1D convolutional neural network-based adaptive algorithm structure with system fault diagnosis and signal feature extraction for noise and vibration enhancement in mechanical systems

Abstract

A variety of adaptive algorithms are utilized to control the vibration and noise for mechanical systems in the industrial field, since their characteristics usually changes due to unwanted disturbance and malfunctioning. A reference signal plays an important role in signal tracking through adaptive algorithms, and well-characterized information about the tracked signal should be employed. However, it is typically difficult to determine the signal feature because operating systems generate complex signals. Moreover, when the system state changes due to malfunctions or disturbances, signal feature can also be changed, affecting the performance of adaptive algorithms. This study proposes a novel strategy based on a 1D convolutional neural network (1D CNN) for improving the signal tracking performance. 1D CNN has the ability to deal with consecutive data that is directly measured by an accelerometer attached to mechanical systems, as well as to extract the signal feature through convolutional layers. The development of proposed algorithm can be explained with following three parts: 1) Data measurement opportunities are limited, particularly because fault data is more difficult to measure than normal state data. Therefore, when performing learning, there is a problem of a lack of training data. Thus, a data generator is proposed that is based on a generative adversarial network (GAN) and a variational auto encoder (VAE). 2) In order to extract the frequency and phase and consider the system state to automatically define the reference signal and increase tracking robustness, the methodology of signal feature extraction and diagnosis is proposed based on a 1D CNN. In order to validate the tracking performance, a neural network-based signal tracking algorithm is applied to the developed structure. Furthermore, bearing and linear motion (LM) guide data from experiments is employed to confirm the versatility of the developed algorithm. Through above process, it is demonstrated that based on defined reference signal through the extracted feature, the signal tracking performance can be enhanced.