An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture

TL;DR

This paper introduces a novel early fault detection method for rotating machines that fuses multiple features from different domains using a stacking architecture, enhancing detection accuracy and robustness.

Contribution

It develops a multi-domain feature fusion approach with stacking architecture and deep feature learning for improved early fault detection in rotating machinery.

Findings

Outperforms existing methods in sensitivity and reliability.

Effective in extracting discriminative features from multiple domains.

Validated on three bearing datasets.

Abstract

Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection. Most existing EFD methods focus on learning fault representation by one type of feature. However, a combination of multiple features can capture a more comprehensive representation of system state. In this paper, we propose an EFD method based on multiple feature fusion with stacking architecture (M2FSA). The proposed method can extract generic and discriminiative features to detect early faults by combining time domain (TD), frequency domain (FD), and time-frequency domain (TFD) features. In order to unify the dimensions of the different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to learn deep features in three domains. The architecture of the proposed M2FSA consists of two layers. The first layer contains three base models, whose corresponding inputs are different deep features. The outputs of the first layer are concatenated to generate the input to the second layer, which consists of a meta model. The proposed method is tested on three bearing datasets. The results demonstrate that the proposed method is better than existing methods both in sensibility and reliability.