Device predictive maintenance and overhaul management integration system

Abstract

An integrated system for predictive maintenance and overhaul management aims to improve the maintenance and operational efficiency of industrial machinery. This system integrates predictive maintenance, major overhaul management, and compliance with regulations for specialized industrial equipment into a single, coordinated system. Utilizing real-time data collection from IoT sensors to monitor parameters such as vibration, pressure, and temperature, the system performs trend degradation analysis to identify potential industrial equipment problems before failures occur. Maintenance warnings and work orders are automatically generated based on predetermined thresholds, enabling timely preventative action. The major overhaul management module schedules comprehensive maintenance activities during planned production downtime and dynamically adjusts based on real-time industrial equipment conditions to optimize resource allocation and minimize production disruptions. Furthermore, the system ensures regulatory compliance for specialized industrial equipment by managing inspection scheduling and providing notifications and warnings for upcoming and overdue inspections.

Description

Technical Field

[0001] This invention relates to an industrial equipment maintenance management system, and more particularly to an integrated system for facilitating comprehensive lifecycle management of industrial equipment. More specifically, this invention relates to a unified solution combining a predictive maintenance module and a major overhaul management module to ensure the integrity of industrial equipment, improve operational efficiency, and minimize downtime in industrial environments. Background Technology

[0002] In industrial operations, the maintenance of industrial equipment is crucial for ensuring continuous production, safety, and cost-effectiveness. Traditional maintenance strategies primarily include reactive maintenance, which involves repairing equipment after it fails, and preventative maintenance, which involves regular inspections and servicing to prevent unexpected breakdowns. While these approaches address certain aspects of industrial equipment reliability, they are often insufficient in optimizing maintenance processes and extending mechanical life.

[0003] With advancements in sensor technology and data analytics, predictive maintenance practices have emerged, utilizing real-time data collection and analysis to predict potential industrial equipment failures. Predictive maintenance uses parameters such as vibration, pressure, temperature, and oil quality to assess the operating condition of machinery. Despite its numerous advantages, predictive maintenance systems often operate in isolation, focusing solely on anomaly detection and condition monitoring without integration with broader maintenance planning and resource management frameworks. This lack of integration can lead to fragmented maintenance efforts, inefficient resource allocation, and extended downtime for industrial equipment.

[0004] Major overhauls or planned comprehensive maintenance activities (i.e., major repairs) are also important for managing significant wear and tear, ensuring regulatory compliance, and extending the service life of critical industrial equipment. However, managing major overhauls involves complex planning and coordination, often requiring cross-departmental collaboration and meticulous resource management. Existing major overhaul management systems may fail to effectively integrate real-time data insights from predictive maintenance, leading to suboptimal scheduling, increased maintenance costs, and potential disruptions to production schedules.

[0005] Integrating predictive maintenance with major overhaul management presents significant challenges. Existing solutions often lack a tight data flow between predictive analytics and maintenance planning modules, leading to inefficient and less effective maintenance strategies. Furthermore, specialized industrial equipment such as boilers, pressure vessels, and elevators require stringent regulatory compliance, further complicating maintenance management processes. Current systems may not adequately meet the unique compliance requirements and inspection scheduling needs of such industrial equipment, increasing the risk of regulatory violations and safety incidents.

[0006] Therefore, there is an urgent need for an advanced maintenance management system that unifies predictive maintenance and major overhaul processes into a single, coordinated system. Such an integrated system will enable real-time data-driven decision-making, streamlined maintenance scheduling, efficient resource allocation, and comprehensive compliance management. By bridging the gap between predictive analytics and major overhaul planning, integrated solutions can significantly reduce industrial equipment downtime, lower maintenance costs, enhance safety, and improve overall operational efficiency in industrial environments.

[0007] In summary, while existing maintenance management systems offer valuable capabilities for predictive maintenance and major overhaul management, they often operate independently and lack sufficient integration. This fragmentation leads to inefficient maintenance, increased costs, and heightened risks in industrial equipment management. This invention provides a comprehensive solution for industrial equipment lifecycle management in industrial environments by offering a unified platform that tightly integrates predictive maintenance and major overhaul processes, thus overcoming the limitations of existing systems. Summary of the Invention

[0008] This invention provides an integrated system for predictive maintenance and overhaul management of equipment, designed to improve the maintenance and operational efficiency of industrial machinery. This unified platform tightly integrates predictive maintenance, major overhaul management, and compliance with special industrial equipment into a single, coordinated solution, thereby ensuring the integrity of industrial equipment and minimizing downtime.

[0009] At the heart of this invention is a unified system architecture that integrates multiple maintenance modules—predictive maintenance, major overhaul management, and specialized industrial equipment management—into a centralized system for predictive maintenance and overhaul management. This integration facilitates tight data flow and coordination between different maintenance activities, enabling comprehensive lifecycle management of industrial equipment. The predictive maintenance module utilizes real-time data collection from sensors in the Internet of Things (IoT) to measure parameters such as vibration, pressure, and temperature, and performs trend degradation analysis. By identifying potential industrial equipment problems before they occur, the system can automatically generate maintenance warnings and work orders based on predetermined thresholds and detected anomalies, allowing for timely preventative measures.

[0010] The Major Overhaul Management module effectively schedules and coordinates a wide range of maintenance activities, including annual overhauls, and conducts them during planned production downtime. Through integration with the Predictive Maintenance module, this module dynamically adjusts maintenance schedules based on real-time industrial equipment status data, ensuring optimal resource allocation and minimizing disruption to production processes. Furthermore, the Specialty Industrial Equipment Group specifically manages specialized industrial equipment such as boilers, pressure vessels, and elevators. This group ensures compliance with regulatory standards across various regions by managing inspection schedules, providing notifications and warnings of upcoming and overdue inspections, and generating compliance reports, thereby maintaining safety and regulatory compliance.

