AIdriven Strategy for Predicting Equipment Failure in Manufacturing
Authors: 
Mandar Diwakar, Shilpa Sharma, Ritika Dhabliya, Reshma Sonar, Shrinivas T. Shirkande, Saurabh BhattacharyaAuthors Info & Claims
Mandar Diwakar, Shilpa Sharma, Ritika Dhabliya, Reshma Sonar, Shrinivas T. Shirkande, Saurabh BhattacharyaAuthors Info & ClaimsArticle No.: 105, Pages 1 - 5
Abstract
The manufacturing industry, which drives global economic activity, struggles with equipment failures. Due to inactivity, these failures can cause financial losses, safety risks, and production losses. Artificial Intelligence (AI) is crucial for predictive maintenance which is a foremost solution to this problem. This study investigates a novel AI-based approach to proactively predict manufacturing equipment breakdowns. To analyze the effectiveness of AI in predictive maintenance failure this paper compares Convolutional Neural Networks (CNN), Random Forest, XGBoost, and a new hybrid model that combines the features of CNN and Random Forest. Historical sensor readings, maintenance logs, and failure records were collected and examined for the research. Here used deep learning algorithm's CNN to automatically extract complex patterns, anomalies, and trends from the dataset. The study showed that the hybrid model had a accuracy rate of 99.08%. The positive result suggests industrial predictive maintenance strategies may advance. This study can help industrial companies implement effective and efficient equipment failure prediction solutions. This study adds to predictive maintenance literature and shows how AI improves manufacturing process dependability and environmental friendliness. This study could change maintenance practices and usher in a new era of data-driven decision-making in the industrial industry as companies prioritize efficiency and downtime.
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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