Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87239
Title: Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring
Authors: Pappachan, Bobby Kaniyamkudy
Caesarendra, Wahyu
Tjahjowidodo, Tegoeh
Wijaya, Tomi
Keywords: Machining
Deburring
Issue Date: 2017
Source: Pappachan, B. K., Caesarendra, W., Tjahjowidodo, T., & Wijaya, T. (2017). Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring. Sensors, 17(6), 1247-.
Series/Report no.: Sensors
Abstract: Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process.
URI: https://hdl.handle.net/10356/87239
http://hdl.handle.net/10220/44336
DOI: http://dx.doi.org/10.3390/s17061247
Rights: © 2017 by The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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metadata.item.fulltext: With Fulltext
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