An adaptive chatter signal enhancement approach for early fault diagnosis in machining process
Procedia CIRP, ISSN: 2212-8271, Vol: 82, Page: 308-313
2019
- 12Citations
- 32Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Chatter is a fast-changing machining fault that needs to be timely diagnosed to avoid the deterioration. However, it is a big challenge to recognize the weak chatter component out of the machining process signal that includes strong disturbances caused by the measurement noise, the machining uncertainty in the early stage. In this paper, an adaptive chatter signal enhancement approach is proposed to improve the signal-to-noise ratio (SNR) based on the principle of statistical resonance. A Fokker-Planck model with smoothly quadratic double well potential is proposed and analytically solved without using the adiabatic approximation. Compared with the recorded double-well models, the superiority of this one mainly lies in its better resolvability and enhancement capacity. The cutting force signals are utilized to prove the efficiency of the approach to enhance and highlight the chatter signal out of strong disturbances for early chatter detection.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2212827119305785; http://dx.doi.org/10.1016/j.procir.2019.03.273; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85070474937&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2212827119305785; https://dx.doi.org/10.1016/j.procir.2019.03.273
Elsevier BV
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