The study introduces the Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF) containing sound and vibration data from two motors across 1080 conditions. It proposes a deep learning approach for sound-based fault detection, addressing limitations of vibration-based methods. Experiments on QU-DMBF show sound-based detection is more robust, independent of sensor location, and cost-effective while matching vibration-based performance. Why it matters: The new dataset and findings could shift the focus toward sound-based methods for more reliable and accessible predictive maintenance in industrial settings.
This paper presents a 2-D convolutional neural network (CNN) approach for damage detection in steel frame structures, using raw acceleration signals as input. The method employs a network of lightweight CNNs, each optimized for a specific element, to enhance accuracy and speed. The proposed framework is validated using the Qatar University Grandstand Simulator (QUGS) benchmark data. Why it matters: The research offers a promising AI-driven solution for real-time structural health monitoring, with potential applications for infrastructure maintenance and safety in the GCC region.
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
Paul Liang from CMU presented on machine learning foundations for multisensory AI, discussing a theoretical framework for modality interactions. The talk covered cross-modal attention and multimodal transformer architectures, and applications in mental health, pathology, and robotics. Liang's research aims to enable AI systems to integrate and learn from diverse real-world sensory modalities. Why it matters: This highlights the growing importance of multimodal AI research and its potential for advancements across various sectors in the region, including healthcare and robotics.
Song Chaoyang from the Southern University of Science and Technology (SUSTech) presented research on Vision-Based Tactile Sensing (VBTS) for robot learning, combining soft robotic design with learning algorithms to achieve state-of-the-art performance in tactile perception. Their VBTS solution demonstrates robustness up to 1 million test cycles and enables multi-modal outputs from a single, vision-based input, facilitating applications such as amphibious tactile grasping and industrial welding. The talk also highlighted the DeepClaw system for capturing human demonstration actions, aiming for a universal interaction interface. Why it matters: This research advances embodied intelligence by improving robot dexterity and adaptability through enhanced tactile sensing, which is crucial for complex manipulation tasks in various sectors such as manufacturing and healthcare within the region.