Predictive maintenance system using IoT sensors and cloud-based analysis

Abstract

The present disclosure provides a system for predictive maintenance of industrial equipment. The system for predictive maintenance of industrial equipment comprises a plurality of IoT sensors installed on industrial equipment, each IoT sensor collecting operational data and transmitting collected data with an associated unique identifier corresponding to the equipment. A cloud server communicates with the IoT sensors, receives and stores operational data, preprocesses received data by filtering out erroneous values, performs feature selection to identify relevant parameters, analyzes preprocessed data using machine learning techniques to identify patterns related to past failures, and generates predictive alerts based on analyzed data to indicate potential failures. An alert generation unit automatically notifies users of predicted failures and recommends maintenance actions via a user interface accessible on computing devices.

Description

PREDICTIVE MAINTENANCE SYSTEM USING IOT SENSORS AND CLOUD-BASED ANALYSIS TECHNICAL FIELD

[0001] The present disclosure generally relates to industrial equipment monitoring systems. Further, the present disclosure particularly relates to a predictive maintenance system using Internet of Things (IoT) sensors and cloud-based data analysis. BACKGROUND

[0002] Predictive maintenance techniques are increasingly being adopted across various industries for operational efficiency and reduction in unexpected equipment failures. Industrial equipment, including machinery used in manufacturing, power generation and other sectors. Predictive maintenance techniques utilize data-driven approaches to anticipate equipment failures before they occur, thereby enabling proactive maintenance interventions. Various industries, such as oil and gas, manufacturing and logistics, actively integrate predictive maintenance solutions to improve efficiency and reduce operational costs.

[0003] In industries such as oil and gas, unexpected equipment failures can cause disruptions that extend beyond economic losses, affecting supply chains and production schedules. Predictive maintenance solutions are increasingly being deployed to address such challenges by leveraging artificialDocket No. RP-2024.98.012 2 intelligence (AI) and machine learning (ML) techniques. The application of industrial Internet of Things (IIoT) sensors in predictive maintenance solutions enables real-time monitoring of equipment conditions, thereby improving maintenance strategies. Data collected from such sensors is utilized for analyzing historical failure patterns, predicting potential failures and optimizing maintenance schedules.

[0004] Traditional maintenance approaches, such as reactive and preventive maintenance, are associated with several limitations. Reactive maintenance involves repairing equipment only after failure has occurred, leading to unplanned downtime and high repair costs. Preventive maintenance, on the other hand, relies on predefined schedules for servicing equipment, which may result in unnecessary maintenance activities and resource wastage.

[0005] The manufacturing sector in Canada has been witnessing rapid adoption of predictive maintenance solutions to enhance operational efficiency and minimize equipment failures. IIoT-based predictive maintenance tools enable manufacturers to detect early signs of potential failures and optimize maintenance schedules accordingly. The manufacturing industry in Canada consists of a large number of establishments, contributing significantly to the economy. The oil and gas industry in Canada is also a key sector where predictive maintenance solutions are gaining traction. The deployment ofDocket No. RP-2024.98.012 3 predictive maintenance platforms in such industries is expected to reduce equipment downtime, enhance productivity and lower maintenance costs.

[0006] Supply chain operations are also benefiting from predictive maintenance solutions, which help mitigate challenges such as equipment downtime, lack of track-and-trace solutions and high maintenance costs. The use of predictive analytics in supply chain operations improves decisionmaking by providing accurate insights into potential disruptions. Third-party logistics (3PL) providers and other supply chain operators in Canada are integrating predictive maintenance solutions to improve overall operational efficiency. The Canada MRO distribution market has been expanding due to the increasing demand for predictive maintenance solutions across industries. The adoption of predictive maintenance solutions is expected to grow further in response to the rising need for cost-effective and efficient equipment management strategies.

[0007] Predictive maintenance solutions are also being widely adopted by original equipment manufacturers (OEMs) to improve equipment uptime, thereby optimal performance.

