The operation of industrial manufacturing processes can suffer greatly when critical components fail suddenly. Large manufacturing processes can involve many critical components, the failure of which can interfere with the process operation. Typically, these parts are changed periodically according to a preventative maintenance strategy. Industry is eager to move towards predictive maintenance in order to make savings in spare parts and to lower downtime. Predictive maintenance requires several measurement campaigns from a single part in order to make a working model or find condition thresholds. A single measurement campaign from a certain part can take a large amount of time and provide limited information regarding developing conditions in a certain environment. Multiplying the quantity of this measured data leads to a more reliable estimate for the aspects affecting the condition and thresholds. The idea is to gather condition monitoring data from several similar machines or machine parts used in a wide range of different environmental and stress conditions. This data can be used to generate models for several varying fault types. Data used for this system can include condition monitoring data from the target, automation system data describing operating conditions, metadata for describing environmental factors and maintenance reports in standardised form, including images of faults and events.
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