A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring.

Sensor fusion is becoming increasingly popular in condition monitoring. Many studies rely on a fusion-level strategy to enable the most effective decision-making and improve classification accuracy. Most studies rely on feature-level fusion with a custom-built deep learning architecture. However, this may limit the ability to use the widely available pre-trained deep learning architectures available to users today. This study proposes a new method for sensor fusion based on concepts inspired by image fusion. The method enables the fusion of multiple and heterogeneous sensors in the time-frequency domain by fusing spectrogram images. The method's effectiveness is tested with transfer learning (TL) techniques on four different pre-trained convolutional neural network (CNN) based model architectures using an original test environment and data acquisition system. The results show that the proposed sensor fusion technique effectively classifies device faults and the pre-trained TL models enrich the model training capabilities.

Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence.

Early fault detection and real-time condition monitoring systems have become quite significant for today's modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers' cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system's Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.

Development of a novel nanoindentation technique by utilizing a dual-probe AFM system.

A novel instrumentation approach to nanoindentation is described that exhibits improved resolution and depth sensing. The approach is based on a multi-probe scanning probe microscopy (SPM) tool that utilizes tuning-fork based probes for both indentation and depth sensing. Unlike nanoindentation experiments performed with conventional AFM systems using beam-bounce technology, this technique incorporates a second probe system with an ultra-high resolution for depth sensing. The additional second probe measures only the vertical movement of the straight indenter attached to a tuning-fork probe with a high spring constant and it can also be used for AFM scanning to obtain an accurate profiling. Nanoindentation results are demonstrated on silicon, fused silica, and Corning Eagle Glass. The results show that this new approach is viable in terms of accurately characterizing mechanical properties of materials through nanoindentation with high accuracy, and it opens doors to many other exciting applications in the field of nanomechanical characterization.

Piezo1 regulates mechanotransductive release of ATP from human RBCs.

Piezo proteins (Piezo1 and Piezo2) are recently identified mechanically activated cation channels in eukaryotic cells and associated with physiological responses to touch, pressure, and stretch. In particular, human RBCs express Piezo1 on their membranes, and mutations of Piezo1 have been linked to hereditary xerocytosis. To date, however, physiological functions of Piezo1 on normal RBCs remain poorly understood. Here, we show that Piezo1 regulates mechanotransductive release of ATP from human RBCs by controlling the shear-induced calcium (Ca(2+)) influx. We find that, in human RBCs treated with Piezo1 inhibitors or having mutant Piezo1 channels, the amounts of shear-induced ATP release and Ca(2+) influx decrease significantly. Remarkably, a critical extracellular Ca(2+) concentration is required to trigger significant ATP release, but membrane-associated ATP pools in RBCs also contribute to the release of ATP. Our results show how Piezo1 channels are likely to function in normal RBCs and suggest a previously unidentified mechanotransductive pathway in ATP release. Thus, we anticipate that the study will impact broadly on the research of red cells, cellular mechanosensing, and clinical studies related to red cell disorders and vascular disease.