[0008] Despite the advantages of predictive maintenance, several challenges persist in implementing such techniques effectively. Data collection and analysis present challenges due to the complexity of integrating data from different sources. Many industrial machines generate log data, including error codes and warnings, but integrating such data with sensor-based informationDocket No. RP-2024.98.012 4 remains a challenge. Additionally, the effectiveness of predictive maintenance models depends on the quality and completeness of the data being utilized. Inaccurate or incomplete data may result in flawed predictions, reducing the efficiency of predictive maintenance techniques. Furthermore, the implementation of deep learning models in predictive maintenance is constrained by high computational requirements and complexity.

[0009] Supervisory Control and Data Acquisition (SCADA) systems are widely used for data collection and monitoring in industrial settings. However, challenges arise in utilizing SCADA systems effectively for predictive maintenance applications. Many SCADA systems require human intervention for configuration and operation, making real-time data interpretation difficult. Additionally, predicting sensor readings within specific time windows remains a challenge, limiting the effectiveness of SCADA-based predictive maintenance solutions.

[0010] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for predictive maintenance of industrial equipment. SUMMARY

[0011] An objective of the present disclosure is to provide a system for predictive maintenance of industrial equipment to enable early failure detection, reduce downtime, and optimize maintenance. The system forDocket No. RP-2024.98.012 5 predictive maintenance of industrial equipment comprises Internet of Things (IoT) sensors that collect and transmit operational data with unique identifiers to a cloud server. The cloud server stores, preprocesses, and analyzes data using machine learning to detect failure patterns and generate predictive alerts. An alert generation unit notifies users via a user interface on computing devices.

[0012] Further, each IoT sensor is associated with a unique identification code corresponding to specific industrial equipment for accurate data mapping. The cloud server automatically segregates data based on industrial equipment type, operational conditions, and failure history for targeted analysis. Further, the cloud server generates warnings for detected anomalies, dynamically adjusts data transmission frequency, and performs automatic root cause analysis.

[0013] The system for predictive maintenance of industrial equipment provides a real-time dashboard for monitoring equipment health and maintenance trends. Further, an edge computing device preprocesses data locally before transmission to the cloud server, reducing bandwidth usage and improving response time for failure detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating theDocket No. RP-2024.98.012 6 present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein.

[0015] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams.

[0016] FIG. 1 illustrates a system 100 for predictive maintenance of industrial equipment 102, in accordance with various implementations of the present disclosure;

[0017] FIG. 2 illustrates a sequential flow of the system 100 for predictive maintenance of industrial equipment 102, in accordance with various implementations of the present disclosure;

[0018] FIG. 3 illustrates a predictive maintenance system utilizing Industrial IoT sensors and a cloud-based analytics platform, in accordance with various implementations of the present disclosure; and

[0019] FIG. 4 illustrates a cyber-physical predictive maintenance framework, in accordance with various implementations of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS

[0020] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, thoseDocket No. RP-2024.98.012 7 skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.

[0021] FIG. 1 illustrates a system 100 for predictive maintenance of industrial equipment 102, in accordance with various implementations of the present disclosure. The system 100 for predictive maintenance comprises a plurality of Internet of Things (IoT) sensors 104 installed on an industrial equipment 102. Each IoT sensor 104 collects operational data from the industrial equipment 102 and transmits collected data to a cloud server 106. The IoT sensors 104 are placed at various locations on industrial equipment 102 to monitor different operational parameters for example temperature, pressure, vibration, humidity, voltage, current, and other parameters indicative of operational health. The IoT sensors 104 operate continuously or at predefined intervals based on operational requirements. The IoT sensors 104 utilize wired or wireless communication interfaces, for example Wi-Fi, Bluetooth, Zigbee, LoRa, or cellular networks to transmit collected operational data to a cloud server 106. The IoT sensors 104 generate time-stamped data records to maintain a chronological history of operational performance. The IoT sensors 104 employ data encoding techniques to minimize transmission errors and assure integrity of transmitted data. The IoT sensors 104 are powered using external power sources, batteries, or energy harvesting techniques. The IoT sensors 104 periodically undergo self-diagnostic tests to detect sensor faults or communication failures. The IoT sensors 104 transmitDocket No. RP-2024.98.012 8 diagnostic data along with operational data to the cloud server 106 for further analysis. The IoT sensors 104 are enclosed in protective casings to withstand harsh industrial environments. The IoT sensors 104 communicate bidirectionally with the cloud server 106, enabling configuration updates and parameter adjustments.

