Reliability Centered Maintenance with Machine Learning

Predictive Maintenance is not about predicting failures – it’s about implementing a risk-based maintenance strategy

Use of Machine Learning (ML) for predictive and prescriptive maintenance can indeed be transformative to build reliable manufacturing operations. But few have been able to realize value from these investments.

Reliability Centered Maintenance (RCM) the most universal and practical approach for reliability is founded on risk-based asset reliability monitoring. Machine learning enables early detectability of random failure modes to provide real-time and predictive risk monitoring.

Making machine learning simply another tool to be used by Reliability and maintenance practitioners is the fastest path to achieving ROI from digitalization of RCM.

Introducing the only digital implementation of RCM with Machine Learning

Avoid long, expensive predictive maintenance pilots projects, and start running your reliability program with ML

“With the Quartic digital RCM application, our maintenance and reliability practitioners were able to turn FMEA’s into failure mode monitoring agents that are monitoring every critical failure mode. We now make all our maintenance decisions based on risk alerts, we our well on our way to eliminating route- based monitoring”
- Fortune 100 Food & Beverage company


  • Accelerate RCM deployment and keep RCM implementation evergreen
  • Move to evidence and risk-based downtime and spares planning
  • Increase ROI on condition-based monitoring investments


  • Reliability and maintenance practitioners can use no-code machine learning to turn FMEA’s into asset health monitoring agents
  • Baseline and monitor asset performance without historical failures
  • Monitor real-time and predictive abnormal asset operation to drive maintenance decisions

Additional Content

Machine Learning to Play Key RCM Role

In this article by Efficient Plant magazine, Dr. Klaus Blache of the RMC, and Quartic founder and CEO, Rajiv Anand provide crisp guidance on the role of machine learning for RCM.

Read article →

Webinar: Successful Digitalization of Reliability Centered Maintenance (dRCM)

In this Webinar, Dr. Klaus Blache of the RMC at the University of Tennessee, Ramesh Gulati, a stalwart in maintenance and reliability, and Rajiv Anand, CEO of discuss how best to apply RCM using digitalization and machine learning.

Watch video →

Predictive Maintenance

This blog by Quartic founder and CEO, Rajiv Anand explains the evolution of predictive maintenance from Condition Based maintenance and the role of machine learning.

Read blog post →

6 Myths That Prevent Maintenance & Reliability Professionals From Initiating Machine Learning Programs

This guide will offer some new perspective for those looking to improve up-time and adopt more mature maintenance and reliability programs.

Download guide →

Download dRCM application note

Stay in touch