Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

With the advancement of technologies such as the Internet of Things (IoT), deploying Deep Neural Network (DNN) models on edge devices like microcontrollers has become one of the most intriguing areas in the field of AI. However, seamlessly deploying and operating these models in extremely constrained environments poses significant challenges. Despite these limitations, microcontrollers are often utilized due to their ability to operate with low power consumption for specific tasks. Deep learning models typically consist of numerous computationally intensive convolutional layers, with the RGB channels contributing to the highest peak memory usage and overhead. In this paper, we propose an efficient CNN kernel algorithm that enables deep learning models to operate effectively on resource-constrained microcontroller architectures through network compression. By focusing on low-power ARM Cortex-M7 based systems, we analyze the balance between key metrics such as memory consumption, execution time, and speed in relation to various parameters through exploration. Our proposed CNN algorithm reduces temporary memory overhead by at least the square of the kernel size compared to the im2col-based convolution implementation. Furthermore, it demonstrates a speedup of 1.15 times for CNNs, even with modifications to a single layer.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.

Social Overhead Capital (SOC) component deteriorates over time due to environmental factors, leading to reduced performance, increased maintenance costs, and potential risks to public safety. To ensure structural integrity and functionality, regular inspection and monitoring are essential. Infrared Thermography (IRT) is a widely used nondestructive testing (NDT) technique that visualizes radiated energy from surfaces, making it an effective method for detecting defects, damage, and degradation in SOC component. This review provides a comprehensive overview of NDT techniques and systematically examines IRT based monitoring technologies for SOC component. Specifically, it investigates and analyzes the latest research on IRT applications in bridges, roads, buildings, tunnels, and railways. Furthermore, this study identifies the limitations of conventional IRT techniques and explores potential advancements to enhance their effectiveness. In particular, the integration of artificial intelligence (AI), multimodal sensing technologies, and robotic platforms is discussed as a promising direction for improving the accuracy and efficiency of IRT based monitoring of SOC components. This review serves as a foundation for the development of intelligent and automated IRT based inspection technologies, contributing to the establishment of effective SOC component maintenance strategies.