DEEP LEARNING-BASED PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL EQUIPMENT

Abstract

The present invention relates to a deep learning-based predictive maintenance system for industrial equipment, utilizing real-time sensor data such as temperature, vibration, and pressure to predict potential failures and optimize maintenance schedules. The system leverages advanced deep learning models, such as convolutional neural networks (CNN) or long short-term memory (LSTM) networks, to analyze complex sensor data, detect early signs of equipment degradation, and generate timely maintenance alerts. By continuously adapting and retraining the predictive model with new data, the system improves the accuracy of failure predictions over time, reducing downtime, minimizing maintenance costs, and enhancing operational efficiency in industrial environments.

Specification

Description:The present invention relates to systems and methods for predictive maintenance of industrial equipment. More particularly, the invention involves the application of deep learning techniques to analyze sensor data from industrial machines and predict potential failures, thereby optimizing maintenance schedules, reducing downtime, and improving operational efficiency in industrial environments.

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.

Industrial equipment such as motors, pumps, turbines, and compressors play a critical role in manufacturing and production environments. These machines often opera , Claims:1. A deep learning-based predictive maintenance system for industrial equipment, comprising:
a plurality of sensors configured to collect real-time data from an industrial machine, including temperature, vibration, and pressure readings;
a central processing unit (CPU) that receives data from the sensors;
a deep learning model, including one or more neural networks selected from convolutional neural networks (CNN) or long short-term memory (LSTM) networks, trained on historical and real-time sensor data to predict the likelihood of machine failure;
a predictive algorithm configured to analyze the data and provide an output indicating the remaining useful life (RUL) of the equipment;
a maintenance scheduling module configured to trigger alerts for preventive maintenance based on the prediction of impending failures.

2. The system of claim 1, wherein the deep learning model is trained to identify patterns in time-series data from sensors, including vibration or temperature variations that precede equipment fa

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