AI Predictive Maintenance in Smart RDM
An intelligent maintenance strategy based on data, AI, and industrial methodologies
In this short video, based on our article published in Energies (MDPI), we present Smart Hybrid Maintenance System (SHMS). The concept integrates reliability KPIs, AI-driven condition monitoring, and energy performance into one decision framework based on the Hybrid Risk Index (HRI).
Implementing advanced maintenance algorithms in real industrial environments is rarely straightforward. This is why many AI and predictive maintenance initiatives struggle: the algorithms are complex, and production deployment is a specialized capability.
At SmartRDM, we have the expertise and experience to make these models work in practice. We translate complex scientific methods into production-ready, scalable solutions – delivered efficiently and tailored to your assets, processes, and operational context.
If this sounds interesting, we encourage you to read the entire scientific article.
From early warning to the right maintenance decision
Predictive Maintenance in Smart RDM is an integrated maintenance strategy that combines OT/IT data, AI/ML algorithms, and expert knowledge to predict failures, optimize service, and significantly reduce operating costs. Predictive numerical models determine the probability of failure and the time to failure, while Smart RDM functionality (visualizations, forms, alarms, and more) guides the operator through the entire decision-making process – from signal detection to service action.
Results for customers:
- 5–15% increase in OEE,
- reduced unplanned downtime by dozens of hours per month,
- faster diagnostics and lower repair costs,
- rapid return on investment (often within a few months).
This is Predictive Maintenance that works – thanks to technology, data, and proven methodology.
Predict, decide, act
Predictive Maintenance that reduces unplanned downtime and maintenance costs by identifying failures early and supporting fact-based maintenance prioritization.
Smart RDM models were developed based on:
- many years of industrial projects,
- maintenance staff experts’ knowledge,
- scientific cooperation,
- analysis of thousands of hours of sensor data.
The platform uses, among other things:
- anomaly detection,
- trend analysis,
- probabilistic failure models,
- time between failure prediction (MTTF/MTBF),
- multivariate signal correlations,
- CBM (condition based monitoring) and RCM (Reliability-centered maintenance) indicators.
Each model is calibrated to customer data and continuously improved. The first effects are visible within a few weeks of implementation, and full PdM maturity is achieved in 3–6 months.
Optimal maintenance policy indicators
Smart RDM determines:
- when to perform maintenance,
- when it is safe to postpone it,
- how the device wears out over time,
- the effect of the work performed,
- which components generate the most failures.
It uses industry-standard methodologies (CBM, RCM) and an innovative approach to create a hybrid decision-making model.
Failure prediction software – a system that learns with operators
Notification grading
From early warnings to critical alarms with a forecast of the time to failure.
Learning through interaction
Every operator action (confirmation, rejection, response) is used in machine learning predictive computational models.
Full event management
Service workflows, activity logs, documentation, and linking alerts to maintenance tasks.
Digital service memory
Each intervention:
- affects subsequent predictions,
- updates technical condition indicators,
- affects OEE,
- builds organizational knowledge.
The system collects data and allows you to track performance on an ongoing basis.
Predictive Maintenance cycle in Smart RDM
- Deviation detection
- Risk assessment
- Failure time prediction
- Recommendations for action
- Work implementation
- Effect assessment + model learning
The system manages the entire Predictive Maintenance cycle – it does not just detect failures. It supports operational excellence through early symptom identification, business process digitization, and data-driven decision making. It facilitates the collection and use of knowledge, and thanks to the implemented LLM-based AI search engines, it significantly improves the comfort and efficiency of user interaction with the system.
Download our methodology now!
Comprehensive Predictive Maintenance implementation plan
Learn about the methodology for implementing Predictive Maintenance, developed based on best practices, completed deployments, and the latest technologies and tools – including machine learning and artificial intelligence (AI).
Gabriela Gic-Grusza
Unit & Product Manager Smart RDM