Predictive Maintenance: Is it the best approach for projects?

Digital transformation in the industry entails the creation of connected enterprises wherein machines and people can seamlessly communicate and access relevant information in real time. Intelligently digitized factories are able to achieve objectives while maximizing production uptime and maintaining optimal machine health. Where the application of smart automation, robotics, the internet of things (IoT), machine learning (ML) and artificial intelligence (AI) can make this strategically possible in new manufacturing sites, legacy sites often struggle to digitalize key operations.

This evokes the question for many plant and maintenance leaders – is IoT-enabled (PdM) suitable for greenfield (new) projects only or can brownfield (old) projects also successfully adopt it? This article will address this question and deliberate on the suitability of predictive maintenance in both greenfield and brownfield projects.

Predictive Maintenance in Greenfield Projects

To begin with, greenfield projects often have the advantage of factoring in scalable automation and digitalising processes from the off-set. The project plan accounts for such technologies and shop-floor teams are sensitized from the very beginning about the standard digitalized practices. Furthermore, there are no legacy applications to upgrade or distributed assets to integrate, calling for extensive change management.

This makes it easy for plant and maintenance heads to pursue and achieve plant reliability objectives in brand-new manufacturing sites. A digitally transformed production unit can have

All machines equipped with sensors or integrated chips to monitor ongoing operations

Real-time information about asset availability and reliability, accessible through intelligent dashboards

Sophisticated data storage and big data analytics to generate strategic insights

Established standard operating procedures (SOPs) to manage digitalized equipment with minimal manual intervention

Blanket adoption of industrial IoT, AI and machine learning (ML) enabled technologies to support production and maintenance functions

Safer work environments with minimal equipment breakdown and unplanned downtime