@article{melani_framework_2021,
	title = {A framework to automate fault detection and diagnosis based on moving window principal component analysis and {Bayesian} network},
	volume = {215},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832021003574},
	doi = {10.1016/j.ress.2021.107837},
	abstract = {Through Condition-Based Maintenance strategy, planners can monitor the health of the machinery and recommend actions based on the information obtained. Nevertheless, this approach depends on the successful establishment of Fault Detection and Diagnosis (FDD) processes. Although FDD is a research area in full growth with the development of several methods and heuristics, the availability of data from systems under a fault condition is still scarce in many applications, mainly related to complex systems. In many circumstances, only data from the system in healthy conditions is available and the applied FDD method should be able to detect variations in system conditions and diagnose faults without the need for previous labeled fault data. In this context, this article proposes a hybrid framework to automate FDD based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN). First, the knowledge base on technical systems is organized to support the next steps of the framework. Then, the detection and diagnosis processes are performed sequentially through MWPCA and BN. The framework was implemented in the analysis of a simplified model of a hydrogenerator, considering real and simulated data. The results showed that the proposed method was able to detect and diagnose several simulated failures.},
	language = {en},
	urldate = {2022-01-13},
	journal = {Reliability Engineering \& System Safety},
	author = {Melani, Arthur Henrique de Andrade and Michalski, Miguel Angelo de Carvalho and da Silva, Renan Favarão and de Souza, Gilberto Francisco Martha},
	month = nov,
	year = {2021},
	keywords = {Adaptative principal component analysis, Bayesian network, Fault detection and diagnosis, MWPCA, Principal component analysis, sigkdd-rw},
	pages = {107837},
}

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Although FDD is a research area in full growth with the development of several methods and heuristics, the availability of data from systems under a fault condition is still scarce in many applications, mainly related to complex systems. In many circumstances, only data from the system in healthy conditions is available and the applied FDD method should be able to detect variations in system conditions and diagnose faults without the need for previous labeled fault data. In this context, this article proposes a hybrid framework to automate FDD based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN). First, the knowledge base on technical systems is organized to support the next steps of the framework. Then, the detection and diagnosis processes are performed sequentially through MWPCA and BN. The framework was implemented in the analysis of a simplified model of a hydrogenerator, considering real and simulated data. The results showed that the proposed method was able to detect and diagnose several simulated failures.","language":"en","urldate":"2022-01-13","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Melani"],"firstnames":["Arthur","Henrique","de","Andrade"],"suffixes":[]},{"propositions":[],"lastnames":["Michalski"],"firstnames":["Miguel","Angelo","de","Carvalho"],"suffixes":[]},{"propositions":["da"],"lastnames":["Silva"],"firstnames":["Renan","Favarão"],"suffixes":[]},{"propositions":["de"],"lastnames":["Souza"],"firstnames":["Gilberto","Francisco","Martha"],"suffixes":[]}],"month":"November","year":"2021","keywords":"Adaptative principal component analysis, Bayesian network, Fault detection and diagnosis, MWPCA, Principal component analysis, sigkdd-rw","pages":"107837","bibtex":"@article{melani_framework_2021,\n\ttitle = {A framework to automate fault detection and diagnosis based on moving window principal component analysis and {Bayesian} network},\n\tvolume = {215},\n\tissn = {0951-8320},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832021003574},\n\tdoi = {10.1016/j.ress.2021.107837},\n\tabstract = {Through Condition-Based Maintenance strategy, planners can monitor the health of the machinery and recommend actions based on the information obtained. Nevertheless, this approach depends on the successful establishment of Fault Detection and Diagnosis (FDD) processes. Although FDD is a research area in full growth with the development of several methods and heuristics, the availability of data from systems under a fault condition is still scarce in many applications, mainly related to complex systems. In many circumstances, only data from the system in healthy conditions is available and the applied FDD method should be able to detect variations in system conditions and diagnose faults without the need for previous labeled fault data. In this context, this article proposes a hybrid framework to automate FDD based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN). First, the knowledge base on technical systems is organized to support the next steps of the framework. Then, the detection and diagnosis processes are performed sequentially through MWPCA and BN. The framework was implemented in the analysis of a simplified model of a hydrogenerator, considering real and simulated data. The results showed that the proposed method was able to detect and diagnose several simulated failures.},\n\tlanguage = {en},\n\turldate = {2022-01-13},\n\tjournal = {Reliability Engineering \\& System Safety},\n\tauthor = {Melani, Arthur Henrique de Andrade and Michalski, Miguel Angelo de Carvalho and da Silva, Renan Favarão and de Souza, Gilberto Francisco Martha},\n\tmonth = nov,\n\tyear = {2021},\n\tkeywords = {Adaptative principal component analysis, Bayesian network, Fault detection and diagnosis, MWPCA, Principal component analysis, sigkdd-rw},\n\tpages = {107837},\n}\n\n","author_short":["Melani, A. H. d. A.","Michalski, M. A. d. C.","da Silva, R. F.","de Souza, G. F. 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