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The Next Big Leap in Asset Management Comes with Predictive Maintenance at Scale
Predictive maintenance (PdM) is a group of technologies that help manufacturers predict when maintenance should be performed and avoid unplanned incidents. Organizations don't always use the most advanced PdM technologies, however. Many already employ basic data driven PdM solutions including rudimentary types of anomaly detection with enterprise asset management (EAM) systems.
In contrast, advanced PdM solutions leverage AI and machine learning (ML) to help manufacturers analyze very large datasets of process parameters over time. Advanced PdM can also leverage historical asset data to predict impending failure sooner and act immediately before opportunity passes.
This whitepaper describes what advanced PdM is and why it's time to move from basic asset tracking and monitoring to AI-based PdM. Download the whitepaper to learn how you can use advanced PdM to benefit from better analysis of real-time issues with data and enable more nuanced and cost-effective maintenance.
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Predictive maintenance (PdM) refers to strategies and technologies that help manufacturers monitor and evaluate the condition of their assets to predict when maintenance should be performed. It involves data analysis to anticipate asset failures, utilizing advanced technologies like artificial intelligence (AI) and machine learning (ML) to analyze large datasets of process parameters over time.
What challenges do manufacturers face with PdM?
Manufacturers face several challenges in implementing predictive maintenance, including a significant skills gap due to retiring subject matter experts and the difficulty in attracting new talent. Additionally, integrating PdM applications with existing systems is often cited as a major hurdle, with over half of respondents in an APM survey indicating this as their biggest challenge.
How can advanced technologies enhance PdM?
Advanced technologies such as AI and cloud computing enhance predictive maintenance by enabling comprehensive analysis of large datasets, improving anomaly detection, and facilitating real-time monitoring. These technologies help manufacturers automate data gathering and develop reliable predictive models, ultimately leading to better maintenance scheduling and reduced downtime.
The Next Big Leap in Asset Management Comes with Predictive Maintenance at Scale
published by PRIVAXI
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