Aiming at automated and intelligent state monitoring of induction motors, which are an integral component of a broad spectrum of manufacturing machines, this paper presents a Deep Belief Network (DBN)-based approach to automatically extract relevant features from vibration signals that characterize the working condition of an induction motor. The DBN model employs a structure with stacked restricted Boltzmann machines (RBMs), and is trained by an efficient learning algorithm called greedy layer-wise training. Vibration signals are used as the input to the DBN, and the outputs from activation functions of the trained network are the features needed for fault diagnosis. Comparing to traditional feature extraction methods for induction motor fault diagnosis such as wavelet packet transform, the proposed method is able to learn features directly from the vibration signal to achieve comparable performance with high classification accuracy. Experiments conducted on a machine fault simulator have verified the effectiveness of the proposed method for induction motor fault diagnosis.
|Title of host publication||International Symposium on Flexible Automation, ISFA 2016|
|Number of pages||6|
|State||Published - Dec 16 2016|
|Event||International Symposium on Flexible Automation, ISFA 2016 - Cleveland, United States|
Duration: Aug 1 2016 → Aug 3 2016
|Name||International Symposium on Flexible Automation, ISFA 2016|
|Conference||International Symposium on Flexible Automation, ISFA 2016|
|Period||8/1/16 → 8/3/16|
Bibliographical noteFunding Information:
This work has been supported in part by the National Natural Science Foundation of China under 51575102 and the National Science Foundation of US under CCF-1331850 and CMMI-1300999.
© 2016 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering