Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Expert Systems with Applications: An International Journal
Use of particle swarm optimization for machinery fault detection
Engineering Applications of Artificial Intelligence
A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm
Expert Systems with Applications: An International Journal
Effect of number of features on classification of roller bearing faults using SVM and PSVM
Expert Systems with Applications: An International Journal
An improved K-nearest-neighbor algorithm for text categorization
Expert Systems with Applications: An International Journal
Induction motors bearing fault detection using pattern recognition techniques
Expert Systems with Applications: An International Journal
An affinity-based new local distance function and similarity measure for kNN algorithm
Pattern Recognition Letters
Hi-index | 12.05 |
This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool. HHT analyzes the AE signal using intrinsic mode functions (IMFs), which are extracted using the process of Empirical Mode Decomposition (EMD). Instead of time domain approach with Hilbert transform, FFT of IMFs from HHT process are utilized to represent the time frequency domain approach for efficient signal response from rolling element bearing. Further, extracted statistical and acoustic features are used to select proper data mining based fault classifier with or without filter. K-nearest neighbor algorithm is observed to be more efficient classifier with default setting parameters in WEKA. APF-KNN approach, which is based on asymmetric proximity function with optimize feature selection shows better classification accuracy is used. Experimental evaluation for time frequency approach is presented for five bearing conditions such as healthy bearing, bearing with outer race, inner race, ball and combined defect. The experimental results show that the proposed method can increase reliability for the faults diagnosis of ball bearing.