DATA DRIVEN MACHINE LEARNING FOR FAULT DETECTION AND CLASSIFICATION IN BINARY DISTILLATION COLUMN
S.Silvester Bennys jakes
, M.Mythily , D.Vasanth , D.Manamalli
UOP3CC binary distillation column,Normal and faulty data,Machine learning algorithm, Fault classification.
Any system that runs is always be expected to experience faults in different ways. Any change in the physical state of numerous components, control machinery, as well as environmental factors, might result in these problems. In process industries, where prompt detection is crucial in maintaining high product quality, dependability, and safety under various operating situations, finding these flaws is one of the most difficult tasks. The goal of this project is to implement several machine learning techniques for fault identification and classification in abinary distillation column. A pilot binary distillation unit (UOP3CC) is utilized for this purpose. The set up is run under normal operating conditions and the real time data is collected. Three common faults namely reboiler fault, feed pump fault and sensor fault are introduced one at a time and the faulty data is collected. These data are then introduced in to different machine learning algorithms like Logistic Regression, KNN, Naive Bayes, Decision Tree, Gradient Boosting, X Gradient Boosting, SVC and Light Gradient Boosting for model development. 70% of the data samples used for training and 30% of data samples areused for testing. It is found the Decision tree algorithm gives the best accuracy possible with 99.9%.Using decision tree algorithm, fault classification is performed for different datasets and is found that the algorithm was able to classify accurately even for new untrained datasets.
"DATA DRIVEN MACHINE LEARNING FOR FAULT DETECTION AND CLASSIFICATION IN BINARY DISTILLATION COLUMN", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.190-195, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001030.pdf
Volume 11
Issue 2,
February-2024
Pages : 190-195
Paper Reg. ID: TIJER_150982
Published Paper Id: TIJERB001030
Downloads: 000312
Research Area: Science and Technology
Country: THIRUVALLUR, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
