A bridge-type inductance sensor with a two-stage filter circuit for high-precision detection of metal debris in the oil is proposed. The sensor mainly consists of a bridge-structure sensing unit and a two-stage filter circuit. The sensing unit consists of two double-wire solenoid coils (as the detection unit and the reference unit, respectively) and a Wheatstone bridge -structure. This structure enhances the response of the detection unit to the magnetic field disturbance generated by the wear debris and can improve the detection accuracy of the sensor. Furthermore, a circuit with rectification, amplitude modulation, and amplification functions is introduced to eliminate the noise interference from the environment and the system, and to accurately extract the weak abrasive signals that are submerged in the noise. Meanwhile, by adjusting the passband gain of the two-stage filter circuit, the measurement range of the sensor voltage value is improved. Experimental results show that adding a two-stage filter circuit can improve the resolution of the sensor, and can effectively detect $45~\mu \text{m}$ iron particles. The sensor provides technical support for the modularity and portability of the testing instrument, which is essential for fault diagnosis and prevention of the hydraulic systems.

A Bridge-Type Inductance Sensor With a Two-Stage Filter Circuit for High-Precision Detection of Metal Debris in the Oil

Magnetic manipulation has the potential to recast the medical field both from an operational and drug delivery point of view as it can provide wireless controlled navigation over surgical devices and drug containers inside a human body. The presented system in this research implements a unique eight-coil configuration, where each coil is designed based on the characterization of the working space, generated force on a milliscale robot, and Fabry factor. A cylindrical iron-core coil with inner and outer diameters and length of 20.5, 66, and 124 mm is the optimized coil. Traditionally, FEM results are adopted from simulation and implemented into the motion logic; however, simulated values are associated with errors; 17% in this study. Instead of regularizing FEM results, for the first time, artificial intelligence has been used to approximate the actual values for manipulation purposes. Regression models for Artificial Neural Network (ANN) and a hybrid method called Artificial Neural Network with Simulated Annealing (ANN/SA) have been created. ANN/SA has shown outstanding performance with an average R2, and a root mean square error of 0.9871 and 0.0153, respectively. Implementation of the regression model into the manipulation logic has provided a motion with 13 μm of accuracy.

/pdf/design-and-fabrication-of-a-magnetic-actuator-for-torque-and-mujp1h5g.pdf

Design and Fabrication of a Magnetic Actuator for Torque and Force Control Estimated by the ANN/SA Algorithm

Magnetic contacts we could define as a switching device used in transport structures such as a tunnel, to which the manufacturer prescribes a certain number of closures within its lifetime, during which they should operate flawlessly. Verification of the data provided by the manufacturer is time-consuming and physically demanding due to the data being large in number. For this reason, we developed a test device using torque in the research of magnetic contacts, which greatly automates the whole process and thus eliminates human error. The test device can use internal memory to calculate the number of closures of magnetic contacts and then transmit the digitized data. The test device is registered as an industrial utility model and can be used to test any magnetic contacts.

https://www.mdpi.com/2072-666X/12/5/479/pdf

New Possibilities for Testing the Service Life of Magnetic Contacts.

Airborne metal particles (MPs; particle size > 10 μm) in workplaces result in a loss in production yield if not detected in time. The demand for compact and cost-efficient MP sensors to monitor airborne MP generation is increasing. However, contemporary instruments and laboratory-grade sensors exhibit certain limitations in real-time and on-site monitoring of airborne MPs. This paper presents a microfluidic MP detection chip to address these limitations. By combining the proposed system with microcirculation-based particle-to-liquid collection and a capacitive sensing method, the continuous detection of airborne MPs can be achieved. A few microfabrication processes were realized, resulting in a compact system, which can be easily replaced after contamination with a low-priced microfluidic chip. In our experiments, the frequency-dependent capacitive changes were characterized using MP (aluminum) samples (sizes ranging from 10 μm to 40 μm). Performance evaluation of the proposed system under test-bed conditions indicated that it is capable of real-time and continuous monitoring of airborne MPs (minimum size 10 μm) under an optimal frequency, with superior sensitivity and responsivity. Therefore, the proposed system can be used as an on-site MP sensor for unexpected airborne MP generation in precise manufacturing facilities where metal sources are used.

Microfluidic Airborne Metal Particle Sensor Using Oil Microcirculation for Real-Time and Continuous Monitoring of Metal Particle Emission.

Inductance detection is an important method for detecting wear debris in ship lubricating oil. Presently, an LCR (inductance, resistance, capacitance) meter is generally used to detect wear debris by measuring the inductance change of the sensing coil. When ferromagnetic debris passes through the sensing coil, a pulse will appear in the inductance signal. Previous studies have shown that the amplitude of the inductance pulse decreases significantly with the increase in the particles’ velocity. Therefore, it is difficult to detect ferromagnetic debris with a high flow velocity using an LCR meter. In this paper, a novel method, high-frequency voltage acquisition (HFVA), is proposed to detect ferromagnetic debris. Different from previous methods, the wear debris was detected directly by measuring the voltage change of the sensing coil, while the synchronized sampling method was utilized to ensure the higher-frequency acquisition of the sensor output signal. The experimental results show that when the velocity of particles increased from 6 mm/s to 62 mm/s, the amplitude of the signal pulse obtained by HFVA decreased by only 13%, which was much lower than the 85% obtained by utilizing the LCR method.

