MACHINE LEARNING-BASED PREDICTIVE MAINTENANCE SYSTEM

Abstract

The invention provides a machine learning-based predictive maintenance system designed to predict equipment failures before they occur by analyzing real-time sensor data and historical maintenance logs. By utilizing advanced machine learning algorithms, the system identifies patterns and anomalies that indicate impending failures, enabling proactive maintenance scheduling. The system continuously updates its predictions through a feedback loop, ensuring improved accuracy over time. It integrates data collection, preprocessing, failure prediction, and maintenance scheduling modules, optimizing maintenance efforts to reduce downtime, lower maintenance costs, and extend the lifespan of machinery across various industrial applications.

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 machine learning-based predictive maintenance system that leverages real-time sensor data and historical maintenance logs to predict failures in equipment before they occur. The system aims to reduce unplanned downtime, optimize maintenance schedules, and minimize maintenance costs by utilizing advanced machine learning algorithms to provide accurate failure predictions.

The system consists of several key modules that work together to ensure accurate predictions and effective maintenance scheduling:

This module is responsible for collecting real-time sensor data from the machinery, including metrics such as temperature, vibration, pressure, and operational status. In addition to sensor data, the module gathers historical maintenance logs, including past failures, repairs, and maintenance tasks performed. This data is crucial for training the machine learning model to understand the patterns that lead to equipment failures.

The data collected from the machinery is typically noisy and may contain inconsistencies. The preprocessing unit cleans and normalizes this data to ensure it is in a suitable format for analysis. This includes removing outliers, filling missing values, and transforming the data into a uniform scale. The data preprocessing unit also converts the raw sensor readings into features that are relevant for machine learning, such as average temperature over a given time period or frequency of abnormal vibration patterns.

The core of the system is the machine learning model that is trained using historical data to recognize patterns indicative of impending failures. Various machine learning algorithms, such as Random Forest, Support Vector Machines, or Deep Neural Networks, can be employed depending on the complexity and nature of the data. The model learns to predict failure events by identifying correlations between sensor data readings and the occurrence of past failures.

The failure prediction unit leverages the trained machine learning model to predict the likelihood of a failure occurring within a specified time window. The system analyzes real-time data continuously and updates its failure predictions based on the most recent sensor readings. The unit provides failure probability scores, such as "high," "medium," or "low," to help prioritize maintenance actions.

This module takes the failure predictions generated by the system and uses them to recommend or automatically schedule maintenance tasks. It aims to minimize downtime by scheduling maintenance only when necessary, rather than based on arbitrary time intervals. The maintenance scheduling module uses optimization techniques to balance equipment availability, resource allocation, and cost while ensuring that failures are addressed before they occur.


A key component of the system is the user interface, which provides maintenance personnel with an intuitive way to view the system's predictions, alerts, and recommended maintenance schedules. The interface allows users to prioritize and plan maintenance activities based on the predicted likelihood of failure and the urgency of the task. It may also include graphical representations such as failure prediction timelines and equipment health dashboards.

The system is designed to continuously improve its predictions through an adaptive feedback mechanism. As new sensor data becomes available, the machine learning model is retrained to incorporate this data, allowing the system to adapt to changes in the operating environment and improve its prediction accuracy. This continuous learning process ensures that the system remains effective even as machinery, environmental conditions, and operational parameters evolve over time.

In this embodiment, the machine learning-based predictive maintenance system is deployed in a large-scale industrial manufacturing facility. The system collects sensor data from various machines on the production floor, including motors, conveyor belts, and pumps. The data is used to train a machine learning model that predicts when a machine is likely to experience a failure, such as overheating in a motor or excessive wear in a conveyor belt. The maintenance scheduling module then generates maintenance recommendations to inspect or repair the equipment at the most optimal times, minimizing downtime and ensuring smooth production. By incorporating both sensor data and historical maintenance logs, the system is able to accurately predict failures and optimize the timing of maintenance activities, leading to a reduction in unscheduled downtime and significant cost savings.

In this embodiment, the predictive maintenance system is used in a power plant to monitor the health of critical equipment, including turbines, boilers, and generators. Sensors placed on the equipment collect real-time data on factors such as vibration, pressure, and temperature. The machine learning model is trained using historical data from similar equipment, allowing it to recognize patterns and predict potential failures. When a failure is predicted, the system provides alerts to maintenance personnel and suggests preventive actions to take before a major breakdown occurs. In a power plant, where downtime can be extremely costly, the system helps optimize maintenance scheduling, avoid costly unplanned outages, and extend the lifespan of critical assets. The system's ability to adapt to changing conditions in real time ensures that maintenance actions are based on the most up-to-date data, leading to more accurate predictions and efficient operations.

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 machine learning-based predictive maintenance system, comprising:
A data collection module configured to receive real-time sensor data from machinery, including at least one of temperature, vibration, pressure, and operational status;
A data preprocessing unit configured to clean and normalize the collected data;
A machine learning model trained to predict equipment failures based on historical and real-time data, wherein the model is adapted to provide failure predictions with a predetermined accuracy threshold;
A failure prediction unit configured to estimate the likelihood of failure at specific time intervals based on the output of the machine learning model; and
A maintenance scheduling module configured to generate maintenance schedules based on the predicted failure probabilities, wherein the schedules minimize operational downtime and maintenance costs.

2.The system of claim 1, wherein the machine learning model comprises a classification algorithm, selected from the group consisting of Random Forest, Support Vector Machine, and Deep Neural Networks.

3.The system of claim 1, further comprising a feedback loop wherein the system automatically updates the machine learning model based on new data collected from the machinery.

4.The system of claim 1, wherein the failure prediction unit provides real-time alerts to the maintenance team based on the likelihood of impending failure.

5.The system of claim 1, wherein the data collection module further receives historical maintenance logs and equipment usage data to enhance the machine learning model's prediction accuracy.

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