Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)

By N. Saravanan, K. Ramachandran. Expert systems with applications, 2010

324 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/7113640ea39f2937d2afb110f26a6e3656b8316b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Gaoliang Peng
2. Shengli Zhang
3. Pei Cao
4. Zhujun Zhang
5. Yuanhang Chen
6. Jiong Tang
7. Chuanhao Li
8. Shen Zhang

In Mechanics of Materials

1. Gaoliang Peng
2. Shengli Zhang
3. Pei Cao
4. Zhujun Zhang
5. Yuanhang Chen
6. Jiong Tang
7. Chuanhao Li

Citations in the corpus

• Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning (2017, methodology): “…given limited training data, researchers have combined pre-processing and data augmentation techniques, e.g., discrete wavelet transform (Saravanan and Ramachandran, 2010), antialiasing/decimation filter (Ince et al, 2017), and wavelet packet transform (Li et al, 2016), with neural…”
• A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “In [18], the discrete wavelet transform is used for feature extraction and an artificial neural network is used for classification.”