Element Method (FEM). The FEM is used to modelling the eddy current sensor. In this context, Comsol Multiphysics resolution using a 3D electromagnetic formulation have been considered to create a database required to train the LS-SVM. Several method exist to find the LS-SVM parameters. Electromagnetism-like Mechanism (EM) algorithm is proposed. A good agreement is obtained between the numerical results and the experimental measure.

For resolving deficiencies of conventional tension measurement methods, this paper proposes a novel eddy current sensor with a single-coil structure based on the inverse magnetostrictive effect. An inductor–resistor–capacitor (LRC) model of eddy current sensor, which considers more parameters than the traditional inductor–resistor (LR) model, was established. The eddy current sensor was operated by a swept frequency signal that ranged from 0.1 MHz to 1.6 MHz, encompassing the sensor resonant frequency. At the resonant frequency, the data of impedance magnitude and phase were extracted and linear relations between the impedance parameters and the external tension were ascertained. The experimental results show that the resonant frequency and impedance magnitude of eddy current sensor will decrease linearly with the increase of the external tension, which is consistent with the theoretical model. In addition, to improve sensor performance, the sleeve structure was designed to reduce the loss of magnetic field. Both finite element simulations and experimental results demonstrate that the sleeve structure provides a higher permeability path to the magnetic field lines than the non-sleeve structure and effectively improves sensor sensitivity and correlation coefficient.