Comprehensive Study Of Predictive Maintenance In Industries Using
  Classification Models And LSTM Model
Saket Maheshwari Sambhav Tiwari Shyam Rai Satyam Vinayak Daman Pratap Singh
In today's technology-driven era, the imperative for predictive maintenance
and advanced diagnostics extends beyond aviation to encompass the
identification of damages, failures, and operational defects in rotating and
moving machines. Implementing such services not only curtails maintenance costs
but also extends machine lifespan, ensuring heightened operational efficiency.
Moreover, it serves as a preventive measure against potential accidents or
catastrophic events. The advent of Artificial Intelligence (AI) has
revolutionized maintenance across industries, enabling more accurate and
efficient prediction and analysis of machine failures, thereby conserving time
and resources. Our proposed study aims to delve into various machine learning
classification techniques, including Support Vector Machine (SVM), Random
Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for
predicting and analyzing machine performance. SVM classifies data into
different categories based on their positions in a multidimensional space,
while Random Forest employs ensemble learning to create multiple decision trees
for classification. Logistic Regression predicts the probability of binary
outcomes using input data. The primary objective of the study is to assess
these algorithms' performance in predicting and analyzing machine performance,
considering factors such as accuracy, precision, recall, and F1 score. The
findings will aid maintenance experts in selecting the most suitable machine
learning algorithm for effective prediction and analysis of machine
performance.