Abstract
Recently, linear motion (LM) guides have been widely used in industrial processes, especially for precise positioning applications. A LM guide typically requires a custom design for specific characteristics of several industrial fields, which is time consuming for manufacturing process; additionally, and the production line with failed LM guides cannot be fixed rapidly. Therefore, to reduce production loss during such periods, it is important to prepare for maintenance and management in case of a fault through a real-time diagnosis of LM guide conditions. Currently, studies on condition diagnosis applying deep learning algorithms are actively being conducted, and actual measured signals in the industrial fields are being used as training data. In this study, the condition diagnosis of LM guides is conducted through a variational auto-encoder (VAE) with convolutional layers. Normal and fault data are measured using an LM guide unit that consists of four LM blocks and one ball screw. The measured signals are converted to spectrogram images through short-time Fourier transform, and only normal data are used to perform network learning. The trained model is applied to classify the normal and fault states, and the reconstruction error is utilized as the evaluation metrics for classification performance. In order to validate the performance, the results are compared to those of a restricted Boltzmann Machine (RBM), and a stacked auto-encoder and a VAE, which does not consist of a convolutional network. Through this study, it is shown that the LM guide diagnosis is possible with a network (VAE with convolution layers) learned with only normal states and the performance is shown to be superior to other networks.
Highlights
The condition diagnosis on the parts constituting the rotating machinery has an important effect on safety because the operating condition can be controlled in advance
This study focused on the possibility of condition diagnosis for an Linear motion (LM) guide unit based on the generative model, which trains using limited dataset with only normal states
The main contributions of this study are as follows: 1) The condition diagnosis has been performed focused on the LM guide unit, 2) data from normal states are used only for training, 3) the possibility of network model training with relatively limited dataset is checked
Summary
The condition diagnosis on the parts constituting the rotating machinery has an important effect on safety because the operating condition can be controlled in advance. Zhang et al [26] proposed a bearing anomaly detection method through deep VAE based on data provided by Case Western Reserve University and the University of Cincinnati’s Center, and it showed high accuracy compared to other models. This study focused on the possibility of condition diagnosis for an LM guide unit based on the generative model, which trains using limited dataset with only normal states. The generative model used in this paper is mainly for learning the probability distribution of input data and the condition diagnosis is performed by using that characteristics. The main contributions of this research work can be summarized as follows: 1) the condition diagnosis has been performed focused on the LM guide unit, 2) data from normal states are used only for training, 3) the possibility of network model training with relatively limited dataset is checked. The loss is obtained by the difference between input xand reconstructed x, and can be expressed as (5):
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