Data Mining Practical Machine Learning Tools and Techniques

Artificial intelligence for fault diagnosis of rotating machinery: A review

Applications of machine learning to machine fault diagnosis: A review and roadmap

Wavelets for fault diagnosis of rotary machines: A review with applications

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.

Fault diagnosis of ball bearings using machine learning methods

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

Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN

Bearing failure is one of the foremost causes of breakdown in rotating machines, resulting in costly systems downtime. This paper presents a methodology for rolling element bearings fault diagnosis using continuous wavelet transform (CWT). The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered in which three are from real valued and other three from complex valued. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. Finally, the bearing faults are classified using these statistical features as input to machine learning techniques. Three machine learning techniques are used for faults classifications, out of which two are supervised machine learning techniques, i.e. support vector machine (SVM), artificial neural network (ANN) and other one is an unsupervised machine learning technique, i.e. self-organizing maps (SOM). The methodology presented in the paper is applied to the rolling element bearings fault diagnosis. The Meyer wavelet is selected based on Maximum Energy to Shannon Entropy ratio and the Complex Morlet wavelet is selected using Maximum Relative Wavelet Energy criterion. The test result showed that the SVM identified the fault categories of rolling element bearing more accurately for both Meyer wavelet and Complex Morlet wavelet and has a better diagnosis performance as compared to the ANN and SOM. Features selected using Meyer wavelet gives higher faults classification efficiency with SVM classifier.

S. P. Harsha

Papers

Fault diagnosis of ball bearings using machine learning methods

Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN

Fault diagnosis of ball bearings using continuous wavelet transform

Rolling element bearing fault diagnosis using wavelet transform

Non-linear dynamic behaviors of rolling element bearings due to surface waviness