Machining vibration states monitoring based on image representation using convolutional neural networks

Measured signals are usually fed into filters or signal decomposers to extract useful features to assist making identification in state monitoring or fault diagnosis. But what is routinely ignored is that an experienced expert can realize what is happening just by watching the signals presented on the oscilloscope even without the analyzing report. The vision image input and the experience feedback are the two keys in this identification process by the brain. The experience can be easily quantified, like 1 for “good” and 0 for “bad”, and used for identification model construction, while there has been no attempt to use pictured signal as the model input. For closed-loop control system, it is necessary to acquire signal feedback point by point to adjust the system in real time. But for state monitoring and fault diagnosis, the pattern hiding among the signal points is usually more important, which is exactly one of the special fields of image representation to indicate complex interrelationship. Taking machining state monitoring as example, this paper explore the possibility to use the pictured signals as input to construct identification model without traditional feature engineering based on signal analysis. Convolutional neural networks (CNN) is introduced to connect pictured signals to different vibration states with experience feedback. Results validate the proposed method with excellent modeling performance. Time complexity analysis proves this pictured signal image representation based CNN method to be capable to be real-time. Two dimensional image representation is a powerful way to exhibit and fuse information. With high flexibility, the proposed method may be a promising framework for monitoring or fault diagnosis tasks.