Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Quantify pixel-level detection of dam surface crack using deep learning

An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

An in-situ conductometric apparatus for physicochemical characterization of solutions and in-line monitoring of separation processes at elevated temperatures and pressures

Specific conductance and frequency-dependent resistance (impedance) data are widely utilized for understanding the physicochemical characteristics of aqueous and non-aqueous fluids and for evaluating the performance of chemical processes. However, the implementation of such an in-situ probe in high-temperature and high-pressure environments is not trivial. This work provides a description of both the hardware and software associated with implementing a parallel-type in-situ electrochemical sensor. The sensor can be used for in-line monitoring of thermal desalination processes and for impedance measurements in fluids at high temperature and pressure. A comparison between the experimental measurements on the specific conductance in aqueous sodium chloride solutions and the conductance model demonstrate that the methodology yields reasonable agreement with both the model and literature data. A combination of hardware components, a softwarebased correction for experimental artifacts, and computational fluid dynamics (CFD) calculations used in this work provide a sound basis for implementing such in-situ electrochemical sensors to measure frequency-dependent resistance spectra.

Integrity monitoring for undifferenced and uncombined PPP under the local environment

Integrity monitoring of precise point positioning (PPP) can provide tightly guaranteed absolute position error bounds for safety-critical applications. However, complex local environment makes PPP integrity monitoring much more challenging, such as urban canyons. Significant prone multipaths and low observation redundancy are main difficulties to the accuracy and the reliability of PPP. Therefore, we proposed a solution separation-based integrity monitoring algorithm, which is based on a single and dual frequency-mixed undifferenced and uncombined PPP model considering compensation for the multipath error distortion by Gaussian overbounding. Both the static and the kinematic data are utilized to test the proposed algorithm. The results show that the proposed algorithm can produce adequate protection level in horizontal and vertical directions. Furthermore, the proposed algorithm can obtain smoother protection level and positioning error under the dynamic local environment, and effectively suppress the misleading information.

Covariance matrix transformation method in all-earth integrated navigation considering coordinate frame conversion

In the exploration of polar region, navigation is one of the most important issues to be resolved. To avoid the limitations of single navigation coordinate frame, the navigation systems usually use different navigation coordinate frames in polar and nonpolar region, such as the north-oriented geographic frame and the grid frame. However, the error states and covariance matrix are related with the definition of navigation coordinate frame, since the coordinate frame conversion will cause the integrated navigation Kalman filter overshoot and error discontinuity. To solve this problem, the transformation relationship of error states defined in different frames is deduced, whereby the covariance matrix transformation relationship is also analyzed. On this basis, covariance transformation-based the open-loop and the closed-loop Kalman filter integrated navigation algorithms are proposed. The effectiveness of algorithms is verified by flight tests with rotational strapdown inertial navigation system (RSINS)/global navigation satellite system (GNSS) integrated navigation system.

A dual-ended 400 km OFDR for vibration detection

Optical frequency-domain reflectometer (OFDR) has been widely used in vibration detection because of its unique advantages of simple configuration and high spatial resolution. Based on remote fiber amplification, an unrepeatered OFDR is experimentally investigated for vibration monitoring. To locate the vibration, we present an algorithm by calculating segmental cross-correlation between the beating signals with and without disturbances on the sensing fiber. It is shown that the OFDR demonstrates the ability of detecting the vibration over 222 km testing distance (112 km + 110 km). After sensing the first spool fiber of 112 km, the remnant laser is amplified by a remote-pumped EDFA before proceeding to probe the vibration in the second spool one of 110 km. To be specific, the PZT-induced vibrations positioned at z=110.9 km and z=220.9 km are both detected. More importantly, the OFDR system can be extended to operate in bi-directional sensing mode and to double detection range from 200 km to 400 km.

Robust phase unwrapping algorithm based on Zernike polynomial fitting and Swin-Transformer network

Phase unwrapping plays an important role in optical phase measurements. In particular, phase unwrapping under heavy noise conditions remains an open issue. In this paper, a deep learning-based method is proposed to conduct the phase unwrapping task by combining Zernike polynomial fitting and a Swin-Transformer network. In this proposed method, phase unwrapping is regarded as a regression problem, and the Swin-Transformer network is used to map the relationship between the wrapped phase data and the Zernike polynomial coefficients. Because of the self-attention mechanism of the transformer network, the fitting coefficients can be estimated accurately even under extremely harsh noise conditions. Simulation and experimental results are presented to demonstrate the outperformance of the proposed method over the other two polynomial fitting-based methods. This is a promising phase unwrapping method in optical metrology, especially in electronic speckle pattern interferometry.

Median line-gram and its application in the fault diagnosis of rolling bearing

The level selection of frequency band division structure relies on previous information in most gram approaches that capture the optimal demodulation frequency band (ODFB). When an improper level is specified in these approaches, the fault characteristic information contained in the produced ODFB may be insufficient. This research proposes a unique approach termed median line-gram (MELgram) to tackle the level selection problem. To divide the frequency domain signal into a series of demodulation frequency bands, a spectrum median line segmentation model based on Akima interpolation is first created. The level and boundary of the segmentation model can be adaptively identified by this means. Second, the acquired frequency bands are quantized using the negative entropy index, and the ODFB is defined as the frequency band with the largest value. Third, the envelope spectrum is used to determine the ODFB characteristic frequency to pinpoint the bearing fault location. Finally, both simulation and experimental signal analysis are used to demonstrate the efficiency of the suggested method. Furthermore, the suggested method extracts more defect feature information from the ODFB than existing methods.

Performance enhancement of Scanning Electron Microscope using a Deep Convolutional Neural Network

We report noise reduction and image enhancement in Scanning Electron Microscope (SEM) imaging while maintaining a Fast-Scan rate during imaging, using a Deep Convolutional Neural Network (D-CNN). SEM images of non-conducting samples without conducting coating always suffer from charging phenomenon, giving rise to SEM images with low contrast or anomalous contrast and permanent damage to the sample. One of the ways to avoid this effect is to use Fast-Scan mode, which suppresses the charging effect fairly well. Unfortunately, this also introduces noise and gives blurred images. The D-CNN has been used to predict relatively noise-free images as obtained from a Slow-Scan from a noisy, Fast-Scan image. The predicted images from D-CNN have the sharpness of images obtained from a Slow-Scan rate while reducing the charging effect due to images obtained from Fast-Scan rates. We show that using the present method, and it is possible to increase the scanning rate by a factor of about seven with an output of image quality comparable to that of the Slow-Scan mode. We present experimental results in support of the proposed method.

Investigation of the stability of graphene devices for quantum resistance metrology at direct and alternating current

Interlaboratory comparisons of the quantized Hall resistance are essential to verify the international coherence of primary impedance standards. Here we report on the investigation of the stability of p-doped graphene-based quantized Hall resistance devices at direct and alternating currents at CMI, KRISS, and PTB. To improve the stability of the electronic transport properties of the polymer encapsulated device, it was shipped in an over-pressurized transport chamber. The agreement of the quantized resistance with RK/2 at direct current was on the order of 1 nΩ/Ω between 3.5 T and 7.5 T at a temperature of 4.2 K despite changes in the carrier density during the shipping of the devices. At alternating current, the quantized resistance was realized in a double-shielded graphene Hall device. Preliminary measurements with digital impedance bridges demonstrate the good reproducibility of the quantized resistance near the frequency of 1 kHz within 0.1 μΩ/Ω throughout the international delivery.