From the capacity of production depending on machine tool availability, it becomes a day-today concern for maintenance operator and production manager. Each stop in production constitutes a loss of earning that should be anticipated to allow an efficient planning of maintenance operation and to avoid unscheduled production downtime. Condition-based maintenance solutions at a machine level provide the operator with tools that support monitoring of the machine's behaviour and highlight behaviour changes. However, machine tools as large engineering systems have multiple subsystems and equipment of different natures (electrical, mechanical, hydraulics, electronics, etc.) following different fault rates and modes in which behaviour may differ from specific phases of their life cycle due to events and maintenance history. Supporting proactive maintenance at a fleet level provides summarized information and means of comparison and investigation for decision-making.

Knowledge of machine tool behaviour is a key element to improve production, quality and availability as behaviour indicators represent or give an idea of the real state of the machine. Indeed, real conditions can be used to update simulation models’ parameters, detect or anticipate quality fault on workpiece, and to estimate damage state of machine components such as axis and spindle. Behaviour indicators extraction is based on the analysis of specific and known moves or actions of the machine tools. Two kinds of approaches to build these indicators are discussed in this chapter.