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Sensors 2017, 17(6), 1247;

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

Rolls-Royce @ NTU Corporate Lab, 65 Nanyang Avenue, Singapore 637460, Singapore
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Author to whom correspondence should be addressed.
Received: 30 March 2017 / Revised: 17 May 2017 / Accepted: 26 May 2017 / Published: 30 May 2017
(This article belongs to the Section Sensor Networks)
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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. View Full-Text
Keywords: machining; deburring; Welch’s estimate machining; deburring; Welch’s estimate

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Pappachan, B.K.; Caesarendra, W.; Tjahjowidodo, T.; Wijaya, T. Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring. Sensors 2017, 17, 1247.

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