Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Zhang, Xiaochen; Jiang, Dongxiang; Han, Te; Wang, Nanfei; Wei-guo, Yang; Yang, Yizhou

doi:10.1155/2017/8092691

Cited by 30 publications

(21 citation statements)

References 22 publications

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“…Growing interest in deep learning has brought the problem of data imbalance to the foreground, particularly in the field of data mining [3], medical diagnosis [4], the detection of fraudulent calls [3], risk management [5][6][7], text classification [8], fault diagnosis [9,10], anomaly detection [11,12], and face recognition [13]. Conventional machine learning models, i.e., non-deep learning, have been extensively applied in the study of class imbalance; however, there has been relatively little work using deep learning models, despite recent advances in this field [3,14].…”

Section: Introductionmentioning

confidence: 99%

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Lin

Nguyen

2020

Applied Sciences

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Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).

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Section: Introductionmentioning

confidence: 99%

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Lin

Nguyen

2020

Applied Sciences

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“…13,14 Therefore, data-driven methods are widely used for monitoring and FD purposes. 15 Latent variable regression (LVR) is a well-known data-driven modeling technique that includes principal component analysis (PCA) 16 and partial least squares (PLS). 17 LVR modeling methods are a multivariate analysis that aims to reduce the dimensionality of the data and rely on the definition of a linear data transformation via an orthonormal matrix which is computed from the dataset itself.…”

Section: Introductionmentioning

confidence: 99%

Effective fault detection in structural health monitoring systems

Chaabane

Mansouri

Abodayeh

et al. 2019

Advances in Mechanical Engineering

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A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

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“…Therefore, in such conditions, analysis of the measured signals by means of classical signal processing techniques alone, like the fast Fourier transform, is considered to be insufficient because they provide a global transformation that is unable to properly capture the local time–frequency properties of a signal [ 23 ]. The nonstationary behavior can be explored by various time–frequency analysis techniques, including the Wigner Ville distribution (WVD) [ 24 ], short time Fourier transform (STFT) [ 25 , 26 , 27 ], and wavelet packet transform (WPT) [ 28 , 29 ]. The WPT is more practical in fault diagnosis schemes because of its better time–frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Sohaib

Kim

2017

Sensors

167

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Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

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Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Cited by 30 publications

References 22 publications

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Effective fault detection in structural health monitoring systems

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

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