Artificial Intelligence for Fault Detection and Diagnosis
Fault detection and diagnosis (FDD) is an important task in manufacturing and mechatronic systems for reducing costs and improving productivity. Traditionally, the states of machines and their faults are manually checked, a process which is time-consuming and expensive. Therefore, it is desirable to...
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| Format: | Online |
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| Langue: | anglais |
| Publié: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Accès en ligne: | https://directory.doabooks.org/handle/20.500.12854/170623 |
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| collection | Directory of Open Access Books |
| description | Fault detection and diagnosis (FDD) is an important task in manufacturing and mechatronic systems for reducing costs and improving productivity. Traditionally, the states of machines and their faults are manually checked, a process which is time-consuming and expensive. Therefore, it is desirable to develop intelligent systems to achieve automatic FDD. Artificial intelligence as a concept covers a wide range of algorithms that mimic the human mind, thinking and acting like humans to solve important tasks in different fields. In recent years, many AI algorithms have been applied to FDD, including data processing, feature analysis, and classification. Typical methods include deep neural networks, long short-term memory, convolutional neural networks, random forest, and evolutionary computation. However, the potential of AI has not been comprehensively investigated in FDD. This remains a challenging task due to many factors, such as changeable equipment working states, incomplete information, a lack of sufficient training data, complex relationships between faults and symptoms, imbalanced data, and the requirement of having domain knowledge. This reprint is a collection of research regarding AI techniques applied to various FDD tasks. |
| format | Online |
| id | doab-20.500.12854ir-170623 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1706232026-01-02T16:17:50Z Artificial Intelligence for Fault Detection and Diagnosis Bi, Ying Zhang, Mengjie Xue, Bing Peng, Bo DC-DC converters prognostic analysis multi-valued neuron neural network support vector machine Zeta converter knowledge discovery in dataset fault tree causality analysis aging system machine learning power systems harmonic distortion power quality peak detection fault diagnosis frequency domain low-load condition rolling bearing diagnosis fault detection fault type recognition signal processing quantum algorithms quantum computation combinatorial optimization circuit fault diagnostics steam turbine t-distribution stochastic neighborhood embedding (t-SNE) extreme gradient boosting (XGBoost) clustering operation and maintenance (O&M) wind turbines (WTs) predictive fault diagnostic supervisory control and data acquisition (SCADA) Time2Vec (T2V) Long Short-Term Memory (LSTM) T2V-LSTM treatment effect survival analysis Nadaraya–Watson regression Beran estimator neural network meta-learner Fault detection and diagnosis (FDD) is an important task in manufacturing and mechatronic systems for reducing costs and improving productivity. Traditionally, the states of machines and their faults are manually checked, a process which is time-consuming and expensive. Therefore, it is desirable to develop intelligent systems to achieve automatic FDD. Artificial intelligence as a concept covers a wide range of algorithms that mimic the human mind, thinking and acting like humans to solve important tasks in different fields. In recent years, many AI algorithms have been applied to FDD, including data processing, feature analysis, and classification. Typical methods include deep neural networks, long short-term memory, convolutional neural networks, random forest, and evolutionary computation. However, the potential of AI has not been comprehensively investigated in FDD. This remains a challenging task due to many factors, such as changeable equipment working states, incomplete information, a lack of sufficient training data, complex relationships between faults and symptoms, imbalanced data, and the requirement of having domain knowledge. This reprint is a collection of research regarding AI techniques applied to various FDD tasks. 2026-01-02T16:17:46Z 2026-01-02T16:17:46Z 2025 book 978-3-7258-4913-0 https://directory.doabooks.org/handle/20.500.12854/170623 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11351 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4914-7 10.3390/books978-3-7258-4914-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4913-0 188 CH open access |
| spellingShingle | DC-DC converters prognostic analysis multi-valued neuron neural network support vector machine Zeta converter knowledge discovery in dataset fault tree causality analysis aging system machine learning power systems harmonic distortion power quality peak detection fault diagnosis frequency domain low-load condition rolling bearing diagnosis fault detection fault type recognition signal processing quantum algorithms quantum computation combinatorial optimization circuit fault diagnostics steam turbine t-distribution stochastic neighborhood embedding (t-SNE) extreme gradient boosting (XGBoost) clustering operation and maintenance (O&M) wind turbines (WTs) predictive fault diagnostic supervisory control and data acquisition (SCADA) Time2Vec (T2V) Long Short-Term Memory (LSTM) T2V-LSTM treatment effect survival analysis Nadaraya–Watson regression Beran estimator neural network meta-learner Artificial Intelligence for Fault Detection and Diagnosis |
| title | Artificial Intelligence for Fault Detection and Diagnosis |
| title_full | Artificial Intelligence for Fault Detection and Diagnosis |
| title_fullStr | Artificial Intelligence for Fault Detection and Diagnosis |
| title_full_unstemmed | Artificial Intelligence for Fault Detection and Diagnosis |
| title_short | Artificial Intelligence for Fault Detection and Diagnosis |
| title_sort | artificial intelligence for fault detection and diagnosis |
| topic | DC-DC converters prognostic analysis multi-valued neuron neural network support vector machine Zeta converter knowledge discovery in dataset fault tree causality analysis aging system machine learning power systems harmonic distortion power quality peak detection fault diagnosis frequency domain low-load condition rolling bearing diagnosis fault detection fault type recognition signal processing quantum algorithms quantum computation combinatorial optimization circuit fault diagnostics steam turbine t-distribution stochastic neighborhood embedding (t-SNE) extreme gradient boosting (XGBoost) clustering operation and maintenance (O&M) wind turbines (WTs) predictive fault diagnostic supervisory control and data acquisition (SCADA) Time2Vec (T2V) Long Short-Term Memory (LSTM) T2V-LSTM treatment effect survival analysis Nadaraya–Watson regression Beran estimator neural network meta-learner |
| topic_facet | DC-DC converters prognostic analysis multi-valued neuron neural network support vector machine Zeta converter knowledge discovery in dataset fault tree causality analysis aging system machine learning power systems harmonic distortion power quality peak detection fault diagnosis frequency domain low-load condition rolling bearing diagnosis fault detection fault type recognition signal processing quantum algorithms quantum computation combinatorial optimization circuit fault diagnostics steam turbine t-distribution stochastic neighborhood embedding (t-SNE) extreme gradient boosting (XGBoost) clustering operation and maintenance (O&M) wind turbines (WTs) predictive fault diagnostic supervisory control and data acquisition (SCADA) Time2Vec (T2V) Long Short-Term Memory (LSTM) T2V-LSTM treatment effect survival analysis Nadaraya–Watson regression Beran estimator neural network meta-learner |
| url | https://directory.doabooks.org/handle/20.500.12854/170623 |