@inproceedings{eab764962fdf44faa4e4522906440bd7,
title = "Industrial machinery diagnosis by means of normalized time-frequency maps",
abstract = "The development of intelligent and autonomous monitoring systems applied to rotating machinery represents the evolution towards the automatic industrial plants supervision. In this paper, an original method to detect camshaft defaults from the monitoring of the motor phase current is presented. This method is based on the short-time Fourier transform in order to analyze the spectral variations over each cycle of the system. The time-frequency maps are then normalized using statistical techniques in order to create a reference of the healthy functioning of the system. Normalized time-frequency maps allow the detection of changes from the reference that are statistically significant. The method is evaluated on data from an industrial packing machine at three different speeds and for two noise levels. It obtains excellent results with 100\% correct detections and 0\% false alarms in each case. Results are compared to those obtains with classical spectral approaches.",
keywords = "Current-based Diagnosis, Normalized Fault Indicator, Rotating Machinery, Short Time Fourier Transform, Statistical Analysis",
author = "A. Picot and D. Zurita and J. Cari{\~n}o and E. Fournier and J. R{\'e}gnier and Ortega, \{J. A.\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2015 ; Conference date: 01-09-2015 Through 04-09-2015",
year = "2015",
month = oct,
day = "21",
doi = "10.1109/DEMPED.2015.7303684",
language = "English",
series = "Proceedings - SDEMPED 2015: IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "158--164",
booktitle = "Proceedings - SDEMPED 2015",
address = "United States",
}