Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

By K. Zhu, Y. S. Wong, G. S. Hong. 2009

426 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/1a4a3f42b9176abd68a3c27f7ad51cbc31060eda

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Mechanical Engineering

In Mechanics of Materials

Citations in the corpus

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, background): “Also based on the inner product, the continuous wavelet transform (CWT) [32]–[34] as a famous signal processing methods that can transform a raw signal in the temporal domain into the time–frequency domain with a wavelet basis function, which is illustrated as follows.”
Tool condition monitoring techniques in milling process — a review (2020, background): “The frame work of TCMs [3] is shown in Fig.”
Cloud-enabled prognosis for manufacturing (2015, background): “Fault (or defect) diagnosis is a critical part of this process that links the identified abnormal behaviors in a machine to possible root causes [20-22].”
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, neutral): “Zhu et al. (2009) showed the capabilities of wavelet analysis for signal processing and tool condition monitoring for machining.”
Wavelets for fault diagnosis of rotary machines: A review with applications (2014, background): “growth in the theory and practice of wavelets [1–10], and many publications have been seen, describing advance-”
A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, background): “Zhu et al. [133] have carried out a detailed review of various applications of wavelet analysis in TCM systems and concluded that wavelet transform is more effective in analyzing non-stationary machining sensor signals than any other time – frequency method due to its sparsity and localization…”