Predictive Maintenance

The goal of a predictive maintenance project is to reduce unplanned downtime by catching problems before they cause failures. Traditional maintenance either fixes things after they break or services equipment on a fixed schedule. Predictive maintenance intervenes based on data, at the point where a failure is becoming likely.

The work starts with collecting data from sensors, maintenance logs, and operational records. You look for patterns that appear before failures occur, then build a model that flags at-risk equipment. The output is typically a ranked list that maintenance teams use to prioritise their work. Validating whether predictions hold up under real operating conditions is an important part of the project.

The main methods are time-series analysis, classification models, and survival analysis. Python is the primary tool for modelling, with libraries for machine learning and statistical work. Domain knowledge matters: understanding what sensor readings indicate is necessary to identify useful features and build a model that is reliable in practice.

Predictive maintenance projects appear in Energy & Utilities, Transportation & Logistics, and manufacturing. The work connects to the Operations Analyst and Data Scientist roles. In the Netherlands, rail, energy, and port companies use predictive maintenance to manage large infrastructure assets.