[0022] In an embodiment, the cloud server 106 is in communication with The IoT sensors 104 and receives operational data transmitted from The IoT sensors 104. The cloud server 106 comprises storage resources to store received operational data for subsequent analysis. The cloud server 106 maintains a structured database to organize operational data based on industrial equipment identifiers, timestamps, and parameter types. The cloud server 106 receives operational data in real-time or at scheduled intervals, depending on communication protocols established with the IoT sensors 104. The cloud server 106 performs initial verification of received data to detect missing values or corrupted entries. The cloud server 106 logs received data along with metadata, for example reception time, source identification, and transmission quality metrics. The cloud server 106 assigns unique reference identifiers to each received data entry for indexing and retrieval. The cloud server 106 utilizes network security mechanisms, for example encryption and authentication to prevent unauthorized access to received operational data. The cloud server 106 executes validation routines to compare received data against predefined thresholds and operational limits.Docket No. RP-2024.98.012 9

[0023] In an embodiment, the cloud server 106 preprocesses received operational data by filtering out erroneous values due to measurement errors. The cloud server 106 applies statistical techniques to identify and remove outliers caused by sensor malfunctions or transient environmental conditions. The cloud server 106 utilizes threshold-based filtering, moving averages, and machine learning-based anomaly detection to eliminate data inconsistencies. The cloud server 106 reconstructs missing data points using interpolation techniques to maintain continuity in time-series data. The cloud server 106 stores both raw and preprocessed data in separate repositories for traceability and verification purposes. The cloud server 106 maintains logs of preprocessing actions performed on each data entry, comprising timestamps and applied filtering techniques. The cloud server 106 dynamically updates filtering criteria based on historical performance and sensor calibration records. The cloud server 106 prioritizes high-confidence data points for subsequent analysis and decision-making processes.

[0024] In an embodiment, the cloud server 106 performs feature selection to identify relevant parameters for analysis. The cloud server 106 evaluates operational data attributes to determine key parameters contributing to predictive maintenance analysis. The cloud server 106 ranks features based on statistical significance, correlation with failure events, and domain-specific knowledge. The cloud server 106 eliminates redundant or irrelevant features to optimize computational efficiency. The cloud server 106Docket No. RP-2024.98.012 10 applies dimensionality reduction techniques, for example principal component analysis to derive meaningful insights from high-dimensional datasets. The cloud server 106 maintains adaptive selection criteria that evolve based on continuous learning from operational data trends. The cloud server 106 integrates expert-defined rules to refine feature selection and improve analysis accuracy.

[0025] In an embodiment, the cloud server 106 analyzes identified parameters using machine learning techniques to identify patterns related to past failures. The cloud server 106 employs supervised and unsupervised learning models to detect failure trends and classify potential failure risks. The cloud server 106 extracts temporal patterns from time-series data to anticipate deviations from normal operating conditions. The cloud server 106 compares historical failure events with current operational data to assess risk levels. The cloud server 106 assigns probability scores to detected anomalies based on confidence levels derived from machine learning models. The cloud server 106 updates analytical models dynamically as additional operational data become available. The cloud server 106 logs analytical outcomes, comprising detected failure probabilities, contributing factors, and recommended maintenance actions.

[0026] In an embodiment, the cloud server 106 generates predictive alerts based on analyzed parameters to indicate potential failures. The cloud server 106 evaluates detected failure probabilities and assigns severity levelsDocket No. RP-2024.98.012 11 to predicted failures. The cloud server 106 generates structured alert messages containing details, for example affected industrial equipment identifiers, detected anomaly types, and recommended maintenance actions. The cloud server 106 timestamps generated alerts and categorizes alerts based on priority levels. The cloud server 106 transmits alerts to designated recipients via multiple communication channels, comprising email, SMS, or application notifications. The cloud server 106 maintains an alert history log to facilitate post-event analysis and system performance evaluation.

