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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 enable manufacturers to monitor and assess the condition of their assets to predict when maintenance should be performed. By utilizing advanced technologies like artificial intelligence (AI) and machine learning (ML), PdM helps in analyzing large volumes of process data to anticipate asset failures, allowing manufacturers to take proactive measures before issues arise.
What challenges do manufacturers face in implementing PdM?
Manufacturers face several challenges in implementing PdM, including a significant skills gap as many subject matter experts retire and the demand for software engineers and data scientists increases. Additionally, integrating PdM applications with existing systems poses difficulties, as over half of manufacturers report this as their biggest challenge. Scaling PdM initiatives from pilot projects to broader applications across different assets and sites also remains a hurdle.
How can manufacturers effectively scale their PdM initiatives?
To effectively scale PdM initiatives, manufacturers should focus on automating data collection and leveraging advanced analytics solutions that facilitate the development and validation of machine learning models. Collaborating with analytics leaders who offer scalable solutions and automated pipelines can help streamline the process, enabling organizations to transition from pilot projects to full production more efficiently and capture greater value from their PdM efforts.
The Next Big Leap in Asset Management Comes with Predictive Maintenance at Scale
published by CoreHive Computing
Established in 2003, CoreHive Computing is a technology consulting and solution provider offering a wide range of IT services for public and private enterprise clients. We specialize in handling large, mission-critical, complex projects designed to provide innovative, cost effective solutions to our clients.
CoreHive helps clients design, build, and execute highly complex and compute-intensive tasks in real-time with High Performance Computing Infrastructure. We develop, deploy, and manage HPC clusters which include Hadoop, Bigdata, HPC on the Cloud, and Application Analysis using intelligent interconnected solutions for services, storage, and hyper-converged infrastructure.
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