/pdf/a-novel-method-for-detecting-ferromagnetic-wear-debris-with-12r5ft96.pdf

A Novel Method for Detecting Ferromagnetic Wear Debris with High Flow Velocity

The inductive debris sensor has been studied because of its wide application prospects in mechanical health monitoring. In order to ensure a high-precision detection performance, a comprehensive method to improve the detection sensitivity and detection ability of the inductive sensor for non-ferromagnetic metal debris is proposed. Based on the characteristics of the eddy current inside the metal, the change of the coil impedance caused by the metal debris is increased by enhancing the magnetic field strength and selecting the optimal excitation frequency. The impedance detection method involving inductance and resistance parameters is used to improve the detection limit of non-ferromagnetic metal debris. The experimental results verify that the magnetic field in the detection region can be enhanced by adding a silicon steel strip (paramagnetic material) in the central hole of the coil, thereby greatly improving the detection sensitivity of the inductive sensor, and the concentrated distribution of the magnetic field avoids the double-peak signals generated by a single particle. The characteristics of the signal amplitude of non-ferromagnetic debris with excitation frequency are studied. Higher inductance, resistance amplitudes, and signal-to-noise ratio (SNR) can be obtained by using a high-frequency alternating current. Compared with inductance parameter detection, resistance parameter detection can detect smaller non-ferromagnetic debris. Combining the detection results of the inductance and resistance parameters can effectively improve the sensor's ability to detect non-ferromagnetic debris.

Improving the Detection Ability of Inductive Micro-Sensor for Non-Ferromagnetic Wear Debris.

The cleanliness of hydraulic oil can reflect the service life of the oil and the wear state of hydraulic machinery. An impedance sensor is proposed to distinguish multi-contaminants in hydraulic oil. The impedance sensor has two detection modes: the inductance-resistance mode is used to detect metal debris, and the capacitance mode is used to distinguish water droplets and air bubbles. By adding a built-in silicon steel strip and an external silicon steel strip with high magnetic permeability, the distribution area, strength, and uniformity of the magnetic field are enhanced to improve the detection sensitivity under inductance and resistance parameters. In addition, the silicon steel strips are used as electrode plates to introduce capacitance parameter detection. The experimental results show that the resistance detection method based on coil successfully improves the detection ability for non-ferromagnetic metal debris. The impedance sensor for distinguishing multi-contaminants in hydraulic oil can provide technical support for fault diagnosis of offshore hydraulic machinery.

An Impedance Sensor for Distinguishing Multi-Contaminants in Hydraulic Oil of Offshore Machinery

An on-chip inductive-capacitive sensor is proposed to detect metal debris and air bubbles in hydraulic oil. The sensor includes inductance and capacitance detection units. The inductance detection unit detects and distinguishes the properties of metal debris by monitoring the inductance change of the solenoid coil. The capacitance detection unit detects air bubbles by monitoring the capacitance change of the cylindrical capacitor. Inductance tests are conducted using iron and copper debris with different sizes. The test results showed that the inductance pulse amplitude increases nonlinearly with debris size. Capacitance tests are conducted using air bubbles with different sizes. The test results showed that the cylindrical capacitor can detect and count air bubbles with high sensitivity. The design concept presented in this paper can be extended to oil conditioning sensors for multi-contaminants detection and differentiation.

An On-Chip Inductive-Capacitive Sensor for the Detection of Wear Debris and Air Bubbles in Hydraulic Oil **This work was supported by the Natural Science Foundation of China (Grant No. 51679022), the Dalian Science and Technology Innovation Fund (Grant No. 2019J12GX023).

As a critical component of the wind turbine, the gearbox will produce metal particles when worn, which will shorten the system's service life. In this paper, a metal wear detection sensor containing high magnetic conductivity material is designed. The sensor consists of a multi-layer winding coil and closely matched permalloy. Four permalloy arrays are arranged on one side of the coil at an angle of 90 degrees. The simulation results show that the four pieces of permalloy can better assemble the magnetic field in the center of the detection area. Moreover, the multi-layer winding coil increases the detection area of the sensor and improves the detection efficiency. According to the experiment, the presence of permalloy can increase the sensitivity of the detection chip but will not increase the noise of the detection system. The signal-to-noise is six times higher than that of a single coil. The sensor has high-pitched detection accuracy and can successfully detect 30 µm iron wear particles and 85 µm copper wear particles. This study has more practical implications for condition monitoring and fault diagnosis of wind turbine gearboxes. It has great significance for prolonging the service life of wind turbines. 

Dian Huo

Papers

Improving the Detection Ability of Inductive Micro-Sensor for Non-Ferromagnetic Wear Debris.

An Impedance Sensor for Distinguishing Multi-Contaminants in Hydraulic Oil of Offshore Machinery

An On-Chip Inductive-Capacitive Sensor for the Detection of Wear Debris and Air Bubbles in Hydraulic Oil **This work was supported by the Natural Science Foundation of China (Grant No. 51679022), the Dalian Science and Technology Innovation Fund (Grant No. 2019J12GX023).

A sensor containing high permeability material for mechanical wear particle detection