[0027] In an embodiment, an alert generation unit 108 automatically notifies users of predicted failures and recommends maintenance actions via user interface 110 accessible on a computing device 112. The Alert generation unit 108 formats alert messages to comprise graphical representations, numerical summaries, and diagnostic insights. the Alert generation unit 108 categorizes alerts based on urgency and assigns response deadlines for maintenance personnel. The Alert generation unit 108 prioritizes alerts based on operational impact and equipment malfunctioning. The Alert generation unit 108 integrates with maintenance scheduling systems to suggest optimal maintenance windows. The Alert generation unit 108 allows users to acknowledge alerts and provide feedback regarding maintenance actions performed. The Alert generation unit 108 maintains a record of user interactions to improve system responsiveness and alert accuracy. The Alert generation unit 108 facilitates communication between different user levels byDocket No. RP-2024.98.012 12 directing alerts to appropriate personnel based on role-based access controls. The user interface 110 displays real-time operational data, historical performance trends, and recommended maintenance schedules. The computing devices 112 may comprise desktop computers, tablets, and mobile devices capable of accessing the user interface 110. The computing devices 112 receive and process notifications from the Alert generation unit 108 to provide remote access to predictive maintenance insights.

[0028] In an exemplary use case scenario, a manufacturing facility operates multiple industrial machines, comprising hydraulic presses, conveyor systems, and robotic arms. Each industrial machine is equipped with a plurality of The IoT sensors 104 to monitor operational parameters, for example temperature, vibration, pressure, and energy consumption. For instance, a hydraulic press is equipped with the IoT sensors 104 which measure hydraulic fluid temperature, piston speed, and applied pressure. The IoT sensors 104 continuously collect such operational data and transmit recorded values to the cloud server 106 along with a unique identifier corresponding to industrial equipment 102. Assume that the hydraulic press is operating under normal conditions with a hydraulic fluid temperature of 60°C, a piston speed of 200 mm / s, and an applied pressure of 150 bar. The IoT sensors 104 continuously collect such operational data and transmit recorded values to the cloud server 106. At a certain point, the hydraulic fluid temperature rises to 90°C while the piston speed reduces to 120 mm / s. TheDocket No. RP-2024.98.012 13 IoT sensors 104 detect such deviations and transmit updated values to the cloud server 106. The cloud server 106 receives such operational data and stores recorded values in an organized database for further analysis. The cloud server 106 preprocesses received operational data by filtering out erroneous values which may have resulted from temporary sensor malfunctions. For instance, if an IoT sensor 104 erroneously records a hydraulic fluid temperature of 300°C due to a transient glitch, The cloud server 106 identifies such an error and eliminates the incorrect data point. The cloud server 106 subsequently performs feature selection by identifying key operational parameters for example hydraulic fluid temperature, piston speed, and applied pressure which contribute to failure predictions. Based on historical data, the cloud server 106 analyzes preprocessed data using machine learning techniques and identifies a pattern indicating that hydraulic fluid overheating has been a recurring factor in past system failures. The cloud server 106 subsequently generates a predictive alert indicating that the hydraulic press is at risk of overheating, which may lead to seal failure and reduced efficiency. The Alert generation unit 108 automatically notifies maintenance personnel through the user interface 110 accessible on the computing devices 112. The alert message comprises recorded data showing that the hydraulic fluid temperature has increased from 60°C to 90°C, piston speed has decreased from 200 mm / s to 120 mm / s, and applied pressure has fluctuated beyond normal operating thresholds. The alert messageDocket No. RP-2024.98.012 14 recommends an immediate inspection of the hydraulic fluid cooling system and suggests preventive maintenance to replace worn-out seals and inspect fluid circulation.

[0029] In an embodiment, each IoT sensor 104 may associate with a unique identification code (same as unique identifier) corresponding to industrial equipment 102 in which IoT sensor 104 is installed. Each unique identification code enables accurate mapping of collected operational data to specific industrial equipment 102. Such an association allows the cloud server 106 to organize and analyze operational data efficiently. For example, in a manufacturing facility, each hydraulic press has a unique identification code, and the IoT sensor 104 transmits recorded values with said code. The cloud server 106 prevents data from overlapping. The IoT sensor 104 embeds or dynamically assigns codes and transmits via communication protocols. Multiple IoT sensors 104 on industrial equipment 102 have distinct identifiers for accurate data mapping and predictive maintenance.

