PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL POWER SYSTEMS USING AI AND IOT SENSORS

Abstract

ABSTRACT “PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL POWER SYSTEMS USING AI AND IOT SENSORS” The present invention provides an intelligent predictive maintenance system for industrial power systems, integrating IoT sensors, cloud-based AI analytics, and a user interface (UI) for real-time monitoring and maintenance optimization. The system utilizes a network of IoT sensors—monitoring parameters like temperature, vibration, voltage, and current—placed on components such as transformers, circuit breakers, and cables. Data from these sensors is processed by an AI-powered engine that employs machine learning algorithms to detect anomalies, predict potential failures, and generate maintenance alerts. The UI displays real-time alerts and provides actionable insights, while the maintenance scheduler optimizes task prioritization based on failure predictions and operational requirements. The system further includes temperature, oil quality, vibration, and arc fault sensors for condition monitoring and historical data analysis to support data-driven maintenance decisions, ensuring minimal operational disruption and increased reliability. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to predictive maintenance of industrial power systems, and more particularly, to a system that integrates Artificial Intelligence (AI) and Internet of Things (IoT) sensors to monitor, predict, and prevent potential failures in power system components, ensuring operational reliability and minimizing downtime.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] In industrial settings, power system reliability is crucial for uninterrupted operations. Power system components, including transformers, circuit breakers, and cables, are prone to faults due to aging, environmental conditions, and operational stress. Traditional maintenance practices are either scheduled periodically or are reactive, addressing problems only after failures occur. Approaches often result in unplanned downtime, higher maintenance costs, and operational inefficiencies. Another available solution includes manual monitoring of operational parameters, which is time-consuming and prone to human error. Some systems may use SCADA for remote monitoring but lack predictive analytics and real-time anomaly detection, limiting their ability to prevent failures.
[0004] In light of the foregoing, there is a need for Predictive maintenance system for industrial power systems using AI and IoT sensors that overcome problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0005] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0006] The principal object of the present invention is to overcome the disadvantages of the prior art by providing predictive maintenance system for industrial power systems using AI and IoT sensors.
[0007] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors that detects potential electrical faults.
[0008] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors that prevents catastrophic failures, ensuring a safer working environment.
[0009] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors that lowers overall maintenance costs and extends the lifespan of power system components.
[0010] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors that reduces unplanned outages in industrial operations.
[0011] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors is scalable and can be adapted to monitor power systems in various industrial sectors, from manufacturing plants to data centers.
[0012] Another object of the present invention is to provide predictive maintenance system for industrial power systems using AI and IoT sensors that helps in better resource management for maintenance teams, ensuring that repairs are carried out efficiently.
[0013] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0014] The invention relates to an intelligent predictive maintenance system for industrial power systems, designed to improve reliability and optimize maintenance operations. The system consists of several key components:
[0015] IoT Sensor Network: A set of IoT sensors is deployed across critical power system components, including transformers, circuit breakers, cables, and switchgear. These sensors monitor key parameters such as temperature, vibration, voltage, current, and environmental factors. Specific sensors include temperature and oil quality sensors for transformers, vibration and arc fault sensors for circuit breakers, and voltage and current sensors for cables.
[0016] Cloud-Based AI Engine: The system features a cloud-based artificial intelligence engine that processes real-time sensor data. This engine uses machine learning techniques, such as neural networks and anomaly detection models, to identify abnormal patterns, predict potential component failures, and generate maintenance alerts. The engine is capable of processing both historical and real-time data, improving the accuracy of failure predictions over time.
[0017] User Interface (UI): The system provides a user interface that enables real-time monitoring of power system health. The UI displays alerts, maintenance recommendations, and insights derived from the AI engine. It also includes historical data analysis tools for reviewing sensor readings, maintenance records, and failure trends, aiding in data-driven decision-making.
[0018] Maintenance Scheduler: The system incorporates a maintenance scheduler that prioritizes maintenance tasks based on predicted failure risks, operational schedules, and available resources. By integrating predictive maintenance with operational logistics, the scheduler minimizes disruptions to the industrial process and ensures that critical maintenance is performed before failures occur.
[0019] Alerting and Notification: The AI engine generates actionable alerts that notify maintenance teams through the UI, email, or SMS when a high-risk failure is predicted, enabling timely intervention.
[0020] Overall, the system integrates IoT technology, machine learning, and advanced analytics to enhance the efficiency of predictive maintenance, improve system uptime, reduce unplanned downtime, and optimize resource allocation in industrial power systems.
