Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms

Using Deep Learning Based Approaches for Bearing Fault Diagnosis with AE Sensors

In the age of Internet of Things and Industrial 4.0, the prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. Mechanical big data has the characteristics of large-volume, diversity and high-velocity. Effectively mining features from such data and accurately identifying the machinery health conditions with new advanced methods becom...

Bearing Fault Diagnosis Using Information Fusion and Intelligent Algorithms

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration s...

Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet de...

Bearing Fault Diagnosis Using Shifted Wavelet Filters

Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. This paper proposes a method for processing of signals resulting from rolling element bearing defects, based on the use of a shifted wavelet filter family. Using a time-frequency repre...