A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm.

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony-optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss-Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm's capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm's generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

A Study on Impact Force Detection Method Based on Piezoelectric Sensing.

Impact force refers to a transient phenomenon with a very short-acting time, but a large impulse. Therefore, the detection of impact vibration is critical for the reliability, stability, and overall life of mechanical parts. Accordingly, this paper proposes a method to indirectly characterize the impact force by using an impact stress wave. The LS-DYNA software is utilized to establish the model of a ball impacting the steel plate, and the impact force of the ball and the impact response of the detection point are obtained through explicit dynamic finite element analysis. In addition, on this basis, a correspondence between the impact force and the impact response is established, and finally, an experimental platform for impact force detection is built for experimental testing. The results obtained by the finite element method are in good agreement with the experimental measurement results, and it can be inferred that the detected piezoelectric signal can be used to characterize the impact force. The method proposed herein can guide the impact resistance design and safety assessment of structures in actual engineering applications.

Development of a 16-Channel Broadband Piezoelectric Micro Ultrasonic Transducer Array Probe for Pipeline Butt-Welded Defect Detection.

Butt welding is extensively applied in long-distance oil and gas pipelines, and it is of great significance to conduct non-destructive ultrasonic testing of girth welds in order to avoid leakage and safety accidents during pipeline production and operation. In view of the limitations of large transducer size, single fixed beam angle, low detection resolution and high cost of conventional ultrasonic inspection technologies, a 16-channel piezoelectric micro ultrasonic transducer (PMUT) array probe was developed through theoretical analysis and structural optimization design. After the probe impedance characterization, the experimental results show that the theoretical model can effectively guide the design of the ultrasonic transducer array, offering the maximum operating frequency deviation of less than 5%. The ultrasonic echo performance tests indicate that the average -6 dB bandwidth of the PMUT array probe can be up to 77.9%. In addition, the fabricated PMUT array probe has been used to successfully detect five common internal defects in pipeline girth welds. Due to the multiple micro array elements, flexible handling of each element, large bandwidth and high resolution of defect detection, the designed PMUT array probe can provide a good application potential in structural health monitoring and medical ultrasound imaging fields.

Modeling and Optimization of a Novel ScAlN-Based MEMS Scanning Mirror with Large Static and Dynamic Two-Axis Tilting Angles.

The piezoelectric MEMS (micro-electro-mechanical systems) scanning mirrors are in a great demand for numerous optoelectronic applications. However, the existing actuation strategies are severely limited for poor compatibility with CMOS process, non-linear control, insufficient mirror size and small angular travel. In this paper, a novel, particularly efficient ScAlN-based piezoelectric MEMS mirror with a pupil size of 10 mm is presented. The MEMS mirror consists of a reflection mirror plate, four meandering springs with mechanical rotation transformation, and eight right-angle trapezoidal actuators designed in Union Jack-shaped form. Theoretical modeling, simulations and comparative analysis have been investigated for optimizing two different device designs. For Device A with a 1 mm-length square mirror, the orthogonal and diagonal static tilting angles are ±36.2°@200 VDC and ±36.2°@180 VDC, respectively, and the dynamic tilting angles increases linearly with the driving voltage. Device B with a 10 mm-length square mirror provides the accessible tilting angles of ±36.0°@200 VDC and ±35.9°@180 VDC for horizontal and diagonal actuations, respectively. In the dynamic actuation regime, the orthogonal and diagonal tilting angles at 10 Hz are ±8.1°/Vpp and ±8.9°/Vpp, respectively. This work confirmed that the Union Jack-shaped arrangement of trapezoidal actuators is a promising option for designing powerful optical devices.

Development of a Novel Guided Wave Generation System Using a Giant Magnetostrictive Actuator for Nondestructive Evaluation.

As a common approach to nondestructive testing and evaluation, guided wave-based methods have attracted much attention because of their wide detection range and high detection efficiency. It is highly desirable to develop a portable guided wave testing system with high actuating energy and variable frequency. In this paper, a novel giant magnetostrictive actuator with high actuation power is designed and implemented, based on the giant magnetostrictive (GMS) effect. The novel GMS actuator design involves a conical energy-focusing head that can focus the amplified mechanical energy generated by the GMS actuator. This design enables the generation of stress waves with high energy, and the focusing of the generated stress waves on the test object. The guided wave generation system enables two kinds of output modes: the coded pulse signal and the sweep signal. The functionality and the advantages of the developed system are validated through laboratory testing in the quality assessment of rock bolt-reinforced structures. In addition, the developed GMS actuator and the supporting system are successfully implemented and applied in field tests. The device can also be used in other nondestructive testing and evaluation applications that require high-power stress wave generation.

Measurement of the Length of Installed Rock Bolt Based on Stress Wave Reflection by Using a Giant Magnetostrictive (GMS) Actuator and a PZT Sensor.

Rock bolts, as a type of reinforcing element, are widely adopted in underground excavations and civil engineering structures. Given the importance of rock bolts, the research outlined in this paper attempts to develop a portable non-destructive evaluation method for assessing the length of installed rock bolts for inspection purposes. Traditionally, piezoelectric elements or hammer impacts were used to perform non-destructive evaluation of rock bolts. However, such methods suffered from many major issues, such as the weak energy generated and the requirement for permanent installation for piezoelectric elements, and the inconsistency of wave generation for hammer impact. In this paper, we proposed a portable device for the non-destructive evaluation of rock bolt conditions based on a giant magnetostrictive (GMS) actuator. The GMS actuator generates enough energy to ensure multiple reflections of the stress waves along the rock bolt and a lead zirconate titantate (PZT) sensor is used to detect the reflected waves. A new integrated procedure that involves correlation analysis, wavelet denoising, and Hilbert transform was proposed to process the multiple reflection signals to determine the length of an installed rock bolt. The experimental results from a lab test and field tests showed that, by analyzing the instant phase of the periodic reflections of the stress wave generated by the GMS transducer, the length of an embedded rock bolt can be accurately determined.

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold.

A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors' best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT-BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.

Concrete Infill Monitoring in Concrete-Filled FRP Tubes Using a PZT-Based Ultrasonic Time-of-Flight Method.

Concrete-filled fiber-reinforced polymer tubes (CFFTs) have attracted interest for their structural applications in corrosive environments. However, a weak interfacial strength between the fiber-reinforced polymer (FRP) tube and the concrete infill may develop due to concrete shrinkage and inadequate concrete compaction during concrete casting, which will destroy the confinement effect and thereby reduce the load bearing capacity of a CFFT. In this paper, the lead zirconate titanate (PZT)-based ultrasonic time-of-flight (TOF) method was adopted to assess the concrete infill condition of CFFTs. The basic idea of this method is that the velocity of the ultrasonic wave propagation in the FRP material is about half of that in concrete material. Any voids or debonding created along the interface between the FRP tube and the concrete will delay the arrival time between the pairs of PZT transducers. A comparison of the arrival times of the PZT pairs between the intact and the defected CFFT was made to assess the severity of the voids or the debonding. The feasibility of the methodology was analyzed using a finite-difference time-domain-based numerical simulation. Experiments were setup to validate the numerical results, which showed good agreement with the numerical findings. The results showed that the ultrasonic time-of-flight method is able to detect the concrete infill condition of CFFTs.