Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

Wang, Shuhui; Xiang, Jiawei; Zhong, Yongteng; Zhou, Yuqing

doi:10.1016/j.knosys.2017.12.027

Cited by 219 publications

(93 citation statements)

References 35 publications

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“…Thus, in order to detect the faults in bearings, many researchers have proposed different intelligent diagnosis methods based on machine learning methods or artificial intelligence models [1][2][3]. Wang et al [4] presented convolutional neural network-based hidden Markov models (CNN-HMMs) to classify multi faults in a bearing. To enlarge the application cases of training samples collected from fault simulators, an intelligent fault diagnosis approach was proposed using transfer learning to transfer fault samples from laboratory bearings to locomotive bearings [5].…”

Section: Introductionmentioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

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Section: Introductionmentioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

Self Cite

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“…The feature extraction method itself can be automated using these approaches, but establishing such models requires complicated signal processing steps. Automatic feature extraction using an auto-encoder has been proposed [19], but the computation cost of the DNN model itself is quite high. To realize wide adoption of data-driven FDD to industry, simpler and more efficient methods are required in both data-processing and DNN models.…”

Section: Introductionmentioning

confidence: 99%

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Suh

Lee

et al. 2019

Applied Sciences

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In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.

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“…In recent years, deep learning has received unprecedented attention and development. Its greatest advantage is that it can learn the representative characteristics in data automatically and avoid misleading from hand-crafted features [18]. It is widely applied in research fields related to feature extraction: Zhou et al [19] combined independent subspace analysis (ISA) with a convolutional network to extract the local morphology of the burning image for the rotary kiln sintering process layer by layer and built the word package model to learn its global feature.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model

et al. 2019

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Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the principal component analysis (PCA), and the hidden Markov model (HMM) is proposed to monitor the combustion condition with the uniformly spaced flame images, which are collected from the furnace combustion monitoring system. First, CAE is adopted to extract the features from the flame images, which obtain the sparse representations in the images. Then, PCA is applied to project the feature vectors into the orthogonal space for robustness and computation efficiency. Finally, a HMM is built to calculate the corresponding optimal states by learning the temporal behaviors in the compressed representations. A coal combustion adjustment experiment was conducted in a 660 MW opposed-firing boiler, and the sequential 14,400 flame images with three different combustion states were obtained to evaluate the effectiveness of the proposed approach. We tested six different compression dimensions of the latent variable z in the CAE model and ensured that the appropriate compress parameter was 1024. The proposed framework is compared with five other methods: the CAE + Gaussian mixture model (GMM), CAE + Kmean, the CAE + fuzzy c-mean method, CAE + HMM, and the traditional handcraft feature extraction method (TH) + HMM. The results show that the proposed framework has the highest classification accuracy (95.25% for the training samples and 97.36% for the testing samples) and has the best performance in recognizing the semi-stable state (85.67% for the training samples and 77.60% for the testing samples), indicating that the proposed framework is capable of identifying the combustion condition, changing when the combustion deteriorates as the coal feed rate falls.

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Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

Cited by 219 publications

References 35 publications

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model

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