The electromechanical impedance (EMI) technique is considered to be one of the most promising methods for developing structural health monitoring (SHM) systems. This technique is simple to implement and uses small and inexpensive piezoelectric sensors. However, practical problems have hindered its application to real-world structures, and temperature effects have been cited in the literature as critical problems. In this paper, we present an experimental study of the effect of temperature on the electrical impedance of the piezoelectric sensors used in the EMI technique. We used 5H PZT (lead zirconate titanate) ceramic sensors, which are commonly used in the EMI technique. The experimental results showed that the temperature effects were strongly frequency-dependent, which may motivate future research in the SHM field.

https://www.mdpi.com/1424-8220/14/1/1208/pdf

An Experimental Study on the Effect of Temperature on Piezoelectric Sensors for Impedance-Based Structural Health Monitoring

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

/pdf/technology-development-of-electric-vehicles-a-review-41lypxy17x.pdf

Technology development of electric vehicles: A review

Chipped radio-frequency identification (RFID) sensor systems have been studied for structural health monitoring (SHM) applications. However, the use of chip in sensor tags and its standardized narrowband operation contribute shortcomings in cost, durability, and detection capability. This paper presents a novel use of the frequency signature-based chipless RFID for metal crack detection and characterization operating in ultra-wideband frequency. The vision is to implement a low-cost and high-temperature-resistant passive wireless sensor able to monitor the crack on a metallic structure with multiparameter detection. We propose a chipless RFID sensor tag integrating four tip-loaded dipole resonators as a 4-bit ID encoder and a circular microstrip patch antenna (CMPA) resonator as a crack sensor. The radar cross section spectrum of the chipless RFID sensor tag generates four resonant frequencies from the dipole resonators and a resonant frequency from the CMPA resonator. Simulation and experimental results show that the resonant frequency shift of the CMPA is a useful feature to indicate the crack orientation and the crack width on a metallic structure. The direction of the resonant frequency shift represents the orientation of the crack, while the magnitude of the resonant frequency shift is proportional to the width of the crack. Furthermore, the experimentation with a natural fatigue crack sample proves that the proposed sensor tag is capable of detecting submillimeter cracks.

/pdf/chipless-rfid-sensor-tag-for-metal-crack-detection-and-1ifmzbp0h2.pdf

Chipless RFID Sensor Tag for Metal Crack Detection and Characterization

In this paper, a new fine-grained image categorization system that improves spatial pyramid matching is developed. In this method, multiple cells are combined into cellets in the proposed categorization model, which are highly responsive to an object's fine categories. The object components are represented by cellets that can connect spatially adjacent cells within the same pyramid level. Here, image categorization can be formulated as the matching between the cellets of corresponding images. Toward an effective matching process, an active learning algorithm that can effectively select a few representative cells for constructing the cellets is adopted. A linear-discriminant-analysis-like scheme is employed to select discriminative cellets. Then, fine-grained image categorization can be conducted with a trained linear support vector machine. Experimental results on three real-world data sets demonstrate that our proposed system outperforms the state of the art.

A Fine-Grained Image Categorization System by Cellet-Encoded Spatial Pyramid Modeling

Eddy current testing is well established for non-destructive testing of electrical conductive materials [1]. The development of radio frequency (RF) eddy current technology with frequency ranges up to 100 MHz made it possible to extend the classical fields of application even towards less conductive materials like CFRP [2] [3](Table 2). It turns out that RF eddy current technology on CFRP generates a growing number of valuable information for comprehensive material diagnostic. Both permittivity and conductivity of CFRP influence the complex impedance measured with RF eddy current devices. The electrical conductivity contains information about fiber texture like orientations, gaps or undulations in a multilayered material. The permittivity characterization influenced by dielectric properties allows the determination of local curing defects on CFRP e.g. hot spots, thermal impacts or polymer degradation. An explanation for that effect is seen in the measurement frequency range and the capacitive structure of the carbon rovings. Using radio wave frequencies for testing, the effect of displacement currents cannot be neglected anymore. The capacitive structures formed by the carbon rovings is supposed to further strengthen the dielectric influences on eddy current measurement signal [3]. This report gives an overview of several realized applications and should be understood as a general introduction of CFRP testing by HF Radio Wave techniques.

/pdf/review-on-quality-assurance-along-the-cfrp-value-chain-non-xdoo325fdy.pdf

Review on quality assurance along the CFRP value chain – Non-destructive testing of fabrics, preforms and CFRP by HF radio wave techniques

Non-destructive techniques are used widely in the metal industry in order to control the quality of materials. Eddy current testing is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. This paper includes an overview of the fundamentals and main variables of eddy current testing. It also describes the state-of-the-art sensors and modern techniques such as multi-frequency and pulsed systems. Recent advances in complex models towards solving crack-sensor interaction, developments in instrumentation due to advances in electronic devices, and the evolution of data processing suggest that eddy current testing systems will be increasingly used in the future.

/pdf/non-destructive-techniques-based-on-eddy-current-testing-ekomtngkyg.pdf

Non-Destructive Techniques Based on Eddy Current Testing

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.

https://www.mdpi.com/1424-8220/10/7/6901/pdf

Position and speed control of brushless DC motors using sensorless techniques and application trends.

Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based on the ripple component of the current. This paper presents a new method, based on the ripple component, for speed and position estimation in brushed dc motors, using support vector machines. The proposed method only measures the current and detects the pulses in this signal. The motor speed is estimated by using the inverse distance between the detected pulses, and the position is estimated by counting all detected pulses. The ability to detect ghost pulses and to discard false pulses is the main advantage of this method over other sensorless methods. The performed tests on two fractional horsepower brushed dc motors indicate that the method works correctly in a wide range of speeds and situations, in which the speed is constant or varies dynamically.

A New Method for Sensorless Estimation of the Speed and Position in Brushed DC Motors Using Support Vector Machines

Many motor applications require accurate speed measurement. For brushed dc motors, speed can be measured with conventional observers or sensorless observers. Sensorless observers have the advantage of not requiring any external devices to be attached to the motor. Instead, voltage and/or current are measured and used to estimate the speed. The sensorless observers are usually divided into two groups: those based on the dynamic model, and those based on the ripple component. This paper proposes a method that measures the current of brushed dc motors and analyses the position of its spectral components. From these spectral components, the method estimates the motor speed. Three tests, performed each with the speeds ranging from 2000 to 3000 rpm either at constant-speed, at slowly changing speeds, or at rapidly changing speeds, showed that the average error was below 1 rpm and that the deviation error was below 1.5 rpm. The proposed method: (i) is a novel method that is not based on either the dynamic model or on the ripple component; (ii) requires only the measurement of the current for the speed estimation; (iii) can be used for brushed dc (direct current) motors with a large number of coils; and (iv) achieves a low error in the speed estimation.

/pdf/a-novel-method-for-sensorless-speed-detection-of-brushed-dc-1apms6k312.pdf

Ernesto Vázquez-Sánchez

Papers

Non-Destructive Techniques Based on Eddy Current Testing

Position and speed control of brushless DC motors using sensorless techniques and application trends.

A New Method for Sensorless Estimation of the Speed and Position in Brushed DC Motors Using Support Vector Machines

A Novel Method for Sensorless Speed Detection of Brushed DC Motors