Chun-Yeon Lin

This paper presents an enclosed differential driver‐pickup eddy current (EC) sensor for detecting internal and external defects in HP‐40 Mod Nb reformer tubes. These tubes are widely used for hot‐gas transport in petrochemical plants and are susceptible to thermal cracking during long‐term operation. Inspection of this alloy is challenging because of its low electrical conductivity and low magnetic permeability. By employing a differential coil configuration, the sensor effectively suppresses environmental noise while significantly enhancing crack detection sensitivity. Finite‐element analysis (FEA) simulations are performed to design sensor geometry and evaluate its performance for external‐defect inspection, while the optimal excitation frequency for internal‐defect detection is also investigated. For experimental validation, a prototype sensor and an automated constant‐velocity rotational scanning platform are developed. High-pass filtering and empirical curve fitting are further employed for defect‐feature extraction and external-defect depth estimation. Experimental results show that the proposed system accurately characterizes external defects deeper than 0.5 mm, with a mean absolute percentage error of 3.03%, while also detecting relatively shallow internal defects under nonideal specimen conditions.

This article presents an eddy current tomography framework for imaging defects in metal structures. The tomography problem is formulated as a linear inverse problem with a binary solution vector. A Bayesian approach is utilized, incorporating a binary-inducing prior and determining the posterior probability conditioned on the measurements. Since recovering binary vectors from underdetermined linear measurements is NP-hard, an approximation to the true posterior is obtained by minimizing a Kullback-Leibler (KL) divergence. Alternatively, a convex optimization approach relaxes the binary constraint and applies alternating direction method of multipliers (ADMM) to compute a solution. The convergence of both algorithms is proven. To improve computational efficiency, the two algorithms are cascaded and augmented with a decomposition technique to form a hybrid algorithm. The proposed framework is validated experimentally with a prototype eddy current sensing probe, demonstrating the ability to image defects as small as 1 mm at various depths using a sensor array with 4 mm spacing.

Common imaging techniques for detecting structural defects typically require sampling at more than twice the spatial frequency to achieve a target resolution. This study introduces a novel framework for imaging structural defects using significantly fewer samples. In this framework, defects are modeled as regions where physical properties shift from their nominal values to resemble those of air, and a linear approximation is formulated to relate these binary shifts in physical properties with corresponding changes in measurements. Recovering a binary vector from linear measurements is generally an NP-hard problem. To address this challenge, this study proposes two algorithmic approaches. The first approach relaxes the binary constraint, using convex optimization to find a solution. The second approach incorporates a binary-inducing prior and employs approximate Bayesian inference to estimate the posterior probability of the binary vector given the measurements. Both algorithmic approaches demonstrate better performance compared to existing compressive sampling methods for binary vector recovery. The framework's effectiveness is illustrated through examples of eddy current sensing to image defects in metal structures.

This paper presents a supervisor model-switch method for model predictive control of room temperature using a one-to-three variable refrigerant flow (VRF) system. This method utilizes system identification to obtain linearized transfer functions of the variable refrigerant flow system’s compressor speed and electronic expansion valve to room and superheat temperature. The transfer functions for room and the two-phase region temperatures of the evaporator are derived from the energy conservation law to apply in VRF systems. The model predictive control structure is adopted to consider the coupling effects between control states. The effects of superheat and room temperature error weighting factors on rapid cooling and steady-state error are numerically investigated. In addition, this research utilizes a switching linearization model approach to overcome the limitation of a single identification model with a narrow, accurate temperature control range. A supervisor model-switch strategy is proposed to switch multiple models for the same target temperature to average the risk of model mismatch, and calculate the minimum allowable switching time to ensure system stability during switching, thereby expanding the range of temperature control. The proposed method has been experimentally evaluated on a one-to-three VRF system to control the steady-state temperature of each indoor unit within 0.5°C and maintain a superheat above 0°C to avoid liquid refrigerant from entering the compressor.

This study presents a novel eddy current sensing method for imaging defect distribution in multilayered nonferrous metal plates using an array of magnetic sensors. This method involves dividing the metal plate into small voxels to facilitate reconstruction and uses multifrequency to excite the coil. For each frequency, reconstructing defects from magnetic flux density measurements is formulated as a linear inverse problem. The absence or presence of a defect strongly suggests that the solution to the linear inverse problem is binary. This study develops an algorithm under a statistical framework to solve the linear inverse problem with binary constraints. The algorithm introduces a Bernoulli prior over the hidden variables and uses a variational Bayesian inference to analytically approximate the posterior probability of the hidden variables conditioned on the observed data. The effectiveness of the proposed method is demonstrated using numerically simulated data and a prototype consisting of a coil and an 8 × 8 array of magnetic sensors with 4 mm intervals. The results demonstrate that the method is feasible for imaging defects of 2 mm with a depth resolution of 0.5 mm.

