Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm

2017 ◽  
Author(s):  
Jagath Sri Lal Senanayaka ◽  
Huynh Van Khang ◽  
Kjell G. Robbersmyr
Keyword(s):  
Fault Detection ◽  
Decision Tree ◽  
Vibration Signal ◽  
Classification Algorithm ◽  
Envelope Analysis ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Decision Tree Classification

scholarly journals Wheel Bearing Fault Isolation and Prognosis Using Acoustic Based Approach

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianshe Feng ◽  
Xinyu Du ◽  
Mutasim Salman
Keyword(s):  
Fault Detection ◽  
Vibration Signal ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Installation Space ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Failure Prognosis ◽  
Wheel Bearing

Wheel bearing fault detection, isolation and failure prognosis are critical to improve perceived quality and customer experience for retail vehicles, and to reduce the repair cost and down time for fleet vehicles. Currently, most of the research in bearing failure and degradation diagnosis focus on vibration signal analytics. However, these techniques are rarely applied in automotive industry due to the high sensor cost, installation space limitation, and limited communication bandwidth. In this work, an acoustic based approach for wheel bearing fault detection and isolation is developed to overcome these limitations. Since the bearing noise is a precursor of bearing failure, the proposed method is a prognosis solution. The whole solution is verified using the data collected from a production vehicle. The results show that the proposed method can predict the wheel bearing failure with promising accuracy and robustness.

scholarly journals Bearing Fault Detection in Induction Machine Using Squared Envelope Analysis of Stator Current

10.5772/67145 ◽  
2017 ◽  
Author(s):  
Valeria Cristina Maria Nascimento Leite ◽  
Jonas Guedes Borges da Silva ◽  
Germano Lambert Torres ◽  
Giscard Francimeire Cintra Veloso ◽  
Luiz Eduardo Borges da Silva ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Machine ◽  
Envelope Analysis ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Stator Current

BEARING FAULT DETECTION AND DIAGNOSIS BASED ON TEAGER–HUANG TRANSFORM

2009 ◽  
Vol 07 (05) ◽  
pp. 643-663 ◽  
Author(s):  
HUI LI ◽  
HAIQI ZHENG ◽  
LIWEI TANG
Keyword(s):  
Fault Detection ◽  
Modulation Frequency ◽  
Energy Operator ◽  
Vibration Signal ◽  
Mean Amplitude ◽  
Fault Detection And Diagnosis ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Detection

A new approach to fault diagnosis of bearings based on the Teager–Huang Transform (THT) is presented. This method is based on the Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) techniques. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM) Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager–Huang Transform approach is better than that of the Hilbert–Huang Transform approach for bearing fault detection. The Teager–Huang Transform has better resolution than the Hilbert–Huang Transform. The Teager–Huang Transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.

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