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

A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.

A transfer convolutional neural network for fault diagnosis based on ResNet-50

A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm

In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method.

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Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

In this paper, a new fault feature extraction method based on Intrinsic Mode Function (IMF) envelope sample entropy (SampEn) is proposed for rolling bearings fault diagnosis. First, the Empirical Mode Decomposition (EMD) method is utilized to decompose the vibration signals self-adaptively into a number of IMFs which represent different frequency bands from high to low. Second, the IMF envelope signals are used to highlight the fault-induced information in a structurally simpler and physically more meaningful way than the original signals. Thus, the shortcoming of SampEn assigning high values to uncorrelated random signals can be overcome. Finally, the IMF envelope SampEn serve as a fault feature vector to be input into multi-class classifier of Support Vector Machine (SVM) for identification of different bearing conditions. The experimental results indicate that the proposed approach based on IMF envelope SampEn can identify different fault types as well as levels of severity effectively and is superior to that based on IMF SampEn.

http://www.joics.com/publishedpapers/2013_10_16_5189_5198.pdf

Dongxin Xue

Papers

A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm

Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

Fault Diagnosis of Rolling Bearings Based on IMF Envelope Sample Entropy and Support Vector Machine