A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and nonstationary signals. It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to en...
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| Veröffentlicht in: | Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-9 |
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| creator | Hou, Heping Liu, Jinjin Zhang, Haiyan Xu, Zhuofei |
| description | Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and nonstationary signals. It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. It can also be used in other fields related to EMD method. |
| doi_str_mv | 10.1155/2017/9724502 |
| format | Article |
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It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. 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Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. It can also be used in other fields related to EMD method.</description><subject>Damage detection</subject><subject>Decomposition</subject><subject>Empirical analysis</subject><subject>Engineering</subject><subject>Entropy</subject><subject>Fault diagnosis</subject><subject>Heart rate</subject><subject>Machinery</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Noise</subject><subject>Nonlinear analysis</subject><subject>Pattern recognition</subject><subject>Researchers</subject><subject>Signal processing</subject><subject>Structural damage</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqF0M9LwzAUB_AiCs7pzbMEPGpdXtIs7XHMTQcbgj_AW0nT1GWsSU1adP-9mR149JSXx-e9B98ougR8B8DYiGDgo4yThGFyFA2AjWnMIOHHocYkiYHQ99PozPsNxgQYpINoM0HPyivh5BpZg1biW9ddjV52dWG3WqKZaZ1tdqhytkaL8NHGh_bKlgrNOyNbHaaEKdGi9WjSNGFG_Pa0QXPRbVt0r8WHsV778-ikEluvLg7vMHqbz16nj_Hy6WExnSxjSce4jQvBSwEgSaGyQpXpWBaZKMYqrVhWCVkAT6AErNJEUiVVWbIs6BRXCaMpA0qH0XW_t3H2s1O-zTe2cyaczCFjHDhlhAV12yvprPdOVXnjdC3cLgec79PM92nmhzQDv-n5WptSfOn_9FWvVTCqEn8aMsJToD8Qt377</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Hou, Heping</creator><creator>Liu, Jinjin</creator><creator>Zhang, Haiyan</creator><creator>Xu, Zhuofei</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-1477-7067</orcidid></search><sort><creationdate>20170101</creationdate><title>A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis</title><author>Hou, Heping ; 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It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. 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| subjects | Damage detection Decomposition Empirical analysis Engineering Entropy Fault diagnosis Heart rate Machinery Methods Neural networks Noise Nonlinear analysis Pattern recognition Researchers Signal processing Structural damage |
| title | A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis |
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