This paper presents a pulsed eddy current (PEC) sensor design for cascade electrical conductivity and thickness estimation in non-ferrous metal plates. With a single excitation coil design, the combined change in magnetic flux density caused by the excitation coil and the induced eddy current (EC) in the test plate is detected by a compact anisotropic magneto-resistive (AMR) sensor. The effects of the varying input pulse width are discussed with both numerical simulations in commercial finite element analysis (FEA) software and experimental results, and the time-domain characteristics of the responses are analyzed in relation to the properties of the test plates. It is revealed that the electrical conductivity can be gauged based on the peak value of the short pulse response, and with a known electrical conductivity value, plate thickness can be subsequently derived with the rise time of the long pulse response. A cascade estimation process is therefore proposed to calculate the parameters sequentially by adopting hybrid input signals with both short and long input pulses. Verified by the FEA results, this cascade estimation process was then experimentally validated with a prototype of the PEC sensor.

This paper presents the development of an electromagnetic (EM) tracking system that consists of a pyramid field generator (PFG) of five excitation coils and a sensing coil for location and orientation estimation. The system measures the induced electromotive forces (EMF) of the sensing coil with the change of the magnetic field contributed by the PFG. The proposed modeling distributed magnetic flux (DMF) method that divides the multi-turns sensing coil into multiple layers and concentric circles for the distributed elements is utilized to determine EMF induced in the sensing coils for investigating the effects of translational and angular displacement. The finite element analysis numerically verifies the DMF method and validates the Artificial Neural Network (ANN)-based estimation models. The calibration and ANN-based estimation process are applied to inversely estimate the location and orientation of the sensing coil based on the DMF method and the experimental EM tracking system. The proposed design, along with a prototype of the EM tracking system, has experimentally verified the DMF method and showed the validity of the location and orientation estimation.

This paper presents a vibration control method for the vibration suppression of a robotic arm. To understand the causes of the vibration in the six-axis manipulator and then eliminate the vibration through an effective control method, the vibration control structure can be divided into two parts. First, variational mode decomposition (VMD) and the Hilbert–Huang transform (HHT) algorithm are integrated to analyze the vibration signal and extract the vibration characteristics. Second, a smooth trajectory planning method is used to eliminate the residual vibration. Then, a feedback controller and iterative learning controller are used for controlling the vibration to stabilize the system while compensating for the torque of the elastic-joint model and repetitive errors caused by the vibration through the repeated operation of the arm. In addition, to improve the iterative learning control (ILC) performance, the integration of VMD and the HHT is used to distinguish useless information. The ILC is able to learn more quickly and compensate for the error caused by the vibration. The proposed VMD-HHT iterative learning control for the elastic joint (VH-ILC-EJ) method, along with a laboratory-developed six-axis degree-of-freedom (6-DOF) robotic arm, has been numerically and experimentally validated for vibration suppression.

This paper presents a distributed current source (DCS) method for modeling the eddy current (EC) field induced in biological or non-ferrous metallic objects in two-dimensional axisymmetric and three-dimensional Cartesian coordinates. The EC fields induced in the objects, magnetic flux density (MFD) in space, and magnetic flux (MF) of the sensing coils are formulated in state-space representation. The harmonic responses of the eddy current fields and electromotive force (EMF) of the sensing coil are formulated in closed-form solutions. The proposed DCS method is applied to design two eddy current sensing systems. The Bio-Differential Eddy Current (BD-EC) sensor distinguishes biological objects, and the Metal-Coaxial Eddy Current (MC-EC) sensor classifies non-ferrous metallic objects. The simulated EC field and EMF are numerically verified by comparing results with finite element analysis. An example is utilized to illustrate the advantage of the DCS method for calculating the MFD, MF, and EMF contributed from the induced ECD in the objects directly, and the EMF generated from each material. The proposed method, along with a prototype of BD-EC sensor, has been experimentally evaluated on sweep frequency analysis for detecting meat and bone.

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.