[0030] In an embodiment, the cloud server 106 may automatically segregate received operational data based on industrial equipment type, operational conditions, and failure history. The cloud server 106 organizes stored data by categorizing recorded values according to preconfigured classification rules. For example, in an industrial facility with multiple types of equipment, for example hydraulic presses, conveyor belts, and robotic arms, the cloud server 106 categorizes operational data separately for eachDocket No. RP-2024.98.012 15 industrial equipment type. Such categorization enables targeted analysis and prevents unrelated data. The cloud server 106 further segregates recorded values based on operational conditions, for example high-load operation, idle states, and temperature variations.

[0031] In an embodiment, the cloud server 106 may automatically generate warnings based on detected anomalies or deviations from expected operational parameters. The cloud server 106 establishes predefined operational thresholds for various industrial equipment types and compares received recorded values against expected ranges. If an operational parameter exceeds acceptable limits, the cloud server 106 generates a warning notification. For example, if a hydraulic press typically operates with a pressure range of 100 to 150 bar and an IoT sensor 104 transmits a recorded value of 200 bar, the cloud server 106 detects an anomaly and triggers a warning. The cloud server 106 classifies warnings based on severity levels and assigns priority tags to generated warnings. For instance, minor fluctuations in vibration levels may trigger a low-priority warning, while excessive temperature rise in a motor casing may trigger a high-priority warning. The cloud server 106 automatically sends warning notifications to alert generation unit 108, which then displays warning messages on the user interface 110. The cloud server 106 maintains a historical log of generated warnings for future reference.Docket No. RP-2024.98.012 16

[0032] In an embodiment, the cloud server 106 may dynamically adjust data transmission frequency based on detected anomalies or deviations in sensor readings. The cloud server 106 monitors received operational data from the IoT sensors 104 and evaluates recorded values against predefined baseline conditions. If recorded values remain within normal operating thresholds, the cloud server 106 maintains standard transmission intervals. However, if an anomaly or deviation is detected, the cloud server 106 increases data transmission frequency to capture additional data points for real-time analysis. For example, if a motor in an industrial conveyor system operates at a steady speed of 1200 RPM, the cloud server 106 receives periodic data updates every 10 minutes. However, if IoT sensor 104 records a sudden drop in motor speed to 800 RPM, the cloud server 106 dynamically reduces the transmission interval to every 30 seconds to track fluctuations more accurately. The cloud server 106 continues high-frequency monitoring until recorded values stabilize or maintenance intervention is performed. The cloud server 106 also decreases data transmission frequency during stable operational periods to conserve network bandwidth and processing resources.

[0033] In an embodiment, the cloud server 106 may perform automatic root cause analysis upon detecting an anomaly by identifying correlations between current operational data and historical failure events. The cloud server 106 compares newly received sensor readings with stored failure records and determines probable causes of detected deviations. The cloudDocket No. RP-2024.98.012 17 server 106 utilizes statistical models to establish relationships between failure patterns and contributing operational factors. For example, if the IoT sensors 104 detect increasing motor temperature along with fluctuating voltage levels, the cloud server 106 correlates such data with past failure events where overheating led to motor burnout. The cloud server 106 generates a diagnostic report detailing possible causes of detected anomalies and recommends corrective actions. The cloud server 106 refines root cause analysis by continuously learning from maintenance records and repair logs. If a particular industrial machine consistently exhibits vibration anomalies before mechanical failure, the cloud server 106 assigns high correlation scores to vibration deviations detected in future operations. The cloud server 106 performs multi-factor analysis by considering simultaneous deviations across multiple sensors.

