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Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis
[chapter]
2018
Artificial Intelligence - Emerging Trends and Applications
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Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. ...
The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets ...
Acknowledgements The author would like to thank Northern Technical University in Iraq through Professor Dr Mowafaq Y. Hamdoon, the Chancellor of the university for supporting this work. ...
doi:10.5772/intechopen.74932
fatcat:wuz6jnad4vff7bln4blfv3kwzm
The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey
2021
American Journal of Artificial Intelligence
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AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, casebased reasoning and any combination of these techniques (hybrid systems) ...
, machine learning, biomimicry such as swarm intelligence and distributed intelligence. are widely used by multi-disciplinarians to solve a whole range of hitherto intractable problems associated with ...
[86][82]introduces the principles and the algorithm model of the ant colony algorithm.Then the fault diagnosis system of neural network(NN)is established.ACA algorithm is used to train a NN for fault ...
doi:10.11648/j.ajai.20210501.12
fatcat:gvplqmpqubdw3pquik5eavux5u
A survey on artificial intelligence based techniques for diagnosis of hepatitis variants
2020
Journal of Advances in Science and Engineering
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aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients ...
This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the ...
Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. ...
doi:10.37121/jase.v3i1.83
fatcat:2kb3zwrpunejlcbw26x7xznj6m
Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors
2013
International Journal of Distributed Sensor Networks
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Rotating machinery is widely used in modern industry. It is one of the most critical components in a variety of machinery and equipment. ...
It is important to enable reliable, safe, and efficient operation of large-scale and critical rotating machinery, which requires us to achieve accurate diagnosis of concurrent fault, for example, rolling ...
Acknowledgments This work was partially supported by the NSFC under Grant 61174113 and the Natural Science Fund of Guangdong Province under Grant S2011020002735. ...
doi:10.1155/2013/472675
fatcat:kju5n5kty5grlm5bf4h7eft43e
Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis
2004
Expert systems with applications
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This paper presents a new approach for integrating case-based reasoning (CBR) with an ART-Kohonen neural network (ART-KNN) to enhance fault diagnosis. ...
When solving a new problem, the neural network is used to make hypotheses and to guide the CBR module in the search for a similar previous case that supports one of the hypotheses. ...
The authors (Yang, Han, & An, 2003) proposed a fault diagnosis network (ART-Kohonen neural network, ART-KNN) which synthesizes the adaptive resonance theory (ART) (Carpenter & Grossberg, 1988) and ...
doi:10.1016/j.eswa.2003.09.009
fatcat:eecs76sxozfzdglp56q4dtom3u
A new cognitive-based massive alarm management system in electrical power administration
2008
2008 7th International Caribbean Conference on Devices, Circuits and Systems
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Additionally, in this methodology, a rule based expert system is used to treat the alarms with a neural net based approach to treat the historical database of alarms and failures. ...
Here, the integration is developed using the ontology of each system domains, i.e., the ontology corresponding to the alarms, controls, events, energy flow and trigger sequence. ...
A typical "Fuzzy Cognitive Map Neural Network" (FCM) based on Nonlinear Hebbian Rule was implemented. ...
doi:10.1109/iccdcs.2008.4542607
fatcat:tcp3nto3zjhrfgzwmp4id5wg7a
Methodologies and Applications of Artificial Intelligence in Systems Engineering
2022
International Journal of Robotics and Control Systems
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These are knowledge-based systems (KBS), artificial neural networks (ANN), and fuzzy logic and systems (FLS). ...
To illustrate the concepts, merits, and demerits, a typical application is given from each methodology. The relationship between ANN and FLS is emphasized. ...
planning system AIPS, and (application 3) the use and implementation of AI in VLSI design and verification, fault diagnosis, and test genera-tion. ...
doi:10.31763/ijrcs.v2i1.532
fatcat:nl5ylioc6vbu7iw6hjdvijvigi
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
[article]
2017
arXiv
pre-print
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Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. ...
The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. ...
An example of a neural-symbolic system that is already providing a contribution of this type to problems in bioinformatics and fault diagnosis is the Connectionist Inductive Learning and Logic Programming ...
arXiv:1711.03902v1
fatcat:3fod6z4oevhplpv2hzlkguyqiu
An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-based Hopfield Neural Network
2020
IEEE Access
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Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts. ...
Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance ...
ACKNOWLEDGMENT Authors would like to thank Sultan Qaboos University (Government of the Sultanate of Oman) for supporting Dr. A. M. Abdulghani. ...
doi:10.1109/access.2020.2985839
fatcat:mclixaatyzbk7kn4lvshxw7aie
Early Detection Of Abnormal Emergent Behaviour
2007
Zenodo
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Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007 ...
Faults are modelled separately and Fault Diagnosis and Isolation (FDI) is based on classifying machine behaviour as normal or having a known and characterized fault [1] , [2] . ...
The number of possible states and state-interactions explodes with increased freedom in local throughput and local control & calibration, and a global model of the emergent behaviour of a system using ...
doi:10.5281/zenodo.40559
fatcat:owf6ndiyzfb5pduyqpq7nvunnq
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
[article]
2020
arXiv
pre-print
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, diagnosing and predicting faults of complex industrial assets has been limited. ...
The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and ...
Acknowledgment The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2 176878. ...
arXiv:2005.02144v1
fatcat:wxm3dstogjfkhbcu6aueyaddja
Spectrum Enhanced Fault Localization with Artificial Intelligence Paradigm for Software Diagnosis
2019
International Journal for Research in Applied Science and Engineering Technology
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The proposed approach based neural network was used to find the results on the given public dataset. ...
The application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) [7] were investigated in software fault-proneness prediction. ...
Barinel is a combination of model-based diagnosis (MBD) and spectrumbased fault localization (SFL). ...
doi:10.22214/ijraset.2019.1036
fatcat:llekbzr5ivfydl7s76td4dpoqe
Modeling the faulty behaviour of digital designs using a feed forward neural network approach
2015
2015 IEEE International Symposium on Circuits and Systems (ISCAS)
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Finally, neural network and its advantage with regard to fault diagnosis and fault clustering are discussed. ...
Dataset generation A neural network needs a relevant dataset for proper training. ...
It is computed by using outputs from the neural network based model of circuit C17. , is the probability of having error type when the input of the model is . for(int i = 0; i < actual.rows ; i ++) { for ...
doi:10.1109/iscas.2015.7168934
dblp:conf/iscas/MirzadehBS15
fatcat:lljjcbpgynd77ogojbohmrjxfi
Potential, challenges and future directions for deep learning in prognostics and health management applications
2020
Engineering applications of artificial intelligence
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Not all of the signals contain information on a specific fault type since different fault types are affecting different signals and the correspondence is generally not one-to-one. ...
, diagnosing and predicting faults of complex industrial assets has been limited. ...
Acknowledgements The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2_176878. ...
doi:10.1016/j.engappai.2020.103678
fatcat:6heplbhuozautml5p7pzwrlfvq
A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems
2020
IEEE Open Journal of Intelligent Transportation Systems
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Similar to the reasoning of human intuition, the final outcome of a complex black-box procedure is verified against given expectations of an object's behavior. ...
In this article, we apply and evaluate collaborative, sensor-generic plausibility checking as a mean to detect empirical perception faults from their statistical fingerprints. ...
We test our plausibility-based fault diagnosis in simulation, using the injection of typical perception faults as identified from real-world infrastructure experiments. ...
doi:10.1109/ojits.2020.3027146
fatcat:iajki3vpezcbzajmlsul7mpkr4
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