✦ LIBER ✦

Rolling element bearing fault diagnosis using wavelet transform

✍ Scribed by P.K. Kankar; Satish C. Sharma; S.P. Harsha

Book ID: 113816664
Publisher: Elsevier Science
Year: 2011
Tongue: English
Weight: 500 KB
Volume: 74
Category: Article
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2011.01.021

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

SINGULARITY ANALYSIS USING CONTINUOUS WA

SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS

✍ Q. SUN; Y. TANG 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 562 KB

Rolling element bearing fault detection

Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms

✍ Dong Wang; Qiang Miao; Xianfeng Fan; Hong-Zhong Huang 📂 Article 📅 2009 🏛 Springer-Verlag 🌐 English ⚖ 630 KB

Multi-fault diagnosis of rolling bearing

Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

✍ V. Purushotham; S. Narayanan; Suryanarayana A.N. Prasad 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 497 KB

Bearing and Gear Fault Diagnosis Using A

Bearing and Gear Fault Diagnosis Using Adaptive Wavelet Transform of Vibration Signals

✍ D.P. Jena; S.N. Panigrahi 📂 Article 📅 2012 🏛 Elsevier 🌐 English ⚖ 462 KB

Bearing fault diagnosis based on wavelet

Bearing fault diagnosis based on wavelet transform and fuzzy inference

✍ Xinsheng Lou; Kenneth A Loparo 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 941 KB

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The

Fault severity assessment for rolling el

Fault severity assessment for rolling element bearings using the Lempel–Ziv complexity and continuous wavelet transform

✍ Hoonbin Hong; Ming Liang 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 636 KB