Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”
Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]
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| Veröffentlicht in: | Processes Jg. 9; H. 3; S. 532 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.01.2021
MDPI |
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| ISSN: | 2227-9717, 2227-9717 |
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| Abstract | Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...] |
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| AbstractList | The papers proposed in this book present new methods of fault diagnosis and failure prognosis which provide solutions to scientific issues such as structured and unstructured uncertainties, the presence of multiple faults, the lack of prior knowledge on the conditions of use, feature extraction and selection, model optimisation and online implementation. Induction motor is also considered in [3] which focus their study on the impact of the use of attribute selection methods such as ReliefF, correlation-based feature selection (CFS), and correlation and fitness value-based feature selection (CFFS), on the performance of neural classifiers such as probabilistic neural network (PNN), radial basis function neural network (RBNN), and back propagation neural network (BPNN). To evaluate the effectiveness of this algorithm in presence of different operating conditions, a hydraulic pump is used as a case study, where three kinds of loads are simulated by means of the throttle valve. Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...] |
| Author | Bendahan, Marc Djeziri, Mohand |
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| Snippet | Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective... The papers proposed in this book present new methods of fault diagnosis and failure prognosis which provide solutions to scientific issues such as structured... |
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| StartPage | 532 |
| SubjectTerms | Algorithms Artificial neural networks Back propagation Back propagation networks Case studies Classification Computer Science Conflicts of interest Data processing Deep learning Fault diagnosis Feature extraction Feature selection Hydraulic equipment Induction motors Learning algorithms Machine Learning Neural networks Optimization Performance evaluation Prognosis Radial basis function Throttles Wavelet transforms |
| Title | Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis” |
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