İlaç endüstrisi plansız duruşlara en hassas endüstrilerin başında gelir. Bu vaka analizimizde bir klima santralindeki rulman ve aktarma elemanın arızasını önceden nasıl belirlediğimize daha yakından bakıyoruz. Webinar..
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Where continuous operation is crucial for maximum productivity, any failure in the smelting process can lead to substantial production delays and significant material costs. Artesis emerges as the ideal ally for the iron and steel industry, offering unparalleled condition monitoring tailored for challenging environments.
Artesis is here to support the energy sector with our advanced system for monitoring and predicting issues. In an industry where a steady power supply is crucial, our technology plays a key role in ensuring things run smoothly. Our special software is great at spotting potential problems early in rotating equipment, acting like an important early warning system. This is especially important in the energy sector, where unexpected breakdowns can lead to disruptive power outages affecting businesses, homes, and critical infrastructure. Artesis significantly contributes to the reliability of the energy sector by offering an effective predictive maintenance solution. By looking at patterns over time, our technology gives an early sign of possible issues. This helps grid operators plan ahead, taking actions to prevent power disruptions and ensuring a constant energy supply to industries and communities.
The logistic and transportation industry faces significant challenges when it comes to downtime costs, with unplanned disruptions leading to schedule delays, missed delivery dates, and additional charges. Artesis plays a crucial role in minimizing these challenges by providing continuous monitoring of asset health, offering timely insights into the need for repairs.
Artesis introduces an advanced Predictive Maintenance (PdM) solution tailored for the automotive industry. Our system, Artesis e-MCM, is designed for simplicity, requiring minimal installation efforts and offering low start-up costs. With no need for sensor installation on equipment and the ability to function in non-EX areas, Artesis e-MCM is an ideal solution for inaccessible automotive equipment.
The food and beverage industry faces unique challenges, particularly concerning hygienic standards and safety. Artesis condition monitoring products emerge as a solution, offering a distinctive advantage—no sensor installation on the equipment itself. This not only ensures uncompromised hygiene but also addresses safety concerns seamlessly.
Artesis’s condition monitoring and predictive maintenance solutions offer a comprehensive approach, encompassing failure prediction, fault diagnosis, failure-type classification, and recommendations for relevant maintenance actions. This empowers pharmaceutical equipment manufacturers, engineers, and process plant owners and operators to proactively identify and address potential issues, enhancing the efficiency of their maintenance services and improving equipment uptime.
Artesis empowers water companies with continuous monitoring of aerators, centrifugal pumps, positive displacement pumps, mixers, clarifiers, and submerged equipment, preventing potential failures and optimizing asset performance and energy consumption. In the water industry, system downtime is both disruptive and costly. Artesis’s predictive maintenance and power monitoring solutions enhance operational efficiencies, prevent unplanned downtime, and extend the economic life of critical assets.
Knowing how rotating equipment is performing, predicting failures, improving asset utilization, and finding productivity enhancements in challenging environments are particular challenge for Oil & Gas companies. Artesis helps oil and gas companies predict their rotating equipment failures and prevents the unplanned downtime where the cost of downtime is extremely high. When oil and gas operations break down, it can have a significant domino effect on energy supply – making predictive maintenance technology a key ally for companies in the sector.
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