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220822 ||| eng |
| 020 |
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|a 9783036510484
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|a books978-3-0365-1049-1
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|a 9783036510491
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| 100 |
1 |
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|a Witczak, Piotr
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| 245 |
0 |
0 |
|a Sensors Fault Diagnosis Trends and Applications
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (236 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a adaptive noise reducer
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| 653 |
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|a braking control
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| 653 |
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|a hybrid kernel function
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| 653 |
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|a stacked auto-encoder
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| 653 |
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|a fault detection and isolation (FDIR)
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| 653 |
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|a weighting strategy
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| 653 |
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|a rolling bearing
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| 653 |
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|a fault tolerant control
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| 653 |
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|a fault identification
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| 653 |
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|a performance degradation
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| 653 |
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|a n/a
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| 653 |
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|a fault detection and diagnosis
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| 653 |
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|a cryptography
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| 653 |
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|a nonlinear systems
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| 653 |
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|a perception sensor
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| 653 |
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|a deep learning
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| 653 |
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|a neural networks
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| 653 |
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|a fault recovery
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| 653 |
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|a Takagi-Sugeno fuzzy systems
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| 653 |
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|a lidar
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| 653 |
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|a fault detection
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| 653 |
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|a iterative learning control
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a fault detection and isolation
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| 653 |
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|a artificial neural network
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| 653 |
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|a automotive
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| 653 |
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|a SVR
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| 653 |
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|a faults estimation
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| 653 |
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|a intelligent leak detection
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| 653 |
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|a model predictive control
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| 653 |
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|a attention mechanism
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| 653 |
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|a wavelet denoising
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| 653 |
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|a fault isolation
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| 653 |
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|a wireless sensor networks
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| 653 |
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|a control valve
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| 653 |
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|a gear fault diagnosis
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| 653 |
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|a NARX
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| 653 |
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|a actuator and sensor fault
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| 653 |
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|a path tracking control
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| 653 |
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|a support vector machine
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| 653 |
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|a gearbox fault diagnosis
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| 653 |
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|a fault diagnosis
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| 653 |
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|a autonomous vehicle
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| 653 |
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|a observer design
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| 653 |
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|a statistical parameters
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| 653 |
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|a decision tree
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| 653 |
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|a varying rotational speed
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| 653 |
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|a signature matrix
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| 653 |
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|a krill herd algorithm
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| 653 |
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|a convolutional neural network
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| 653 |
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|a acoustic emission signals
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| 653 |
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|a gaussian reference signal
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| 653 |
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|a Shannon entropy
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| 653 |
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|a scan-chain diagnosis
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| 653 |
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|a one against on multiclass support vector machine
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| 653 |
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|a acoustic-based diagnosis
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| 653 |
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|a bearing fault diagnosis
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| 700 |
1 |
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|a Witczak, Piotr
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 024 |
8 |
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|a 10.3390/books978-3-0365-1049-1
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4056
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76611
|z DOAB: description of the publication
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| 082 |
0 |
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|a 700
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| 082 |
0 |
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|a 600
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| 520 |
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|a Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.
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