Generalizable Tool-Wear Monitoring in Brownfield CNC Milling via Time, Frequency, and Time-Frequency Vibration Features

Abstract

The paper proposes a practical, scalable, and non-intrusive system for the automatic detection of tool wear under real industrial conditions that does not require process metadata (e.g., spindle speed, feed rate) or specialised equipment. The proposed method is based solely on the analysis of triaxial vibration signals and combines multiple signal-processing methods (time, frequency, and time-frequency) to enable deeper analysis. Various time-domain features, such as RMS, standard deviation, kurtosis, and crest factor, are combined with spectral analysis via Welch power spectral density (PSD) estimation and the continuous wavelet transform (CWT) for time-frequency analysis. To make features comparable across machines and operating conditions, the features are combined using median statistics and normalised relative to the median (Δ%). Experimental validation was performed with multiple machines and measurement axes on real industrial datasets that were heavily imbalanced. It was shown that there is a direct and consistent correlation between the condition of worn tools and the overall increase in tool vibration energy, as evidenced by significantly higher RMS and standard deviation values. On the other hand, higher-order statistical measures, such as kurtosis and crest factor, were less consistent when used alone due to their sensitivity to operational variability.  Frequency domain analysis showed that the wear of the tool could not only be described by a general rise in energy, but also by the significant increase of certain frequency components in the spectrum. Most notably, the peaks centred at 200 Hz were found to be significantly raised, along with their harmonics at around 600 Hz and 800 Hz, when the tool was worn, and this was true for all the axes of the measurements. These frequencies can thus be considered reliable and repeatable indicators of tool wear. The continuous wavelet transforms (CWT), a complementary method to time-localised time-frequency representations, confirmed the results and, notably, revealed that the signal's high-energy bursts are time-recurrent and running; thus, they can be seen as a series of bright spots closely packed in time in the scalogram. The strength of the proposal is its rational and transparent integration of several well-known signal-processing methods into a single, coherent concept that encompasses the full spectrum of changes, from the global increase in vibration energy to the localised, frequency-specific excitations that are the hallmark of wear progression. By combining global energy indicators with spectral and time frequency features, the approach enhances diagnostic reliability while improving physical understanding of tool-wear mechanisms. It is robust, practical, and readily transferable, offering strong potential for scalable predictive maintenance applications in machining environments.