Predictive Maintenance in the energy sector

Why traditional maintenance isn't enough

Weaknesses of traditional maintenance systems in the energy sector

Static thresholds instead of intelligent data analysis

Many monitoring systems use fixed limits to assess machine condition. However, these values are based on average data and do not take into account dynamic load changes or varying environmental conditions. As a result, alerts may be triggered too late or not at all - with potentially disastrous consequences for equipment availability.

Late fault detection

Reactive maintenance often means that a problem is not discovered until it is too late — for example after or just before a machine failure. This results in expensive emergency repairs and unplanned shutdowns. Additionally, scheduled preventive maintenance often results in replacing components that are functional, adding unnecessary costs.

Unconsidered interactions between components

Modern energy systems consist of complex systems with many interacting components — from compressors to gas turbines to heat exchangers. Traditional monitoring systems often only consider individual sensor values instead of analyzing the entire machine structure as a networked system. As a result, critical patterns and anomalies remain undetected, which can lead to unexpected failures.

Ensuring energy supply and efficiency

Requirements on energy suppliers are increasing — traditional maintenance is reaching its limits. In our white paper, we explain how AI optimizes processes and redefines machine monitoring in the energy sector.

Why traditional machine monitoring is reaching its limits

How AI monitoring effectively protects complex components from failures

What to consider when getting started with AI-based monitoring

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Problems before using our software

Limited flexibility of monitoring systems

Traditional monitoring systems do not react to changing operating conditions and overlook critical changes. Without dynamic analysis, problems remain undetected, which increases the risk of failure.

Risk of unplanned downtime

Traditional maintenance systems only identify faults at a late stage. This results in high costs for emergency repairs, lost production and, in the worst case, power outages.