Feature Extraction of Time-Series Data using DWT and FFT for Ballscrew Condition Monitoring

Nurudeen Alegeh, Munavar Thottoli, Naeem Mian, Andrew Longstaff, Simon Fletcher

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


This paper investigates the use of the discrete wavelet transform (DWT) and Fast Fourier Transform (FFT) to improve the quality of extracted features for machine learning. The case study in this paper is detecting the health state of the
ballscrew of a gantry type machine tool. For the implementation of the algorithm for feature extraction, wavelet is first applied to the data, followed by FFT and then useful features are extracted from the resultant signal. The extracted features were then used in various machine learning algorithms like decision tree, K-nearest neighbour (KNN) and support vector machine (SVM) for binary classification of the ballscrew state. The result shows significant improvement in the classification
accuracy after the wavelet transform and FFT has been performed on the data.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference in Manufacturing Research
Subtitle of host publicationICMR 2021
EditorsMahmoud Shafik, Keith Case
PublisherIOS Press
Number of pages6
ISBN (Electronic)9781614994398
Publication statusPublished - 21 Aug 2021
Event18th International Conference on Manufacturing Research - University of Derby, Derby, United Kingdom
Duration: 7 Sep 202110 Sep 2021
Conference number: 18

Publication series

NameAdvances in Manufacturing Technology
PublisherIOS Press


Conference18th International Conference on Manufacturing Research
Abbreviated titleICMR 2021
Country/TerritoryUnited Kingdom


Dive into the research topics of 'Feature Extraction of Time-Series Data using DWT and FFT for Ballscrew Condition Monitoring'. Together they form a unique fingerprint.

Cite this