Abstract
Most machine health monitoring techniques are likely to suffer from ineffective selection of state features, and the increasing redundancy of raw signals. As a new strategy, an interactive and artistic monitoring approach is presented. Its basic concept is to transform raw signals into artistic graphs rather than obscure waveforms. The entire procedure includes three steps: signal acquisition, plotting artistic graphs and interactive diagnosis. In the case of numerical control machine tools, an interactive and artistic monitoring prototype system is developed based on the integration of the open-source Arduino platform and the open-source Processing language. Experimental results indicate that the artistic visualization of measured data facilitates the identifications of machine condition and the diagnosis of observed symptoms.
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