IoT-based predictive maintenance
Predictive maintenance requires processing large amounts of data and running complex algorithms, which SCADA cannot achieve. At the same time, based onIoT solutions support parallel storage of terabytes of data on multiple computers, run machine learning algorithms to predict potential hazards, and also be able to tell when industrial equipment is about to fail.
IoT-based predictive maintenance solutions must be well-considered architecture, and user applications allow IoT-based predictive maintenance solutions to alert users to potential battery failures.
IoT-based predictive maintenance: Discrete manufacturing
Major discrete manufacturers are using IoT-based predictive maintenance to monitor the health of spindles such as milling machines . These spindles are prone to breakage and expensive to repair.
IoT-based predictive maintenance: Manufacturing process
During the production process of papermaking, pulp processing enterprises and papermaking enterprises use the Internet of Things to monitor the status of papermaking equipment. Maastricht Mill, for example, has installed temperature and vibration sensors on its press rolls and rolled out a cloud-based predictive maintenance solution to predict bearing and gear wear.
IoT-based predictive maintenance: Oil and gas
Oil and gas companies in particular benefit from predictive maintenance solutions. Physical inspections of oil and gas production equipment require personnel to enter a hazardous environment to inspect the equipment, which is not feasible in some cases. IoT-based predictive maintenance allows oil and gas companies to identify potential failures and increase oil and gas production from critical assets.
IoT-based predictive maintenance: Power industry
Power plants must ensure a reliable supply of electricity, especially during periods of peak demand. IoT-based maintenance solutions can help ensure uninterrupted power generation and monitor evolving defects in the rotating components of gas/wind/steam turbines. For this purpose, the turbines are equipped with vibration sensors.
IoT-based predictive maintenance: Railway
Railroad companies employ IoT-based predictive maintenance to ensure railroads and rolling stock are in good condition. For example, BNSF Railroad deploys dynamometers, visual cameras, infrared and sound sensors to identify defects in locomotive braking capacity, excessive wear on wheels and bearings, and damage to track curves and straights.
IoT-based predictive maintenance: Construction industry
Predictive maintenance is suitable for predictive maintenance of large machinery such as excavators, bulldozers, loaders, lifts, etc. Sensors can be attached to the machine to monitor driveshaft and brake temperature, engine speed, tire pressure, fuel consumption, and other data.
IoT-based predictive maintenance: Summarize
IoT-based predictive maintenance can extend the life of equipment, help eliminate up to 30% of time-based routine maintenance, and can also reduce equipment downtime by 50%. However, an architecture focused on machine learning is also critical for a mature and reliable predictive maintenance solution.
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