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Fault diagnosis and prognostics based on cognitive computing and geometric space transformation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fault diagnosis and prognostics based on cognitive computing and geometric space transformationby Chen Lu ... [et al.].
other author:	Lu, Chen.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xiv, 554 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Fault location (Engineering)Data processing.
Online resource:	https://doi.org/10.1007/978-981-99-8917-1
ISBN:	9789819989171$q(electronic bk.)

Fault diagnosis and prognostics based on cognitive computing and geometric space transformation
Fault diagnosis and prognostics based on cognitive computing and geometric space transformation[electronic resource] /by Chen Lu ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xiv, 554 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Fault Diagnosis and Prognosis based on Deep Learning and Transfer Learning -- Chapter 3 Fault Diagnosis and Evaluation Based on Visual Cognitive Computing -- Chapter 4 Fault Diagnosis Based on Compressed Sensing -- Chapter 5 Fault Diagnosis and Evaluation Based on Differential Geometry -- Chapter 6 Performance Degradation Prediction and Assessment based on Geometric Space Transformation and Morphology Recognition.

This monograph introduces readers to new theories and methods applying cognitive computing and geometric space transformation to the field of fault diagnosis and prognostics. It summarizes the basic concepts and technical aspects of fault diagnosis and prognostics technology. Existing bottleneck problems are examined, and the advantages of applying cognitive computing and geometric space transformation are explained. In turn, the book highlights fault diagnosis, prognostic, and health assessment technologies based on cognitive computing methods, including deep learning, transfer learning, visual cognition, and compressed sensing. Lastly, it covers technologies based on differential geometry, space transformation, and pattern recognition.

ISBN: 9789819989171$q(electronic bk.)

Standard No.: 10.1007/978-981-99-8917-1doiSubjects--Topical Terms:

782242
Fault location (Engineering)
--Data processing.

LC Class. No.: TA169.6

Dewey Class. No.: 620.0044

Fault diagnosis and prognostics based on cognitive computing and geometric space transformation
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300 $a xiv, 554 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a Chapter 1 Introduction -- Chapter 2 Fault Diagnosis and Prognosis based on Deep Learning and Transfer Learning -- Chapter 3 Fault Diagnosis and Evaluation Based on Visual Cognitive Computing -- Chapter 4 Fault Diagnosis Based on Compressed Sensing -- Chapter 5 Fault Diagnosis and Evaluation Based on Differential Geometry -- Chapter 6 Performance Degradation Prediction and Assessment based on Geometric Space Transformation and Morphology Recognition.
520 $a This monograph introduces readers to new theories and methods applying cognitive computing and geometric space transformation to the field of fault diagnosis and prognostics. It summarizes the basic concepts and technical aspects of fault diagnosis and prognostics technology. Existing bottleneck problems are examined, and the advantages of applying cognitive computing and geometric space transformation are explained. In turn, the book highlights fault diagnosis, prognostic, and health assessment technologies based on cognitive computing methods, including deep learning, transfer learning, visual cognition, and compressed sensing. Lastly, it covers technologies based on differential geometry, space transformation, and pattern recognition.
650 0 $a Fault location (Engineering) $x Data processing. $3 782242
650 0 $a Soft computing $x Industrial applications. $3 276575
650 0 $a Transformations (Mathematics) $3 182635
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Control and Systems Theory. $3 825946
700 1 $a Lu, Chen. $3 986940
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856 4 0 $u https://doi.org/10.1007/978-981-99-8917-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)