Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis

Abstract

One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such as principal components analysis, which usually results in an improved fault diagnosis. Other available techniques include auto-encoders and its variants denoising auto-encoders and sparse auto-encoders. Most recently, variational auto-encoders are one of the most promising techniques for unsupervised learning with successful applications in image processing and speech recognition. Differently from other auto-encoder methods, variational auto-encoders use variational inference to generate a latent representation of the data and impose a distribution over the latent variables and the data itself. In this article, we propose a fully unsupervised deep variational auto-encoder-based approach for dimensionality reduction in fault diagnosis and explore the variational auto-encoder capabilities for such a task. This is achieved by comparing the latent representations provided by variational auto-encoders to the ones from principal components analysis as well as when no reduction is performed in ball bearings' fault classification using vibration signals. To tackle massive sensor data, we propose different architectures for the variational auto-encoder's encoder and decoder that are based on deep neural networks and deep convolutional neural networks. Experiments are also carried out by varying the data preprocessing methods for generating spectrograms and hand-engineering features as well as the use of raw vibration data, the architecture of the neural networks fault classifiers operating on the latent representations from variational auto-encoder and principal components analysis, the degree of data dimension reduction, and the size of the available labeled data used for training the fault classifiers. The results show that variational auto-encoders are a competent and promising tool for dimensionality reduction for use in fault diagnosis and worth further exploring their capabilities beyond vibration signals of ball bearing elements.

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

“…3). VAE becomes a popular generative model by combining Bayesian inference and the efficiency of the NNs to obtain a nonlinear low-dimensional latent space [29]- [32]. The Bayesian inference is obtained by an additional layer used for sampling the latent vector z with a prior specified distribution p(z), usually assumed to be a standard Gaussian N (0, I ), where I is the identity matrix.…”

“…In nonlinear processes monitoring, VAE have been recently used for high-dimensional process fault diagnosis. The most relevant characteristics of the process are extracted by the latent variable space by projecting the high-dimensional process data into a lower-dimensional space [8], [29], [31], [32], [34], [39]- [42].…”

Autoencoder-aided measurement of concentration from a single line of speckle

“…Instead of considering the statistics in Fig. 7, another standard way to classify 1D speckle data would be via linear principal component analysis (PCA) [23,24], a widely used and generally applicable method of dimension reduction for large data sets. We start by collecting a training data set, taking a number of line samples from the computed 2D speckle corresponding to each of the 14 concentrations.…”

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

“…Shen et al [29] proposed a stacked CAE for anti-noise and robust fault diagnosis. Martin et al [30] adopted a fully unsupervised deep VAE method for some latent fault feature extraction by variational inferences. These studies motivate us to develop a new AE-based fault diagnosis model for WTs.…”