Performance Analysis of Machine Learning and Deep Learning Techniques in Diagnosing Imbalance Using Machine Fault Simulator-A Case Study

Performance Analysis of Machine Learning and Deep Learning Techniques in Diagnosing Imbalance Using Machine Fault Simulator-A Case Study
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-1
Year of Publication : 2025
Author : Vijayalakshmi K, Amuthakkannan Rajakannu, Ramachandran KP, Mohsina Kamarudden, Sri Rajkavin AV
DOI : 10.14445/22315381/IJETT-V73I1P123

How to Cite?
Vijayalakshmi K, Amuthakkannan Rajakannu, Ramachandran KP, Mohsina Kamarudden, Sri Rajkavin AV, "Performance Analysis of Machine Learning and Deep Learning Techniques in Diagnosing Imbalance Using Machine Fault Simulator-A Case Study," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 1-13, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P123

Abstract
The growth of Machine and Deep Learning in the manufacturing sector has been tremendous in the past decade, and it is widely used in many fields. Conditional monitoring of machines is a challenging task in manufacturing and process industries. It requires real-time monitoring to reduce downtime of machines, reduce cost and scraps, and improve the productive ML and DL, which have given promising results in the core domains of feature extraction and fault classification in machine fault detection. This paper addresses applying ML and DL techniques to predict the unbalancing of machines accurately and finding the proper techniques for predicting the unbalancing of rotating machines. This research uses an accelerometer, data acquisition card, and lab view software to collect vibration signals due to unbalancing. The output of the vibration data is collected in terms of frequency domain and time domain data. The ML techniques KNN, Support vector machine, Decision Tree, Random Forest, Naïve Bayes, logistic regression, and linear discriminant analysis are applied and predict the accurate prediction of unbalancing of machines. Similarly, DL techniques, MLP, CNN, RNN, and LSTM are used to identify the unbalancing of machines. After predicting the accuracy, precision, recall, and FN score of ML and DL, an extensive comparative analysis is done to identify the proper AI techniques in real-time condition monitoring; this research is executed by collecting data from the Spectra quest machine fault simulator. The result shows that ML techniques DT and RF give better results than other ML techniques. Similarly, MLP provides better results than CNN, RNN, and LSTM.

Keywords
Condition monitoring, Machine fault simulator, Machine Learning, Deep Learning.

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