Feature learning for fault detection in high-dimensional condition monitoring signals (2018-10-12T00:00:00.000000Z)

TL;DR

An integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training, demonstrating a better performance in cases where condition monitoring data contain several non-informative signals.

Abstract

Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. This article proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked extreme learning machines (namely hierarchical extreme learning machines) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of hierarchical extreme learning machines fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (extreme learning machines and support vector machines); these same one-class classifiers combined with traditional dimensionality reduction methods (principal component analysis) and a deep belief network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.

Authors

References33 items

Fleet PHM for Critical Systems: Bi-level Deep Learning Approach for Fault Detection

Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction

A review on the application of deep learning in system health management

Wheel Defect Detection With Machine Learning

Remaining useful life estimation in prognostics using deep convolution neural networks

Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction

Deep Feature Learning Network for Fault Detection and Isolation

Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

Lithium-ion battery remaining useful life prediction with Deep Belief Network and Relevance Vector Machine

Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines

Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Extreme Learning Machine for Multilayer Perceptron

Building feature space of extreme learning machine with sparse denoising stacked-autoencoder

One-Class Classification with Extreme Learning Machine

What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle

Representation Learning: A Review and New Perspectives

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes

A Fast Learning Algorithm for Deep Belief Nets

Extreme learning machine: a new learning scheme of feedforward neural networks

Network constraints and multi-objective optimization for one-class classification