MACHINE LEARNING APPROACH FOR PREDICTIVE MAINTENANCE IN MANUFACTURING

Abstract

The invention provides a machine learning-based system for predictive maintenance in manufacturing, designed to minimize equipment downtime and optimize maintenance schedules. The system integrates sensors to collect real-time operational data, a preprocessing module to enhance data quality, and a machine learning framework comprising anomaly detection and predictive models. These models analyze historical and real-time data to identify deviations, estimate failure probabilities, and recommend proactive maintenance actions. A cloud-based platform aggregates and synchronizes data across multiple facilities, enabling scalability and collaborative learning. The user interface displays real-time equipment status, failure probabilities, and maintenance recommendations, ensuring actionable insights. The system dynamically updates its models using new data, improving accuracy and adaptability to diverse manufacturing environments.

Specification

Description:The embodiments of the present invention generally relates to predictive maintenance systems in manufacturing, particularly systems that leverage advanced machine learning algorithms to monitor equipment performance, analyze operational data, and predict maintenance requirements, enabling proactive measures to prevent equipment failure and optimize manufacturing efficiency.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

The manufacturing sector is heavily reliant on the uninterrupted operation of machinery to ensure consistent production quality and meet market demands. Traditional maintenance strategi , Claims:1. A system for predictive maintenance in manufacturing, comprising:
a plurality of sensors configured to collect operational data from manufacturing equipment;
a central processing unit configured to receive the operational data;
a machine learning module comprising an anomaly detection model trained on historical operational data and a predictive model for estimating equipment failure probabilities;
a data storage system for storing historical and real-time operational data;
a user interface configured to display predictive maintenance schedules and alerts;
wherein the machine learning module dynamically updates the predictive model based on newly received operational data to enhance prediction accuracy.

2. The system of claim 1, wherein the machine learning module employs supervised learning algorithms such as decision trees and neural networks for failure prediction.

3. The system of claim 1, further comprising a preprocessing module configured to filter and normalize raw sensor data before analysis.

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