Predicting Motor Failures through Sensor Data and Machine Learning, a Growing Shift Toward Smarter Industrial Maintenance

Akansh Panchal

Abstract: Motors are critical components in industrial operations, and unplanned failures can cause severe production and financial losses. This study explores how data collected from vibration, temperature, and pressure sensors can be used to predict motor failures through machine learning (ML) algorithms. It reviews how each sensor type contributes unique diagnostic information and how algorithms such as Random Forests, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) process this data to detect faults in advance. This paper synthesizes insights from reports of certain companies, studies conducted by certain people and various organizations. The paper also highlights case studies from Tesla, BMW, and Amazon, which demonstrate real-world AI-driven predictive maintenance strategies. The findings underline the growing industrial transition toward data-driven maintenance systems that improve equipment reliability, safety, and operational efficiency.

Keywords: Predictive Maintenance, IoT Sensors, Machine Learning, Motor Failure Detection, Vibration Analysis

How to Cite?: Akansh Panchal, "Predicting Motor Failures through Sensor Data and Machine Learning, a Growing Shift Toward Smarter Industrial Maintenance", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 1898-1901, https://www.ijsr.net/getabstract.php?paperid=SR251127204250, DOI: https://dx.doi.org/10.21275/SR251127204250