Predictive maintenance is revolutionizing how industries manage equipment and optimize automation processes. Using artificial intelligence (AI), this approach identifies potential faults, recommends repairs before breakdowns occur, and ensures seamless operations.
This blog explores how companies like ABB, Siemens, Mitsubishi, and Schneider Electric are utilizing predictive maintenance to enhance efficiency, reduce costs, and increase system reliability.
ABB has embraced predictive maintenance as a core part of its industrial automation strategy. Through a partnership with Viking Analytics, ABB integrated advanced vibration analysis into its operations.
Viking Analytics’ MultiViz Vibration, an AI-powered tool for condition monitoring, is now part of ABB's Ability Asset Manager. This integration has allowed industries such as iron casting to benefit from early fault detection and streamlined maintenance.
This partnership began in 2020, following Viking Analytics’ success in the ABB Electrification Startup Challenge, and continues to drive innovation in condition monitoring and automation solutions.
Siemens leverages predictive maintenance through its Xcelerator platform, which incorporates powerful tools like MindSphere and Predictive Services.
Siemens’ solutions exemplify how predictive maintenance not only minimizes downtime but also enhances product quality and process stability.
Mitsubishi Electric's Maisart platform offers compact AI solutions for predictive maintenance, specifically targeting servo systems and variable frequency drives.
Compact AI identifies wear and corrosion in critical equipment before failure occurs, ensuring smooth operations. Additionally, Mitsubishi extends its AI capabilities to machining applications by continuously adjusting parameters in real-time, enhancing processes like laser cutting.
Mitsubishi’s exploration of quantum machine learning highlights its commitment to advancing ultra-precise feature recognition for high-frequency imaging applications.
Schneider Electric’s EcoStruxure platform empowers industries with custom AI applications through edge computing. By analyzing data directly at the equipment level, Schneider’s solutions excel in optimizing legacy systems and distributed environments.
A standout example of EcoStruxure's utility is its application in oil pumpjack monitoring. Using AI-driven analysis of dynamometer data, Schneider identifies faults, leaks, and wear, drastically reducing the risk of unexpected equipment failures.
This edge-based approach ensures cost-effective, real-time monitoring and minimal operational disruptions.
Predictive maintenance represents a pivotal shift in industrial automation. By combining AI, IoT, and advanced algorithms, companies can:
From ABB's innovative collaborations to Schneider's edge computing solutions, predictive maintenance proves its value across industries.
The future of industrial automation lies in predictive maintenance. As AI-driven tools continue to evolve, businesses gain greater reliability, reduced costs, and enhanced performance across their systems.
Companies like ABB, Siemens, Mitsubishi, and Schneider Electric are leading the way, showcasing how predictive maintenance can transform industries. By adopting these intelligent solutions, organizations can stay ahead in the competitive landscape of industrial automation.
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