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 complex. While AI-driven PdM has potential, its success hinges on several factors:

• The availability of high-quality historical data.
• Effective sensor deployment 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 take years before exhibiting significant wear-and-tear trends. Without historical failure data, AI models struggle to make accurate predictions, forcing organizations to rely on reactive or preventive maintenance in the interim.

Dependence on Historical Data
Predictive models require substantial past failure data to train algorithms effectively. Without this, predictions may be inaccurate, leading to unnecessary maintenance costs or unexpected failures.

Original Equipment Manufacturers (OEMs) play a critical role in maintenance planning:

• 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).

Given these limitations, a hybrid approach that combines traditional maintenance strategies with emerging AI capabilities is more effective:

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

Sensor-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.

Gradual Machine Learning IntegrationRather than immediately relying on AI predictions, build a data pipeline to collect usage and environmental data. Once sufficient historical data is available, AI models can be trained for reliable forecasts.

Collaborate with OEMs
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 Over 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 condition monitoring for critical assets.
• Accumulate real-time operational data for future AI integration.
• Explore hybrid models that combine rule-based triggers with AI capabilities.

This approach ensures cost-effective implementation while avoiding the pitfalls of overpromised AI capabilities.