Industrial Health Monitoring Research Articles

AI-powered health monitoring of anode baking furnace pits in aluminum production using autonomous drones

Industrial health monitoring in factories is essential for quality assurance, energy and cost reduction, and health and safety. In aluminum factories, anode furnace pits’ flue walls deform over time due to cyclic heating and cooling. They are inspected and classified using manually acquired measurements in a process that takes several hours and is done under high temperatures using specialized equipment. We propose an end-to-end AI-powered system for automated inspection using drones. We fly a drone carrying color and depth cameras to film and navigate a 50-pit furnace floor autonomously in a GPS-denied environment. We then mosaic the recorded videos to produce color and depth mosaics using frame-to-frame motion parameters estimated using the color videos and applied to both. We finish the mosaics using depth-to-color mosaic registration based on maximizing mutual information on gradients. We extract pit images from the mosaics using a YOLOv5 object detection initially trained using a physical floor model with a proposed data augmentation scheme and then fine-tuned for the on-site environment. We achieve a mean average precision of 94.7%. Once pit images are detected and initially classified, we propose a dual-stage segmentation algorithm using the Hough transform and a semantic segmentation network trained using probabilistic feature images with a precision of 96.67%. Segmented pits with depth information allow us to produce 3D models of pits to aid in temporal monitoring and diagnosis confirmation. Our system is cost-effective and reduces inspection downtime by 87%, eliminating the need for human intervention.

Design, development of MQ TELEMETRY TRANSPORT-sensor network interface protocol for the sensor device and IoT edge gateway

Abstract The Internet of things is gaining a lot of interestfrom day to day forvarious applications like Smart cities, Industrial health monitoring, smart homes, etc. IoT systems consist of sensor devices with communication capabilities, actuators, edgegateways, Back end Servers, and IoT applications. The Ultimate goal of an IoT network is to collect sensor data, reliable Transfer sensor data to the backend serverthrough the edgegateways. Back end server delivers the sensor data to the IoT Applications. IoT application process the Sensor data and apply the appropriate decision. Reliable Transfer of sensor data tothe Backend server isessential considering wireless sensor network characteristics, low computation power, and sensor devices' bandwidth. The IoT network deployment signaling protocol plays a crucial role in communicating sensor data to the IoT program. Various application messaging protocols are currently available, like MQ TELEMETRY TRANSPORT, COAP, HTTPetc. These protocols aredesigned, developed considering richcomputation powerand bandwidth. These protocolsare notenergy efficientfor sensor devices. MQ TELEMETRY TRANSPORT-SN protocol specified for sensor devices considering wireless sensor networks. In this article, Message Telemetry Transfer Protocol-sensor networks were planned, built, and validated according to the MQ TELEMETRY TRANSPORT-SN specification.

Review of the Current Application of Nonlinear Acoustics and Time Reversal Acoustics in Material Defects Identification

The basic principles of nonlinear acoustics and time reversal acoustics are briefly introduced and their applications in material NDT are reviewed and discussed.It is pointed out that nonlinear acoustic wave is sensitive to defects in solid media and sonic energy in solid media can be focused on any points by using time reversal acoustics.Identifying and lo cating defects precisely and accurately based on the defect sensitivity of nonlinear acoustic wave and self-focusing characteristics of time reversal acoustic wave has become a new trend in the research field of material NDT and will have a bright application prospect in industrial health monitoring,medical diagnosis,underwater communication and seismic analysis.

Computerization of medical and exposure records.

An interdisciplinary team of occupational health professionals and computer engineers at Digital Equipment Corporation have developed a computerized system for maintaining employee medical and exposure records. The Industrial Health Monitoring System (IHMS) was designed to meet the occupational and public health needs of employees engaged in semiconductor manufacturing operations where exposure to potentially hazardous chemicals and physical agents exists. The purpose of the IHMS is to systematically retrieve and analyze data collected from industrial hygiene, health services, and clinical diagnostic testing in order to monitor the general health of the working community.

Health monitoring in industry: medical considerations.

MPROVED TECHNIQUES in monitoring the atmosphere and the worker have facilitated the task of the occupational physician in the prevention and early diagnosis of industrial disease. There is still, however, much to be done, as in the case of many chemicals there is as yet insufficient knowledge of their harmful potential — in particular of what level of concentration, if any, is sufficiently small as to render them harmless.