[0034] In an embodiment, the system 100 for predictive maintenance of industrial equipment may provide a real-time dashboard with visual analytics for monitoring equipment health and operational efficiency, wherein the dashboard is accessible via computing device 112. The dashboard presents live sensor data, performance trends, and predictive maintenance alerts in an organized format. The cloud server 106 updates dashboard contents dynamically, displaying newly received operational data in real-time. The dashboard enables maintenance personnel to view historical trends, set operational thresholds, and analyze failure probabilities. The dashboardDocket No. RP-2024.98.012 18 categorizes display information based on industrial equipment types and sensor locations. For example, in a manufacturing plant with multiple conveyor systems, the dashboard provides separate visual sections for each conveyor, displaying key parameters for example belt tension, motor speed, and load weight. The dashboard highlights deviations and anomalies using color-coded alerts. If a hydraulic press exhibits abnormal pressure fluctuations, the dashboard highlights affected operational parameters with warning indicators. The dashboard supports interactive filtering, allowing users to view specific data ranges and equipment-specific performance metrics. The dashboard also integrates maintenance schedules and recommended actions based on predictive failure analysis.

[0035] In an embodiment, the system 100 for predictive maintenance of industrial equipment may comprise an edge computing device which preprocesses data locally at IoT sensor 104 level before transmitting processed data to the cloud server 106, reducing bandwidth usage and improving response time for failure detection. The edge computing device performs initial data filtering, anomaly detection, and feature extraction before forwarding relevant data to the cloud server 106. Such preprocessing reduces data transmission load by eliminating redundant or low-importance data. For example, if IoT sensor 104 records stable temperature readings within expected ranges, the edge computing device refrains from transmitting redundant data to the cloud server 106. However, if an abnormal temperatureDocket No. RP-2024.98.012 19 spike is detected, the edge computing device immediately transmits recorded values to the cloud server 106 for further analysis. The edge computing device also performs localized anomaly detection using predefined threshold comparisons.

[0036] FIG. 2 illustrates a sequential flow of the system 100 for predictive maintenance of industrial equipment 102, in accordance with various implementations of the present disclosure. Industrial equipment 102 interacts with the IoT sensors 104, which continuously collect operational data for example temperature, vibration, and pressure. The IoT sensors 104 transmit recorded data along with unique identifiers to the cloud server 106, assuring accurate mapping of sensor readings to specific industrial equipment. The cloud server 106 receives transmitted data and stores recorded values in an organized database for further analysis. The cloud server 106 preprocesses collected data by filtering out erroneous values resulting from sensor noise or transmission errors. The cloud server 106 performs feature selection to identify relevant operational parameters contributing to predictive failure analysis. The cloud server 106 applies machine learning techniques to analyze preprocessed data, identifying patterns related to past failures and deviations from normal operational thresholds. Based on detected anomalies, the cloud server 106 generates predictive alerts indicating potential failures before malfunctions occur. Alert system 108 processes generate alerts and sends notifications to the user interface 110, which is accessible on the computingDocket No. RP-2024.98.012 20 devices 112. The user interface 110 displays alert messages along with recommended maintenance actions, enabling maintenance personnel to take preventive measures. The computing devices 112 receive predictive alerts and provide real-time access to diagnostic insights, allowing operators to assess health of industrial equipment remotely. The figure visually represents data flow from industrial equipment 102 through the IoT sensors 104 to the cloud server 106, demonstrating key processing steps leading to predictive maintenance insights. Sequential interactions between components assure automated failure detection, real-time notifications, and proactive maintenance scheduling.

[0037] FIG. 3 illustrates a predictive maintenance system utilizing Industrial IoT sensors and a cloud-based analytics platform, in accordance with various implementations of the present disclosure. Industrial equipment transmits sensor data to an IoT device, which forwards collected data via a cellular network to an IIoT platform. The IIoT platform processes operational data and generates analytics, which are then displayed on a user interface accessible through computing devices for example desktops, tablets, and smartphones. Such architecture enables real-time monitoring of industrial equipment, allowing operators to track important parameters, for example temperature, vibration, and pressure. By continuously analyzing operational trends, the system detects anomalies and predicts potential failures before they occur. The integration of IIoT technique with cloud-based analyticsDocket No. RP-2024.98.012 21 improves predictive maintenance by facilitating data-driven decision-making, reducing unplanned downtime, and optimizing maintenance schedules. The connectivity between industrial equipment, cloud infrastructure, and user interfaces assures efficient remote monitoring and intervention.

