Multiple-domain manifold for feature extraction in machinery fault diagnosis
• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular val...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gan, Meng [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Schlagwörter: |
Multiple-domain manifold (MDM) |
|---|
| Umfang: |
16 |
|---|
| Übergeordnetes Werk: |
Enthalten in: High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level - Kwon, Yeong Min ELSEVIER, 2022, journal of the International Measurement Confederation (IMEKO), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:75 ; year:2015 ; pages:76-91 ; extent:16 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.measurement.2015.07.042 |
|---|
| Katalog-ID: |
ELV023463805 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV023463805 | ||
| 003 | DE-627 | ||
| 005 | 20230623170540.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2015 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.measurement.2015.07.042 |2 doi | |
| 028 | 5 | 2 | |a GBVA2015008000004.pica |
| 035 | |a (DE-627)ELV023463805 | ||
| 035 | |a (ELSEVIER)S0263-2241(15)00389-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 660 | |
| 082 | 0 | 4 | |a 660 |q DE-600 |
| 082 | 0 | 4 | |a 530 |a 620 |q VZ |
| 084 | |a 50.22 |2 bkl | ||
| 084 | |a 35.07 |2 bkl | ||
| 100 | 1 | |a Gan, Meng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| 264 | 1 | |c 2015 | |
| 300 | |a 16 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. | ||
| 650 | 7 | |a Multiple-domain manifold (MDM) |2 Elsevier | |
| 650 | 7 | |a Singular value decomposition (SVD) |2 Elsevier | |
| 650 | 7 | |a Feature space construction |2 Elsevier | |
| 650 | 7 | |a Manifold learning |2 Elsevier | |
| 650 | 7 | |a Machinery fault diagnosis |2 Elsevier | |
| 700 | 1 | |a Wang, Cong |4 oth | |
| 700 | 1 | |a Zhu, Chang’an |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Kwon, Yeong Min ELSEVIER |t High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |d 2022 |d journal of the International Measurement Confederation (IMEKO) |g Amsterdam [u.a.] |w (DE-627)ELV008789606 |
| 773 | 1 | 8 | |g volume:75 |g year:2015 |g pages:76-91 |g extent:16 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.measurement.2015.07.042 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 50.22 |j Sensorik |q VZ |
| 936 | b | k | |a 35.07 |j Chemisches Labor |j chemische Methoden |q VZ |
| 951 | |a AR | ||
| 952 | |d 75 |j 2015 |h 76-91 |g 16 | ||
| 953 | |2 045F |a 660 | ||
| author_variant |
m g mg |
|---|---|
| matchkey_str |
ganmengwangcongzhuchangan:2015----:utpeoanaiodofauexrcinnah |
| hierarchy_sort_str |
2015 |
| bklnumber |
50.22 35.07 |
| publishDate |
2015 |
| allfields |
10.1016/j.measurement.2015.07.042 doi GBVA2015008000004.pica (DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 DE-627 ger DE-627 rakwb eng 660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Gan, Meng verfasserin aut Multiple-domain manifold for feature extraction in machinery fault diagnosis 2015 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier Wang, Cong oth Zhu, Chang’an oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:75 year:2015 pages:76-91 extent:16 https://doi.org/10.1016/j.measurement.2015.07.042 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 75 2015 76-91 16 045F 660 |
| spelling |
10.1016/j.measurement.2015.07.042 doi GBVA2015008000004.pica (DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 DE-627 ger DE-627 rakwb eng 660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Gan, Meng verfasserin aut Multiple-domain manifold for feature extraction in machinery fault diagnosis 2015 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier Wang, Cong oth Zhu, Chang’an oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:75 year:2015 pages:76-91 extent:16 https://doi.org/10.1016/j.measurement.2015.07.042 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 75 2015 76-91 16 045F 660 |
| allfields_unstemmed |
10.1016/j.measurement.2015.07.042 doi GBVA2015008000004.pica (DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 DE-627 ger DE-627 rakwb eng 660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Gan, Meng verfasserin aut Multiple-domain manifold for feature extraction in machinery fault diagnosis 2015 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier Wang, Cong oth Zhu, Chang’an oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:75 year:2015 pages:76-91 extent:16 https://doi.org/10.1016/j.measurement.2015.07.042 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 75 2015 76-91 16 045F 660 |
| allfieldsGer |
10.1016/j.measurement.2015.07.042 doi GBVA2015008000004.pica (DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 DE-627 ger DE-627 rakwb eng 660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Gan, Meng verfasserin aut Multiple-domain manifold for feature extraction in machinery fault diagnosis 2015 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier Wang, Cong oth Zhu, Chang’an oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:75 year:2015 pages:76-91 extent:16 https://doi.org/10.1016/j.measurement.2015.07.042 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 75 2015 76-91 16 045F 660 |
| allfieldsSound |
10.1016/j.measurement.2015.07.042 doi GBVA2015008000004.pica (DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 DE-627 ger DE-627 rakwb eng 660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Gan, Meng verfasserin aut Multiple-domain manifold for feature extraction in machinery fault diagnosis 2015 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier Wang, Cong oth Zhu, Chang’an oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:75 year:2015 pages:76-91 extent:16 https://doi.org/10.1016/j.measurement.2015.07.042 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 75 2015 76-91 16 045F 660 |
| language |
English |
| source |
Enthalten in High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level Amsterdam [u.a.] volume:75 year:2015 pages:76-91 extent:16 |
| sourceStr |
Enthalten in High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level Amsterdam [u.a.] volume:75 year:2015 pages:76-91 extent:16 |
| format_phy_str_mv |
Article |
| bklname |
Sensorik Chemisches Labor chemische Methoden |
| institution |
findex.gbv.de |
| topic_facet |
Multiple-domain manifold (MDM) Singular value decomposition (SVD) Feature space construction Manifold learning Machinery fault diagnosis |
| dewey-raw |
660 |
| isfreeaccess_bool |
false |
| container_title |
High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |
| authorswithroles_txt_mv |
Gan, Meng @@aut@@ Wang, Cong @@oth@@ Zhu, Chang’an @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
ELV008789606 |
| dewey-sort |
3660 |
| id |
