Predictive Maintenance (PdM): Benefits & How it Works in 2024

Predictive maintenance workflow diagram

Predictive maintenance has emerged as one of the most valuable AI applications for industrial companies, providing substantial cost savings, improved uptime, and better asset management. In this comprehensive guide, we‘ll explore what predictive maintenance is, the key benefits it delivers, how to implement a predictive maintenance program, and the vendor landscape to be aware of.

What is Predictive Maintenance and Why it Matters

Predictive maintenance refers to performing maintenance on equipment to prevent predicted failures or issues, rather than just running maintenance on a schedule or fixing things when they break.

Technician conducting predictive maintenance

It relies on monitoring equipment sensor data like temperature, pressure, vibration, and corrosion to identify issues before they cause downtime. Advanced analytics and AI algorithms spot patterns in the data to predict which components or machines will need repairs or replacement.

This predictive approach prevents unexpected downtime and minimizes maintenance costs by only servicing equipment on an as-needed basis. According to PwC, implementing predictive maintenance in manufacturing could:

  • Improve uptime by 9%
  • Reduce maintenance costs by 12%
  • Extend asset lifetimes by 20%

With results like these, it‘s no wonder interest in predictive maintenance has doubled since 2017. The ability to maximize uptime and asset utilization is invaluable for industrial companies where revenue depends on equipment functioning properly.

Evolution of Maintenance Practices

Maintenance strategies have evolved over time along with advancements in technology and data processing capabilities.

Manual Analysis

In the past, lean and six sigma teams would collect equipment data, analyze it manually in spreadsheets, and try to uncover insights and improvements. This approach was labor intensive, not scalable, and relied heavily on the skills of individuals.


Introduction of computerized maintenance management systems (CMMS) allowed for tracking work orders, technician logs, and maintenance history digitally instead of paper-based. However, CMMS platforms offered limited data analytics capabilities historically. Modern CMMS systems like eMaint now provide robust analytics.

Virtual Twins

Engineers can create digital twin models of equipment based on first principles or historical data to predict failures. However, this approach requires special expertise and is costly to build out across an entire facility.

AI/ML Predictive Systems

Current best practice is using AI-powered predictive maintenance software that can learn from data across all equipment types. Technicians provide feedback to train the algorithms. No costly data science team needed!

Major Industries Benefiting from Predictive Maintenance

Though applicable across many sectors, these industries stand to gain the most from implementing predictive maintenance:

Manufacturing – Minimizing downtime is critical with tightly integrated assembly lines. Predictive maintenance maximizes production efficiency.

Oil & Gas – Ensuring continuous operation and preventing disasters like deep sea oil spills have huge economic and environmental impacts.

Transportation – Complex vehicles like airplanes and trains involve lots of sensor monitoring where predictive maintenance adds safety.

Automotive – Car assembly plants want to limit downtime. Self-driving vehicles also need predictive maintenance to avoid breakdowns.

How to Implement a Predictive Maintenance Program

Follow these steps to roll out a predictive maintenance program:

  1. Select software – Choose a purpose-built predictive maintenance solution with machine learning capabilities. Going with a vendor simplifies setup vs. building in-house.

  2. Integrate sensors – Connect equipment sensors to the software via APIs or industrial IoT devices. More sensors means better insights.

  3. Monitor data – Software analyzes sensor data in real-time to detect anomalies and predict failures before they happen.

  4. Perform predictive maintenance – Technicians receive alerts to conduct maintenance on specific components predicted to need service.

  5. Improve algorithms – Over time, algorithms learn from maintenance logs and sensor data to make better predictions.

Here is a typical workflow:

Predictive maintenance workflow diagram

Photo Credit: UpKeep

Overview of Predictive Maintenance Vendors

If going the vendor route, here are the main players in the space:

Software Leaders – IBM, SAP, SAS have strong software but lack domain expertise.

Industrial Automation Leaders – Siemens, GE, Honeywell build the equipment but can lack software focus.

Specialists – Startups like Falkonry, Augury, SparkCognition focus exclusively on industrial analytics. Quick to deploy.

Cutting Edge Startups – New entrants aim to differentiate with advanced ML and niche industry focuses.

When evaluating vendors, prioritize ease of integration, ML capabilities, and industry expertise.

Pricing Models

Predictive maintenance solutions usually follow one of these pricing models:

  • Fixed + Variable – Fixed fee for initial setup and variable fee per sensor/asset monitored. This matches most vendor cost structures.

  • Value-Based – Price is scaled based on measurable value generated. More accurate but complex to implement.

The Future of Maintenance with AI

Predictive maintenance is just one example of how artificial intelligence is transforming operations and manufacturing. As machines get smarter, so too will maintenance practices.

While preventative maintenance was time-based and reactive maintenance was failure-based, predictive maintenance is data-based, only calling for service when algorithms identify a high risk asset.

Someday truly intelligent, self-learning maintenance systems could become standard. Machines that fix themselves – now that‘s an exciting possibility!