[0038] FIG. 4 illustrates a cyber-physical predictive maintenance framework, in accordance with various implementations of the present disclosure. The cyber-physical predictive maintenance framework incorporates data collection, cloud processing, data analysis, and decision support systems. The physical layer consists of industrial equipment equipped with IoT sensors which collect real-time operational data. Such data is transmitted to the cloud, where it is processed using data mining, machine learning, and maintenance algorithms. The cyber layer performs data analysis to identify trends, anomalies, and failure patterns. A decision support system interprets analyzed data, providing recommendations for maintenance scheduling and failure prevention. The processed insights are then visualized through applications which allow maintenance personnel to monitor equipment performance and operational efficiency. The integration of cyber and physical components enables continuous monitoring, predictive failure detection, and data-driven maintenance planning. By cloud computing, machine learning, and real-time data processing, the framework improves reliability, extends equipment lifespan, and reduces operational costs in industrial settings.Docket No. RP-2024.98.012 22

[0039] In an embodiment, the system 100 for predictive maintenance of industrial equipment 102 predicts potential failures using a model-based approach with higher accuracy than data-driven methods. The cloud server 106 automatically segregates operational data and generates warnings, utilizing a machine learning model instead of deep learning. Real-time alerts are sent by the Alert generation unit 108 to the user interface 110 on the computing devices 112 when operational trends deviate, eliminating the need for human intervention. Industrial IoT sensors 104 enable collection, communication, and storage of operational data, forming the foundation of predictive maintenance through smart sensor networks. The cloud server 106 processes data using machine learning and Big Data to improve efficiency and reliability. Artificial intelligence and machine learning automate processes, eliminate repetitive tasks, assure unbiased decision-making, and reduce timeto-market. Big Data enables the cloud server 106 to transition manufacturing operations from preventive to predictive maintenance by optimizing equipment usage and improving decision-making. The IoT sensors 104 collect operational data and transmit recorded values to the cloud server 106, where collected data is preprocessed and stored in the cyber layer before reports are sent to the physical layer. A decision support system plans future maintenance and suggests routes based on analyzed trends. The plant is reviewed to identify performance issues, track operational processes, and install necessary IoT sensors 104. Feature selection improves accuracy and efficiency byDocket No. RP-2024.98.012 23 removing erroneous values during preprocessing, playing an important role in analyzing high-dimensional Big Data within the cloud server 106, assuring reliable predictive maintenance operations.

Claims

CLAIMS What is claimed is:

1. A system for predictive maintenance of an industrial equipment, comprising: a plurality of Internet of Things (IoT) sensors installed on the industrial equipment, wherein each IoT sensor is configured to collect operational data; a cloud server in communication with the IoT sensors, wherein the cloud server is configured to: receive and store the operational data transmitted by the IoT sensors; preprocess the received data by filtering out the erroneous values; perform feature selection on the preprocessed data to the identify parameters for analysis; analyze the identified parameters using the machine learning techniques to identify the patterns related to the past failures; generate the predictive alerts based on the identified patterns to indicate the potential failures; and an alert generation module configured to automatically notify the users of the indicated potential failures and recommend theDocket No. RP-2024.98.012 25 maintenance actions via a user interface accessible on a computing device.

2. The system of claim 1, wherein each IoT sensor is associated with a unique identification code corresponding to a specific industrial equipment in which the IoT sensor is installed.

3. The system of claim 1, wherein the cloud server automatically segregates data based on an industrial equipment type, an operational condition, and a failure history for targeted analysis.

4. The system of claim 1, wherein the cloud server is configured to automatically generate a warning based on the detected anomalies or the deviations from the expected operational parameters.

5. The system of claim 1, wherein the cloud server adjusts frequency of data transmission based on the detected anomalies or the deviations in sensor readings.

6. The system of claim 1, wherein the cloud server is configured to perform automatic root cause analysis upon detecting an anomaly, by identifying the correlations between collected operational data and the historical failure events.

7. The system of claim 1, further comprises a real-time dashboard with visual analytics for monitoring equipment health and operational efficiency, wherein the real-time dashboard is accessible via the computing device.Docket No. RP-2024.98.012 26 8. The system of claim 1, further comprises an edge computing device configured to preprocess data locally at a level of the IoT sensor before transmitting the processed data to the cloud server.