Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality information and update it on the cloud. The developed long-range wide area network (LoRaWAN)-based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness. The system is composed of multiple sensors (NO 2 , SO 2 , CO 2 , CO, PM 2.5 , temperature, and humidity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network (TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable operation. Our system simultaneously gathers the considered air quality information by using the smart sensing unit. Then, the system transmits the information through the gateway to the TTN platform, which is integrated with the ThingSpeak IoT server. This action updates the collected data and displays these data on a developed Web-based dashboard and a Graphical User Interface (GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-AQMS are validated by comparing them with experimental results based on the high-technology Aeroqual air quality monitoring devices. Our system can reliably monitor various air quality indicators and efficiently transmit the information in real time over the Internet.

Bones are continuously remodeled (resorbed and regenerated) to allow fracture healing and skeleton adaptation to stress. When excessive resorption occurs, bone microstructure is deteriorated, leading to osteoporosis. At early stages, osteoporosis usually has no symptoms; most people are diagnosed when a fracture occurs due to disease severity. To prevent fractures, technologies have been developed to identify high risk population eligible to treatment. Fracture risk has been assessed by analyzing the interaction of different energy stimulus with bone tissues as well as by statistical models that evaluate multiple clinical risk factors. The most applied methods are Dual-energy X-ray Absorptiometry and Fracture Risk Assessment tool. As they present some limitations, other technologies have been proposed for such purpose. A survey of the currently applied and emerging methods is here presented in order to provide a scenario of the technological challenges and trends to diagnose osteoporosis.

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

In the condition monitoring of rotating systems, overfitting is a common challenge due to limited data history, which reduces the effectiveness of fault detection frameworks; this limitation often leads to unreliable diagnostics, resulting in unexpected machine failures and increased operational costs in industrial applications. Advances in deep learning suggest using simulated data to address this issue, but operational variabilities still cause significant data distribution shifts, affecting model accuracy. This article presents a new vibration-based monitoring framework that improves fault detection in rotary machines by effectively managing these shifts. It features a novel fine-tuning approach within sequential domain adaptation, requiring only a limited number of observations from the target domain for accurate model adjustment. The domain adaptation process is elucidated through a novel visualization of internal activation patterns within the sequential network. This method is further enhanced by a hybrid algorithm that combines wavelet transformation, a multi-layer perceptron, and a transformer encoder, followed by domain-specific fine-tuning. The framework’s effectiveness is demonstrated through experimental data from two different rotor systems, validated by sensitivity and comparative analyses, highlighting its robustness, generalizability, and practical applicability as a baseline in industrial fault detection scenarios.