2× Analyst Efficiency: How Mälarenergi Scaled Predictive Maintenance with AI

By cutting through data overload and automating prioritization, Mälarenergi’s team doubled their diagnostic capacity without adding headcount.

Executive summary

One of the biggest benefits is that the technology picks up alerts well in advance. The fact that AI is continuously working, analyzing, and sorting different events to present patterns and trends saves us a lot of time.

Tommy Persson

Maintenance engineer with almost 30 years experience in vibration measurements and analysis

Customer

PureSignal, MLT

Employees

774

Turnover

SEK 4.7 billion

Location

Combined heat and power plant, Västerås, Sweden

Production

Electricity and district heating, Cooling, Water and sewage

Heat

1720 GWh

Electricity

441 GWh

Mälarenergi, a major energy provider in Sweden, has transformed its maintenance approachby deploying over 350 pureMEMS wireless vibration sensors from MLT and pureSignal. With support from Viking Analytics’ AI platform, the team moved from manual checks to real-time, prioritized insights – saving time, reducing risk, and detecting faults earlier. In one case, AI flagged a bearing issue on a nearly new machine before failure. Now exploring technologies like the AI-driven lubrication system pureALUBE, Mälarenergi continues to set the standard for modern, data-driven maintenance.

Solution

pureMEMS wireless sensors (MLT/pureSignal) + Viking Analytics AI

Challenge

No visibility between manual checks

Assets

350 sensors / 100+ machines / 4 analysts

Location

Combined heat and power plant, Västerås, Sweden

2× Analyst Efficiency: How Mälarenergi Scaled Predictive Maintenance with AI

Executive summary

PureSignal, MLT

774

SEK 4.7 billion

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