ELV023463805 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV023463805</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623170540.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.measurement.2015.07.042</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2015008000004.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV023463805</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0263-2241(15)00389-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">660</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.22</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.07</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gan, Meng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multiple-domain manifold for feature extraction in machinery fault diagnosis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">16</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multiple-domain manifold (MDM)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Singular value decomposition (SVD)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Feature space construction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Manifold learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machinery fault diagnosis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Cong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Chang’an</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Kwon, Yeong Min ELSEVIER</subfield><subfield code="t">High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level</subfield><subfield code="d">2022</subfield><subfield code="d">journal of the International Measurement Confederation (IMEKO)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV008789606</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:75</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:76-91</subfield><subfield code="g">extent:16</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.measurement.2015.07.042</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.22</subfield><subfield code="j">Sensorik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.07</subfield><subfield code="j">Chemisches Labor</subfield><subfield code="j">chemische Methoden</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">75</subfield><subfield code="j">2015</subfield><subfield code="h">76-91</subfield><subfield code="g">16</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">660</subfield></datafield></record></collection>
|
| author |
Gan, Meng |
| spellingShingle |
Gan, Meng ddc 660 ddc 530 bkl 50.22 bkl 35.07 Elsevier Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| authorStr |
Gan, Meng |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV008789606 |
| format |
electronic Article |
| dewey-ones |
660 - Chemical engineering 530 - Physics 620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl Multiple-domain manifold for feature extraction in machinery fault diagnosis Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis Elsevier |
| topic |
ddc 660 ddc 530 bkl 50.22 bkl 35.07 Elsevier Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis |
| topic_unstemmed |
ddc 660 ddc 530 bkl 50.22 bkl 35.07 Elsevier Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis |
| topic_browse |
ddc 660 ddc 530 bkl 50.22 bkl 35.07 Elsevier Multiple-domain manifold (MDM) Elsevier Singular value decomposition (SVD) Elsevier Feature space construction Elsevier Manifold learning Elsevier Machinery fault diagnosis |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
c w cw c z cz |
| hierarchy_parent_title |
High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |
| hierarchy_parent_id |
ELV008789606 |
| dewey-tens |
660 - Chemical engineering 530 - Physics 620 - Engineering |
| hierarchy_top_title |
High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV008789606 |
| title |
Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| ctrlnum |
(DE-627)ELV023463805 (ELSEVIER)S0263-2241(15)00389-9 |
| title_full |
Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| author_sort |
Gan, Meng |
| journal |
High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |
| journalStr |
High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
zzz |
| container_start_page |
76 |
| author_browse |
Gan, Meng |
| container_volume |
75 |
| physical |
16 |
| class |
660 660 DE-600 530 620 VZ 50.22 bkl 35.07 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Gan, Meng |
| doi_str_mv |
10.1016/j.measurement.2015.07.042 |
| dewey-full |
660 530 620 |
| title_sort |
multiple-domain manifold for feature extraction in machinery fault diagnosis |
| title_auth |
Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| abstract |
• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
| abstractGer |
• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
| abstract_unstemmed |
• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Multiple-domain manifold for feature extraction in machinery fault diagnosis |
| url |
https://doi.org/10.1016/j.measurement.2015.07.042 |
| remote_bool |
true |
| author2 |
Wang, Cong Zhu, Chang’an |
| author2Str |
Wang, Cong Zhu, Chang’an |
| ppnlink |
ELV008789606 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.measurement.2015.07.042 |
| up_date |
2024-07-06T18:55:33.806Z |
| _version_ |
1803857044499857408 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV023463805</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623170540.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.measurement.2015.07.042</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2015008000004.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV023463805</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0263-2241(15)00389-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">660</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.22</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.07</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gan, Meng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multiple-domain manifold for feature extraction in machinery fault diagnosis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">16</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multiple-domain manifold (MDM)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Singular value decomposition (SVD)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Feature space construction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Manifold learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machinery fault diagnosis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Cong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Chang’an</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Kwon, Yeong Min ELSEVIER</subfield><subfield code="t">High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level</subfield><subfield code="d">2022</subfield><subfield code="d">journal of the International Measurement Confederation (IMEKO)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV008789606</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:75</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:76-91</subfield><subfield code="g">extent:16</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.measurement.2015.07.042</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.22</subfield><subfield code="j">Sensorik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.07</subfield><subfield code="j">Chemisches Labor</subfield><subfield code="j">chemische Methoden</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">75</subfield><subfield code="j">2015</subfield><subfield code="h">76-91</subfield><subfield code="g">16</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">660</subfield></datafield></record></collection>
|
| score |
7.4014273 |