[0021] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0022] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0023] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0024] Fig.1: The necessity for a time-ordered analysis of sensor data;
[0025] Fig.2: Overview of the proposed method; and
[0026] Fig.3: Architecture of Edge Computing technique.
DETAILED DESCRIPTION OF THE INVENTION
[0027] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0028] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0029] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0030] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0031] The present invention relates to predictive maintenance system for industrial power systems using AI and IoT sensors.
[0032] IoT Sensors for Data Acquisition: The predictive maintenance system utilizes a network of distributed IoT sensors placed strategically on power system components such as:
- Transformers: Temperature sensors, oil quality sensors, and load monitors.
- Circuit Breakers: Vibration sensors and arc fault detectors.
- Electrical Cables and Switchgear: Voltage and current sensors, as well as environmental sensors for humidity and temperature.
- These sensors continuously monitor the operational parameters of power system components and transmit data wirelessly to a central server or cloud-based AI engine.
[0033] AI-Powered Predictive Analytics Engine: The AI engine is a critical component of the system that performs the following tasks:
- Data Preprocessing: Cleans and preprocesses sensor data to remove noise and outliers.
- Feature Extraction: Extracts relevant features such as temperature rise, load imbalances, or unusual vibration patterns, which indicate wear and tear.
- Anomaly Detection: Utilizes machine learning algorithms to detect abnormal patterns in sensor data that could indicate impending failures.
- Failure Prediction: Based on historical data and learned trends, the AI model predicts the likelihood of a component failure within a given time window (e.g., days, weeks).
- Actionable Insights: The system generates predictive alerts and offers maintenance recommendations, including the type of maintenance required (e.g., oil change in transformers, switchgear cleaning) and the urgency level.
[0034] User Interface (UI): The system provides a real-time monitoring interface accessible via web or mobile applications, offering the following features:
- Real-Time Monitoring: Displays current operational parameters from the IoT sensors in real time.
- Predictive Alerts: Notifications are sent to maintenance teams via the dashboard, email, or SMS when potential failures are detected.
- Historical Analysis: Users can review historical trends and past maintenance records for each component to make informed decisions.
- Maintenance Recommendations: The UI provides specific maintenance actions with timelines based on the AI predictions, helping teams schedule maintenance at optimal times to avoid system downtime.
[0035] Maintenance Scheduler: An integrated maintenance scheduler leverages the AI predictions to suggest the best possible maintenance windows. It factors in the urgency of the maintenance, the operational schedule of the industrial plant, and resource availability (e.g., maintenance staff, spare parts).
- Prioritization of Maintenance Tasks: Components at higher risk of failure are prioritized in the schedule.
- Automated Work Orders: Upon detection of a critical failure risk, the system can automatically generate work orders for maintenance personnel with detailed instructions and deadlines.
[0036] Advantages of the Invention
- Minimized Downtime: By predicting failures before they occur, the system reduces unplanned outages in industrial operations.
- Cost Efficiency: Optimizing maintenance schedules based on predictive data lowers overall maintenance costs and extends the lifespan of power system components.
- Enhanced Safety: Early detection of potential electrical faults prevents catastrophic failures, ensuring a safer working environment.
- Scalability: The system is scalable and can be adapted to monitor power systems in various industrial sectors, from manufacturing plants to data centers.
- Improved Resource Allocation: Automated scheduling and predictive insights help in better resource management for maintenance teams, ensuring that repairs are carried out efficiently.
[0037] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.
, Claims:CLAIMS
We Claim:
1) An intelligent predictive maintenance system for industrial power systems, the system comprising:
- a network of IoT sensors placed on power system components, including transformers, circuit breakers, and cables, to collect real-time data on parameters such as temperature, vibration, voltage, current, and environmental factors;
- a cloud-based AI-powered predictive analytics engine configured to process the real-time data, detect anomalies, predict potential failures, and generate maintenance alerts;
- a user interface (UI) that provides real-time monitoring, predictive alerts, and maintenance recommendations; and
- a maintenance scheduler that prioritizes and optimizes maintenance tasks based on AI-driven failure predictions.
2) The system as claimed in claim 1, wherein the IoT sensors include:
- Temperature and oil quality sensors for monitoring transformer health.
- Vibration and arc fault sensors for circuit breaker condition monitoring.
- Voltage and current sensors for detecting cable and switchgear anomalies.
3) The system as claimed claim 1, wherein the AI engine uses machine learning algorithms, including neural networks and anomaly detection models, to predict component failures based on historical and real-time sensor data.
4) The system as claimed claim 1, wherein the AI engine generates actionable insights and alerts, notifying maintenance teams through the UI, email, or SMS when critical failure risks are detected.
5) The system as claimed claim 1, wherein the maintenance scheduler prioritizes maintenance tasks based on the risk of component failure, operational schedules, and resource availability, allowing for minimal disruption to industrial operations.
6) The system as claimed claim 1, wherein the user interface provides historical analysis of sensor data, maintenance records, and failure trends, assisting users in making data-driven maintenance decisions.

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