Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and to achieve a more robust machining process. Any failure in the machining process and machine tool components may have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and the machine tool. The condition monitoring system may utilise information from several sources to facilitate the detection of disturbances in the machining process. To minimise additional complexity to the machining system, internal sensor signals for condition monitoring are used.

The main contribution from this work is a further understanding of the measured responses from linear and angular position encoders during excitation of the machine tool structure. It is shown that the measured encoder responses contain the operational frequencies and this applies to both active and passive machine axes. The response from an active machine axis however, involves a more complex analysis. The fundamental principles on the extraction of the generated micro-vibrations (translational and torsional vibrations) from the linear and rotary encoders are presented. Various methods for their analysis in time domain, frequency domain and phase space domain are also presented. New extentions to the nonlinear numerical methods in order to facilitate the extraction of features from Poincaré sections are introduced.

The experimental work shows that encoders are sensitive to small disturbances in the machining process. The possibility to use the proposed measurement method and numerical methods for tool wear detection in a milling operation has therefore been investigated. It is shown that tool wear can be detected and quantified by utilising the signals already available in machine tools.