The Reality of Predictive Maintenance: Challenges, OEM Insights & Practical Implementation

Predictive Maintenance (PdM) is often marketed as a game-changer, promising reduced downtime and optimized maintenance schedules. However, the reality is more nuanced. AI in Predictive Maintenance has significant potential, but its success depends on several critical factors:

• Availability of high-quality historical data.
• IoT-based Predictive Maintenance solutions integrated with IWMS & CAFM systems.
• Skilled personnel to interpret predictive insights.
• Continuous refinement of AI models based on real-world feedback.

Instead of diving headfirst into AI-driven PdM, organizations should take a pragmatic approach, balancing data-driven insights with traditional maintenance strategies.

Asset Variability
No two assets operate under identical conditions. Equipment type, manufacturer specifications, installation environments, and usage patterns all influence failure rates. A one-size-fits-all predictive model may not work effectively across different assets.

Data Scarcity for New Equipment
Newly installed assets lack historical failure data, making it difficult for AI-driven Predictive Maintenance to generate accurate predictions. As a result, organizations must rely on reactive or preventive maintenance until sufficient data is available.

Dependence on Historical Data
AI models require large volumes of past failure data to train effectively. Without this, predictions may lead to unnecessary maintenance costs or unexpected failures. Organizations need a strategy to gradually build and refine their predictive models over time.

Original Equipment Manufacturers (OEMs) are critical to successful IoT-based Predictive Maintenance, providing:

• Centralized Data Collection: OEMs gather data from thousands of global deployments, identifying common failure patterns and defining threshold values.
• OEM Guidelines as a Baseline: Many organizations follow OEM-recommended maintenance schedules based on aggregated data. These include:
  • Threshold values for temperature, vibration, and performance metrics.
  • Wear-and-tear trends across various operating environments.
  • Alerts for deviations based on factory-set conditions.
• Challenges with OEM Data: While OEMs provide standardized benchmarks, they don’t account for site-specific conditions like climate, operational load, or facility-specific processes. This can lead to over-maintenance (higher costs) or under-maintenance (unexpected failures).

To overcome these Predictive Maintenance challenges, organizations should adopt a hybrid strategy that blends traditional maintenance practices with AI in Predictive Maintenance capabilities.

Start with OEM Guidelines
Leverage OEM recommendations as an initial framework, then refine them with facility-specific data over time.

Implement IoT-Based Condition Monitoring
Instead of full-scale AI-driven PdM, deploy IoT sensors to track key parameters like temperature, vibration, and energy consumption. These sensors trigger alerts for anomalies rather than attempting to predict failures years in advance.

Gradually Integrate AI & Machine Learning

• First, collect operational data through IoT-based Predictive Maintenance solutions.
• Once a sufficient dataset is available, train AI models to enhance predictive accuracy.

Collaborate with OEMs for Predictive Insights
Partner with OEMs to access aggregated failure insights and industry benchmarks. Some OEMs offer predictive maintenance as an additional service, which can be integrated into IWMS & CAFM platforms like eFACiLiTY^®.

Focus on Anomaly Detection Rather than Failure Prediction
Instead of aiming to predict exact failure dates, use real-time monitoring to detect operational deviations and address issues before they escalate.

Benchmark Against Similar Facilities
Compare asset performance with similar facility types and operational environments to approximate failure trends. An IWMS platform can provide industry benchmarks if relevant data is available.

Rather than embracing predictive maintenance as an instant solution, organizations should adopt a stepwise strategy:

• Use OEM recommendations as a baseline.
• Deploy IoT-based Predictive Maintenance for real-time monitoring.
• Accumulate real-time operational data for future AI integration.
• Explore hybrid models that combine rule-based triggers with AI capabilities.

By taking a strategic, data-driven approach, companies can optimize maintenance efficiency without falling into the trap of overpromised AI capabilities.

Related Blogs: https://www.efacility.in/blog/myth-facts-of-ai-ml-in-buildings-predictive-maintenance/