Sensors Fault Diagnosis Trends and Applications
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 cl...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (236 p.) |
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| 100 | 1 | |a Witczak, Piotr |4 edt | |
| 245 | 1 | 0 | |a Sensors Fault Diagnosis Trends and Applications |
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| 300 | |a 1 electronic resource (236 p.) | ||
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| 520 | |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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| 653 | |a krill herd algorithm | ||
| 653 | |a SVR | ||
| 653 | |a acoustic-based diagnosis | ||
| 653 | |a gear fault diagnosis | ||
| 653 | |a attention mechanism | ||
| 653 | |a convolutional neural network | ||
| 653 | |a stacked auto-encoder | ||
| 653 | |a weighting strategy | ||
| 653 | |a deep learning | ||
| 653 | |a bearing fault diagnosis | ||
| 653 | |a intelligent leak detection | ||
| 653 | |a acoustic emission signals | ||
| 653 | |a statistical parameters | ||
| 653 | |a support vector machine | ||
| 653 | |a wavelet denoising | ||
| 653 | |a Shannon entropy | ||
| 653 | |a adaptive noise reducer | ||
| 653 | |a gaussian reference signal | ||
| 653 | |a gearbox fault diagnosis | ||
| 653 | |a one against on multiclass support vector machine | ||
| 653 | |a varying rotational speed | ||
| 653 | |a fault detection and diagnosis | ||
| 653 | |a faults estimation | ||
| 653 | |a actuator and sensor fault | ||
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| 653 | |a fault isolation | ||
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| 653 | |a fault recovery | ||
| 653 | |a fault diagnosis | ||
| 653 | |a fault detection and isolation (FDIR) | ||
| 653 | |a autonomous vehicle | ||
| 653 | |a model predictive control | ||
| 653 | |a path tracking control | ||
| 653 | |a fault detection and isolation | ||
| 653 | |a braking control | ||
| 653 | |a nonlinear systems | ||
| 653 | |a fault tolerant control | ||
| 653 | |a iterative learning control | ||
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| 653 | |a cryptography | ||
| 653 | |a wireless sensor networks | ||
| 653 | |a machine learning | ||
| 653 | |a scan-chain diagnosis | ||
| 653 | |a artificial neural network | ||
| 653 | |a NARX | ||
| 653 | |a control valve | ||
| 653 | |a decision tree | ||
| 653 | |a signature matrix | ||
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