Smart maintenance strategies in combined cycle power plant

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Fahmi A. W. K.; Reza Kashyzadeh K.; Ghorbani S.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Computational and Applied Research in Mechanical Engineering Year:2024 | Volume:14 | Issue:1 Page(s): 35-46

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Smart maintenance strategies in combined cycle power plant

Author(s)

Fahmi A. W. K. | Reza Kashyzadeh K. | Ghorbani S. | Issue Writer Certificate

Keywords

Anomaly detection

Machine Learning

Eddy current proximity transducers

Blade tip timing

Laser doppler vibrometer

Abstract

This research investigates the effectiveness of various vibration data acquisition techniques coupled with different Machine Learning models for detecting anomalies and classifying them. To this end, synthetic vibration data was generated for techniques such as Eddy current proximity transducers (ECPT), accelerometer sensor, Blade tip timing, Laser doppler vibrometer (LDV), and strain gauge. Afterward, the data was pre-processed and used to train gradient boosting machine, support vector machine, and random forest models. Performance evaluation metrics, including accuracy, recall, F1-score, receiver operating characteristic, and area Under curve were employed to assess the models, revealing varying degrees of success across combining techniques and models. Notable achievements were observed for the random forest model coupled with the Eddy current proximity transducers technique, underscoring the significance of informed technical selection and model optimization in enhancing vibration Anomaly detection systems in combined cycle power plants. The results showed that the LDV technique has a significant increase in accuracy from about 0.49 to approximately 0.52, while the ECPT technique has improved from about 0.9 to close 1.0. These advances highlight the growing accuracy of the methods and enable the development of more efficient and reliable learning machines.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans