What is maintenance planning?

Maintenance planning is the process of scheduling, organizing, and coordinating maintenance activities to ensure that equipment and facilities remain in good working order and can be used reliably.
Maintenance planning typically includes identifying equipment requiring maintenance, establishing a maintenance schedule, implementing a tracking system for maintenance tasks, coordinating with maintenance teams, and managing maintenance-related costs.

 

What is the difference between preventive, predictive, and corrective maintenance?

  • Corrective maintenance is performed after a failure has occurred. It involves repairing or replacing faulty components to restore equipment to normal operation.
  • Preventive maintenance aims to avoid breakdowns, as equipment failures can be costly—both in terms of operations and service quality. It is carried out according to maintenance cycles, while adhering to regulatory constraints and maintenance standards for different types of equipment.
  • Predictive maintenance seeks to anticipate failures before they occur by analyzing data such as wear measurement history, obtained, for example, through connected sensors (IoT). This helps estimate the remaining useful life (RUL) of equipment. The goal is to schedule maintenance operations as precisely as possible based on detected wear or anomalies.

 

How can decision mathematics help optimize maintenance?

Optimization techniques are highly suited to these complex, highly constrained, and combinatorial challenges. EURODECISION, an expert in artificial intelligence and decision mathematics, has been addressing maintenance-related issues for many years. These include maintenance planning to minimize operational impact, sizing maintenance resources, asset management, predictive maintenance, and fault diagnostics.

 

What approaches does EURODECISION use to address maintenance challenges?

Maintenance planning problems are best solved using operations research techniques. However, approaches based on machine learning and BRMS (rule-based systems) are better suited for predictive maintenance, anomaly detection, and diagnostic support. For example, machine learning is particularly effective for time-series forecasting, which we use in our models to predict remaining time before failure. Additionally, to anticipate failures before they occur, we model asymmetric error functions in neural networks.

 

Our experts develop algorithms and data analysis, forecasting, planning, and complex process control models for a wide range of challenges.
To learn more about artificial intelligence and decision mathematics for maintenance:

 

Download the brochure “AI for Maintenance”

 

Maintenance planning:

  • Long-term maintenance planning (assessing maintenance costs for a fleet, anticipating and balancing the maintenance workshop workload, sizing spare equipment inventory),
  • Annual planning for preventive maintenance of infrastructure or a fleet,
  • Fleet maintenance planning (trains, aircraft, etc.) and vehicle assignment to commercial missions, considering maintenance constraints and operational capacity,
  • Verifying compliance of maintenance schedules (mileage counters, time-based counters) using business rules,
  • Sizing maintenance teams, sector allocation, and visit planning,
  • Optimizing operational planning for nuclear power plant unit outages, accounting for task-related uncertainties, optimizing radiation protection for nuclear power plant maintenance operations.

 

Asset management (equipment, vehicle fleets, etc.):

  • Optimizing purchases and sales of vehicle batches across different fleets,
  • Optimizing equipment utilization, technical fleet management, and active fleet sizing,
  • Sizing a public-private partnership (PPP) and optimizing maintenance policies,
  • Planning and allocating scarce or specialized resources (e.g., in rail transport, infrastructure inspection vehicles or ballast-delivery units for construction sites),
  • Forecasting and optimizing spare parts inventory.
For more details, please contact us
Name
First Name
Company
Email *
Phone number
Your request *

* Mandatory fields

The use of artificial intelligence and decision mathematics in maintenance challenges
Armelle Le Gall, Head of the Design Department and expert in optimization for system design and maintenance, explains how EURODECISION's expertise can support maintenance teams in their work. She also shares her insights on the sector's upcoming challenges.
Read the Article

Predictive maintenance and fault diagnostics support:

  • Fault diagnosis model using business rules and machine learning,
  • Expert system for repair assistance of components,
  • Machine learning models for analyzing sensor and IoT data to monitor system health, then anticipate and predict failures,
  • Statistical models and machine learning methods for network and infrastructure monitoring to detect anomalies and forecast congestion,
  • Statistical models to validate autonomous driving tools and driver assistance systems.