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MACHINE LEARNING-ENABLED PREDICTIVE MAINTENANCE FOR IOT DEVICES

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MACHINE LEARNING-ENABLED PREDICTIVE MAINTENANCE FOR IOT DEVICES

ORDINARY APPLICATION

Published

date

Filed on 14 November 2024

Abstract

The invention provides a system and method for predictive maintenance of Internet of Things (IoT) devices using machine learning algorithms. By collecting real-time sensor data from IoT devices, the system analyzes this data to predict potential failures or performance degradation before they occur. The machine learning model continuously learns from new data, improving the accuracy of its predictions over time. Based on these predictions, the system triggers timely maintenance actions, such as sending alerts or autonomously initiating corrective measures, thus reducing downtime, maintenance costs, and improving the reliability and lifespan of IoT devices.

Patent Information

Application ID202441088020
Invention FieldCOMPUTER SCIENCE
Date of Application14/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
Mr. Venkataradha krishnamurtyAssociate Professor, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
G. Deekshith ReddyFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
V. Harshavardhan ReddyFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
V.Seshasai PrudwikrishnaFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
Velugoti Venkata Narendra PrabhasFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
Vemula SimhadriFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
Venu GFinal Year B.Tech Student, Department of Computer Science &Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
Yarava AswiniFinal Year B.Tech Student, Department of Computer Science & Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist., Andhra Pradesh, India-524101, India.IndiaIndia
Lambu Murali KrishnaFinal Year B.Tech Student, Department of Computer Science & Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist, Andhra Pradesh, India-524101, India.IndiaIndia
Malla Devi PrasadFinal Year B.Tech Student, Department of Computer Science & Engineering (Data Science), Audisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist, Andhra Pradesh, India-524101, India.IndiaIndia

Applicants

NameAddressCountryNationality
Audisankara College of Engineering & TechnologyAudisankara College of Engineering & Technology, NH-16, By-Pass Road, Gudur, Tirupati Dist, Andhra Pradesh, India-524101, India.IndiaIndia

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention provides a robust and scalable solution for predictive maintenance of Internet of Things (IoT) devices by leveraging machine learning algorithms. The system is designed to monitor the operational state of IoT devices in real-time, utilizing sensor data collected from various IoT sensors to predict potential device failures or performance degradation. The invention includes several key components, each playing a critical role in enabling predictive maintenance.

The data collection module is responsible for receiving real-time operational data from a plurality of IoT devices. Each IoT device is equipped with one or more sensors that monitor different parameters such as temperature, vibration, pressure, and humidity. The collected data is transmitted to a central processing system, which can either be based on cloud infrastructure, edge computing, or a hybrid model depending on the system requirements. This module ensures that the data is collected continuously and without interruption, providing a constant stream of information about the operational state of the IoT devices.

Once the data is collected, it undergoes preprocessing. The data preprocessing module cleans the raw sensor data to eliminate noise, handle missing values, normalize or standardize the data, and convert it into a form suitable for analysis. This may involve techniques such as feature extraction, dimensionality reduction, and time-series analysis. The goal of preprocessing is to prepare the data in a way that allows the machine learning model to effectively analyze and learn from it.

The heart of the invention lies in the machine learning model, which analyzes the preprocessed data to predict potential failures or performance degradation of the IoT devices. The machine learning model can be based on various algorithms, including supervised learning, unsupervised learning, or deep learning. The model is trained using historical sensor data and failure records to learn patterns associated with device malfunctions. Over time, as more data is collected, the model is updated to improve its predictive accuracy. The model identifies patterns of anomalies, such as unusual temperature spikes or vibration patterns, that may indicate impending failures.

Upon receiving predictions of potential failures or degradation, the system takes appropriate action. In some cases, the system may send alerts to maintenance personnel, advising them to schedule maintenance activities. In other cases, the system may autonomously initiate maintenance actions, such as recalibrating the device, replacing worn-out parts, or adjusting settings to prevent further degradation. The system's ability to act proactively ensures that maintenance tasks are carried out before a failure leads to significant downtime or costly repairs.

One of the key advantages of the invention is its continuous learning capability. As new data is gathered from IoT devices, the machine learning model is retrained to adapt to changes in the operational environment. This feedback loop improves the accuracy and reliability of the predictive maintenance system, ensuring that it becomes more adept at identifying subtle signs of device failure over time. Additionally, the model can be fine-tuned based on real-world outcomes to continually enhance prediction performance.

The system is designed to integrate seamlessly with existing IoT ecosystems. It supports multiple communication protocols such as MQTT, HTTP, or CoAP, allowing it to interface with a wide variety of IoT devices and maintenance management systems. This ensures that the predictive maintenance system is flexible, scalable, and can be adopted in diverse industrial settings. Whether in a factory with a fleet of connected machines or a healthcare setting with medical IoT devices, the system can adapt to different device types and communication standards.

In one embodiment, the system is deployed in a manufacturing plant where numerous machines are equipped with IoT sensors to monitor parameters such as vibration, temperature, and motor load. The data from these sensors is transmitted to a cloud-based platform where it is processed and analyzed. The machine learning model is trained on historical data from similar machines, identifying patterns that correspond to the early stages of component wear, motor failure, or other mechanical issues.

Once the system detects an anomaly that suggests an impending failure-such as an unusual increase in vibration levels-the predictive maintenance system sends an alert to the maintenance team. The team can then schedule maintenance during non-productive hours, minimizing downtime. Additionally, the system can automatically adjust operational parameters or slow down the machine to reduce stress on failing components, thereby preventing a complete breakdown.

In another embodiment, the predictive maintenance system is applied to a healthcare environment where medical IoT devices, such as infusion pumps, temperature monitors, and patient monitoring systems, are used in patient care. These devices are equipped with sensors to monitor various health parameters, including blood pressure, pulse rate, and temperature.

The system collects data from these devices and analyzes it using machine learning algorithms to predict potential issues like sensor drift, battery failure, or malfunctioning parts. For example, if the system detects irregular fluctuations in data, it may predict an impending sensor failure. The system can automatically notify hospital staff of the potential issue, allowing them to take preemptive action, such as replacing the sensor or recalibrating the device. By ensuring these devices operate reliably, the predictive maintenance system contributes to patient safety and optimizes the management of healthcare resources.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A system for predictive maintenance of Internet of Things (IoT) devices, comprising:
a data collection module configured to receive sensor data from one or more IoT devices;
a data processing module configured to preprocess the sensor data into a format suitable for analysis;
a machine learning model configured to analyze the preprocessed sensor data and predict a potential failure or performance degradation of the IoT device;
a maintenance scheduling module configured to trigger maintenance actions based on the prediction from the machine learning model.

2.The system of claim 1, wherein the machine learning model is trained using historical sensor data from the IoT devices.

3.The system of claim 1, wherein the machine learning model is one of a supervised learning model, unsupervised learning model, or deep learning model.

4.The system of claim 1, further comprising a notification module configured to send maintenance alerts to a user or automated system upon detection of a potential failure.

Documents

NameDate
202441088020-COMPLETE SPECIFICATION [14-11-2024(online)].pdf14/11/2024
202441088020-DECLARATION OF INVENTORSHIP (FORM 5) [14-11-2024(online)].pdf14/11/2024
202441088020-DRAWINGS [14-11-2024(online)].pdf14/11/2024
202441088020-FORM 1 [14-11-2024(online)].pdf14/11/2024
202441088020-FORM-9 [14-11-2024(online)].pdf14/11/2024
202441088020-REQUEST FOR EARLY PUBLICATION(FORM-9) [14-11-2024(online)].pdf14/11/2024

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