Bearing fault diagnosis method based on singular value decomposition and hidden Markov model

2015 ◽  
Author(s):  
Hongwu Xu ◽  
Yugang Fan ◽  
Jiande Wu ◽  
Yang Gao ◽  
Zhongli Yu
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Value Decomposition

Bearing Fault Diagnosis Using Singular Value Decomposition and Hidden Markov Modeling

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Rui Sun ◽  
Hong-Zhong Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Hidden Markov ◽  
Singular Value ◽  
Practical Significance ◽  
Markov Modeling ◽  
Classification Rate ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Condition monitoring and fault diagnosis of bearings are of practical significance in industry. In order to get a feature containing different fault signatures, this paper uses Wavelet Transform (WT), Wavelet Lifting Scheme (WLS) and Empirical Mode Decomposition (EMD), respectively, to decompose signal into different frequency bands. Then, Singular Value Decomposition (SVD) is utilized to extract intrinsic characteristic of signal from obtained matrix. These singular value vectors are regarded as inputs to Hidden Markov Models (HMM) for identification of machinery health condition. In this research, the fault diagnosis system is validated by motor bearing data, including normal bearings, inner race fault bearings, outer race fault bearings and roller fault bearings. Analysis results show that this method is effective in bearing fault diagnosis and its classification rate is excellent.

scholarly journals Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

2019 ◽  
Vol 9 (2) ◽  
pp. 19-26
Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Parameter Determination ◽  
Iris Image ◽  
Determination System ◽  
Value Decomposition

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).

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