Predictive Maintenance

Predictive maintenance (PdM) is one step above reactive and preventive maintenance. You can predict when the right time is to perform maintenance on machines based on data such as vibration, temperature and noise. This will give you that added security of avoiding downtime due to engine failure or overspending on preventive routines. We are committed to implementing AI-based technologies for this service to give added-value for your company.

Predictive Maintenance uses data analytics with (AI/ML based) tools to detect defects and anomalies in equipment and machineries. PdM needs historical trends and real-time data gathered from IoT devices or passed from a MCM system. Implementing PdM requires a good dataset to build profiles and trends for the equipment. Some business outputs from PdM are Failure Identification, Failure Type Identification, and Remaining Useful Life (RUL) of equipment.

Several parameters that PdM uses are vibration, oil properties, temperature, and other machine parameters that represent equipment’s conditions. By using PdM, one can expect a tenfold increase in ROI, a 25%-30% reduction in maintenance costs, a 70%-75% decrease of breakdowns and a 35%-45% reduction in downtime. To get the most out of it, a PdM implementation requires trained and experienced personnel and we can help you with that.

How does it work?

Sensors read data from machines and forward it to gateway

Data is processed with machine learning to predict failure types and remaining useful life of machines

A dashboard is used to display insights generated from AI analytics


Early Detection

Detect failures early to prevent equipment damage


Optimize machine performance and reduce maintenance costs arising from preventive maintenance

Downtime Prevention

Prevent downtime occurrence in production plants that require continuous operation


Increase durability for longer equipment lifetime
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