Predictive Maintenance for Security-Industry Assets

The Client

The client is a maintenance service provider responsible for X-ray scanners deployed at critical infrastructure points, including ports, borders, and airports. Their business depends on ensuring high availability of these assets while meeting strict service-level agreements. As operational demands increased, they needed a predictive approach to maintenance that would reduce downtime and optimize resource planning.
 

The Challenge

X-ray scanners used in security environments are mission-critical systems where downtime can disrupt operations and create security risks. Traditional maintenance approaches — based on fixed schedules or reactive repairs — were insufficient to guarantee high availability.

The client faced growing pressure to reduce unplanned equipment failures while also optimizing the workload of field maintenance teams. Without predictive insights, maintenance scheduling was inefficient, often resulting in either unnecessary servicing or delayed interventions.

The challenge required integrating heterogeneous data sources, including real-time sensor data and historical maintenance records spanning over a decade. Additionally, the solution needed to address both short-term failure prediction and long-term asset lifecycle planning.

Without a predictive maintenance framework, the client risked increased operational costs, service disruptions, and inability to meet rising customer expectations.
 

What We Did

Approach

We designed a predictive maintenance system combining short-term failure prediction with long-term survival analysis. The approach aimed to provide both immediate operational alerts and strategic insights for asset lifecycle management.

During the discovery phase, we identified two distinct but complementary needs: early warning signals for imminent failures and probabilistic forecasts for long-term asset degradation. This led to the development of separate modeling layers optimized for different time horizons.

A key decision was to integrate these models into a unified decision support system, enabling maintenance teams to act proactively while also improving long-term planning.
 

Methodology

• Data assessment:
  Aggregated 10 years of historical data, including sensor readings, maintenance logs, and corrective service records
• Approach selection:
  Combined machine learning models for short-term failure prediction with survival analysis for long-term asset reliability
• Model development:
  Built short-term models to predict failure events within a 2–3 week horizon and long-term models to estimate failure probabilities over 6–12 months
• Validation framework:
  Evaluated predictive accuracy using historical failure events and tested model performance across different asset types and conditions
• Integration:
  Deployed models into an online decision support system for condition-based and predictive maintenance
• Compliance:
  Ensured alignment with operational standards for critical infrastructure and data handling policies

The Outcome

High-accuracy prediction of equipment failures up to 3 weeks in advance

The predictive maintenance system enabled early detection of potential failures, allowing maintenance teams to intervene before unplanned downtime occurred.
 

Secondary outcomes:

• Improved asset availability across critical infrastructure locations
• Reduced unplanned downtime and maintenance costs
• Optimized scheduling of field service teams
• Long-term visibility into asset replacement needs (6–12 months horizon)

Facing a similar challenge?

If you operate critical assets and need to reduce downtime while optimizing maintenance operations, we can help build predictive systems tailored to your infrastructure.

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