Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System

Tingting Wu,Yufen Zhuang,Hainan Guo,Wei Fan,Bi Fan,Kangkang Xu,Cai Yi,Tangbin Xia

doi:10.1155/2021/6627305

Abstract

Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under different speeds and environment interference, which may lead to different diagnosis results. In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time-domain and frequency-domain features are extracted from the different speeds vibration signals. Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis.

Highlights

Rolling elements bearings are important machine elements that are widely used in railway wheels, wind turbines, gearboxes, pumps, and helicopter transmissions [1]. e running state of axle bearings, as the core component of high-speed trains, plays an important role in the safe and stable operation of the high-speed rail [2]. e railway axle bearings can support rotating machine elements and transfer loads of machine components under the fast-running state of a train
We propose a multidomain feature fusion for varying speed bearing diagnosis using broad learning system (BLS). e diagnostic power of the method is attributable to two features: First, we extract the intrinsic vibration characteristics at multiple speeds
We proposed a diagnosis framework using multidomain feature fusion and machine learning to detect faults from vibration data at different speeds. e study considers the dynamic characteristics of multiple speeds together to obtain more comprehensive fault diagnosis information

Summary

Introduction

Rolling elements bearings are important machine elements that are widely used in railway wheels, wind turbines, gearboxes, pumps, and helicopter transmissions [1]. e running state of axle bearings, as the core component of high-speed trains, plays an important role in the safe and stable operation of the high-speed rail [2]. e railway axle bearings can support rotating machine elements and transfer loads of machine components under the fast-running state of a train. Erefore, the inherent characteristic of vibration signal of variable speed should be extracted from the time and frequency domain together to achieve better fault diagnosis performance [22]. Erefore, a multidomain features fusion is proposed to present the inherent characteristics of the vibration signal for varying speed comprehensively. Zhao et al [29] proposed semisupervised broad learning system for fault diagnosis These studies either directly use BLS without considering the inherent characteristics of the data or only consider the frequencydomain characteristics of the signal. We propose a multidomain feature fusion for varying speed bearing diagnosis using BLS. With multidomain fusion, the vibration data of different speeds can be explored thoroughly under a unified framework to obtain more dynamic fault information.

Methodology

Experiment and Analysis

Conclusion