MACHINE LEARNING-BASED AI ALGORITHM FOR PREDICTIVE MAINTENANCE

Abstract

The present invention relates to a machine learning-based AI algorithm for predictive maintenance that utilizes real-time sensor data, historical failure information, and advanced machine learning techniques to predict equipment failures and optimize maintenance scheduling. By continuously learning from new data, the system enhances its accuracy over time, providing proactive failure predictions, identifying potential issues before they occur, and recommending optimal maintenance actions. This innovation reduces unplanned downtime, lowers maintenance costs, and extends the lifespan of machinery across various industries, including manufacturing, aerospace, and energy.

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 invention provides a machine learning-based AI algorithm for predictive maintenance, which improves the accuracy and reliability of failure predictions and optimizes maintenance scheduling. The core functionality of the invention involves integrating real-time sensor data, historical failure data, and machine learning techniques to provide actionable insights into the operational status of machinery. The system is designed to adapt over time, continuously learning from new data to refine its predictions and maintenance recommendations.

The system collects real-time data from various sensors that monitor critical parameters of the machine or system, such as temperature, pressure, vibration, sound, and operational speed. These sensors are typically embedded within the machinery and transmit data to a central processing unit. The collected data is often noisy, containing outliers or missing values, and must be preprocessed to ensure that it is clean, normalized, and ready for analysis. This preprocessing phase involves removing outliers, handling missing data, and converting raw sensor readings into features that can be used by the machine learning model.

Feature extraction plays a crucial role in transforming the raw sensor data into meaningful information that helps in predicting machine failures. The features extracted may include trends, statistical moments (mean, variance, skewness), and time-series characteristics (e.g., peak, slope). Additionally, anomalies and correlations across different sensor signals are analyzed to detect early warning signs of potential failure. The extracted features serve as inputs to the machine learning model, allowing it to identify patterns in the data that correlate with failures.

The system utilizes machine learning algorithms such as decision trees, random forests, support vector machines (SVM), and neural networks to train a predictive model. The model is trained using historical data that includes both operational conditions and known failure events. By analyzing this data, the model learns to associate specific sensor patterns with particular types of failures. The training process also involves validating the model's performance to ensure it can generalize well to new, unseen data.

Once the model is trained, it is deployed to make real-time predictions based on incoming sensor data. The system predicts the likelihood of a failure event and estimates the remaining useful life (RUL) of specific components. The machine learning model evaluates the sensor data against the learned patterns and identifies whether the machinery is at risk of failing. The system also provides diagnostic information, indicating which parts of the machine are likely to fail and the root cause of the failure, if possible.

Based on the predicted likelihood of failure and remaining useful life, the system generates maintenance recommendations. These recommendations can range from immediate repairs or shutdowns to scheduled maintenance activities. The system also prioritizes the suggested actions based on the criticality of the failure, its impact on the overall operation, and the cost-effectiveness of addressing the issue at different times. This helps ensure that resources are allocated efficiently and that maintenance activities are carried out at the optimal time to minimize downtime.

One of the most significant features of the invention is its ability to continuously learn from new data. As additional sensor data is collected and more maintenance activities are carried out, the machine learning model updates itself, refining its predictions and adapting to new failure modes. This continuous learning process ensures that the predictive maintenance system remains effective over time and adapts to changes in machine behavior, wear patterns, and operating environments.

In a manufacturing environment, machines such as CNC (Computer Numerical Control) machines or assembly line robots are equipped with sensors that monitor parameters like motor temperature, vibration levels, and operating speed. The machine learning-based AI algorithm processes this sensor data to predict component failures such as motor burnout, gear malfunction, or sensor degradation. The system provides real-time maintenance recommendations, such as replacing the motor or lubricating gears before a failure occurs. This proactive approach minimizes downtime and ensures the production line operates smoothly, thereby reducing operational costs and improving productivity.

In the aerospace industry, an aircraft engine is fitted with multiple sensors that track various parameters such as oil pressure, fuel consumption, temperature, and exhaust emissions. The predictive maintenance system collects data from these sensors during flight and post-flight operations. Using the machine learning algorithm, the system predicts potential failures like turbine blade wear, exhaust system blockage, or fuel system malfunctions. It provides maintenance recommendations, such as inspecting critical components or scheduling engine overhauls based on remaining useful life predictions. This approach increases aircraft reliability, reduces maintenance costs, and enhances passenger safety by preventing unexpected breakdowns.

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 method for predictive maintenance using machine learning-based AI algorithms, comprising the steps of:
Collecting real-time data from one or more sensors monitoring parameters of a machine or system;
Preprocessing the collected data to extract relevant features indicative of machine health;
Training a machine learning model with historical data, including past failure events and maintenance logs;
Predicting the likelihood of a failure of the machine based on the output of the trained machine learning model;
Suggesting an optimal maintenance action based on the predicted failure likelihood.

2.The method of claim 1, wherein the machine learning model is selected from the group consisting of decision trees, random forests, support vector machines, and neural networks.

3.The method of claim 1, wherein the machine learning model is trained using a combination of supervised learning and unsupervised learning techniques.

4.The method of claim 1, wherein the real-time data includes at least one of temperature, vibration, pressure, and sound measurements.

5.A system for predictive maintenance comprising:
A plurality of sensors configured to collect real-time data from a machine or system;
A data processing unit configured to preprocess the collected data and extract relevant features;
A machine learning unit configured to train a machine learning model using historical data, and make predictions based on the preprocessed real-time data;
A maintenance recommendation unit configured to suggest maintenance actions based on the predictions.

6.The system of claim 5, wherein the machine learning unit continuously updates the trained model using new data collected from the sensors.

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