GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

Jiang, Qingchao; Huang, Biao; Yan, Xuefeng

doi:10.1016/j.compchemeng.2015.09.013

Cited by 86 publications

(37 citation statements)

References 41 publications

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“…For the application of GMM classification of the damage signals of the refractory, the increase in the number of the model can improve the accuracy of the model, however with increased complexity of the model, as discussed by Jiang et al [24]. The Bayesian information criterion (BIC) has the ability to maintain the balance between the accuracy and complexity of the model; therefore, it is adopted to classify the damage.…”

Section: Classification Of the Damage Signalsmentioning

confidence: 99%

“…For this purpose, the AE signal parameters of the delay distribution, rise time, energy, and peak amplitude were selected to distinguish the effective features for different failure mechanism so that the two failure modes of fiber breakage and delamination can be distinguished [21,22]. The related parameters can be modeled by a generative model, in particular a Gaussian mixture model (GMM) in the field of dimension processing [23,24]. The global feature descriptor was formed by stacking the parameters of the adapted GMM (i.e., means, covariance, and weight) in a so-called supervector [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

et al. 2018

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Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.

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Section: Classification Of the Damage Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

et al. 2018

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“…The calculations of the gradients for the lth layer (l < L) and layer L are shown in equation (8) and equation (9).…”

Section: Cnnmentioning

confidence: 99%

“…Six types of gear faults were used to verify the proposed method and showed that the PCA process exhibited a high accuracy in diagnosis and enhanced the computational efficiency of the artificial neural network (ANN). PCA was also used by Jiang et al 8 when they presented the Gaussian mixture model and optimal principal components-based Bayesian method to generate lower dimension and be more efficient evidence. The former studies indicate that PCA generally serves as a fundamental method and research on it is significant.…”

Section: Introductionmentioning

confidence: 99%

A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery

You

Shen

Guo

et al. 2017

Advances in Mechanical Engineering

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Rolling element bearings and gears are the most common machine elements. As they are extensively used in rotating machinery, their health conditions are crucial to the safe operation. The signals measured from rotating machines are usually affected by the working conditions and background noises. Thus, identifying faults from the mixed signals is a challenging and important task. Deep learning is initially developed for image recognition. Recently, it has attracted increasing attention in machinery fault diagnosis research. However, the generalization ability of the default classifier of it is not very satisfying. Thus, combining the feature learning ability of deep learning and the existing classifiers with satisfactory generalization ability is necessary. In this article, a hybrid technique based on convolutional neural network and support vector regression is proposed. The former part is used to promote feature extraction capability, and the latter part is used for multi-class classification. The efficiency of the proposed scheme is validated using the real acoustic signals measured from locomotive bearings and vibration signals measured from the automobile transmission gearbox. Results confirm that the method proposed is able to capture fault characteristics from the raw data, and both bearing faults and gear faults can be detected successfully.

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“…The extracted features are selected using in‐control operation data. However, the fault information has no definite mapping on a certain feature . The selected features using the in‐control data cannot guarantee to provide the optimal representation of the fault information.…”

Section: Introductionmentioning

confidence: 99%

A variable‐selection control chart via penalized likelihood and Gaussian mixture model for multimodal and high‐dimensional processes

Yan

Zhang

Jung

2019

Quality & Reliability Eng

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With the development of the sensor network and manufacturing technology, multivariate processes face a new challenge of high‐dimensional data. However, traditional statistical methods based on small‐ or medium‐sized samples such as T2 monitoring statistics may not be suitable because of the “curse of dimensionality” problem. To overcome this shortcoming, some control charts based on the variable‐selection (VS) algorithms using penalized likelihood have been suggested for process monitoring and fault diagnosis. Although there has been much effort to improve VS‐based control charts, there is usually a common distributional assumption that in‐control observations should follow a single multivariate Gaussian distribution. However, in current manufacturing processes, processes can have multimodal properties. To handle the high‐dimensionality and multimodality, in this study, a VS‐based control chart with a Gaussian mixture model (GMM) is proposed. We extend the VS‐based control chart framework to the process with multimodal distributions, so that the high‐dimensionality and multimodal information in the process can be better considered.

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GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

Cited by 86 publications

References 41 publications

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery

A variable‐selection control chart via penalized likelihood and Gaussian mixture model for multimodal and high‐dimensional processes

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