[0011] Through standardized APIs or manual data entry, tight integration with external Enterprise Resource Planning (ERP) systems enables the synchronization of maintenance scheduling and work orders with ERP functions such as inventory management and resource allocation. This integration improves overall operational efficiency and ensures the accuracy of maintenance records. The equipment predictive maintenance and overhaul management integrated system supports automatic and manual data integration from multiple sources, including IoT industrial equipment and external suppliers, facilitating comprehensive data management and real-time decision-making.

[0012] User experience is prioritized through an intuitive user interface featuring data visualization dashboards that provide maintenance personnel and managers with real-time insights into the status of industrial equipment and maintenance activities. The highly customizable predictive maintenance and overhaul management system allows users to define unique maintenance schedules, inspection criteria, and data inputs based on different industries and industrial equipment types. Furthermore, the system includes a feedback mechanism that records completed maintenance and repair activities back into the system. This feedback mechanism enables continuous improvement of predictive maintenance algorithms and maintenance plans, enhancing industrial equipment reliability and extending mechanical life over time.

[0013] The predictive maintenance and overhaul management integrated system of this invention offers several advantages, including improved operational efficiency through optimized maintenance scheduling and resource utilization, enhanced industrial equipment reliability through early detection of potential problems, and ensured regulatory compliance for critical industrial equipment. Furthermore, this integrated system is designed for scalability and flexibility, enabling it to meet the needs of large industrial operations as well as small and medium-sized enterprises through customizable modules and expanded data integration and automation levels. The integration of various maintenance modules facilitates comprehensive lifecycle management, supporting continuous improvement and information-based decision-making, providing a powerful and versatile solution that significantly improves the reliability and lifespan of industrial equipment.

[0014] In summary, the equipment predictive maintenance and overhaul management integrated system of this invention addresses the limitations of existing maintenance solutions by providing a unified platform that combines predictive maintenance, major overhaul management, and compliance with specific industrial equipment requirements. This comprehensive approach ensures efficient maintenance processes, regulatory compliance, and continuous improvement, thereby significantly enhancing the reliability and lifespan of industrial equipment.

[0015] To make the above features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] Various embodiments will now be described with reference to the accompanying drawings, which are illustrative and not intended to limit the scope in any way, wherein similar reference numerals denote similar components, and the figures are simply explained below:

[0017] Figure 1 The diagram illustrates the architecture of the integrated system for predictive maintenance and overhaul management of equipment according to the present invention.

[0018] Figure 2 The diagram shown is an information flow diagram of the integrated system for predictive maintenance and overhaul management of equipment.

[0019] Figure 3 The diagram shown is an architecture diagram of one embodiment of the predictive maintenance module of the present invention.

[0020] Figure 4 The diagram shown is an architecture diagram of one embodiment of the major maintenance management module of the present invention. Detailed Implementation

[0021] This invention relates to an integrated predictive maintenance and overhaul management system for industrial machinery, designed to improve maintenance, operational efficiency, and reliability. This system integrates predictive maintenance, major overhaul management, and compliance for specialized industrial equipment into a unified system or platform. By leveraging real-time data collection, advanced analytics, tight integration with Enterprise Resource Planning (ERP) systems, and a user-friendly interface, this system overcomes the limitations of existing maintenance solutions, providing a comprehensive approach to industrial equipment lifecycle management.

[0022] Please refer to Figure 1 , Figure 1The diagram illustrates the architecture of the integrated system for predictive maintenance and overhaul management of equipment according to this invention. In this embodiment, the integrated system 100 includes multiple interconnected modules, each performing a specific function within the overall system. Specifically, the data acquisition module 110 is responsible for collecting operational data from various industrial equipment 10 or specialized industrial equipment 11. This data acquisition module 110 includes multiple sensors 105 installed in the various industrial equipment 10 to achieve automated data collection. The data acquisition module 110 supports various types of sensors and data sources, including vibration sensors that measure industrial equipment vibration to detect imbalances or mechanical wear, pressure sensors that monitor pressure levels in hydraulic lines and pressure vessel systems, temperature sensors that track temperature changes in industrial equipment components such as motors, bearings, and transformers, and oil sensors that assess the condition of lubricants and transformer oil to detect contamination or deterioration. The data acquisition module 110 uses standardized communication protocols such as MQTT, HTTP, and Modbus to ensure compatibility with various sensors 20. In addition, in one embodiment, the data acquisition module 110 also supports manual data input. The data acquisition module 110 provides an input interface 112 to allow operators to input data after inspecting the industrial equipment 10 or data collected from third-party reports via a client device 30 (e.g., a smartphone or desktop computer).

[0023] In addition, the predictive maintenance module 120 analyzes operational data to perform trend degradation analysis and detect industrial equipment anomalies. This predictive maintenance module 120 uses a data analysis engine 122 to process data from the data acquisition module 110 using statistical methods and machine learning algorithms (collectively referred to in this embodiment as: predictive maintenance algorithms) to identify patterns of degradation or impending failure in the industrial equipment 10. Users can set predetermined thresholds and limits for various parameters of the industrial equipment; when these thresholds and limits are exceeded, the predictive maintenance module 120 triggers a warning. The anomaly detection capability of the predictive maintenance module 120 utilizes machine learning algorithms to consider real-time data and historical trends to detect deviations from normal operating conditions. Once an anomaly is detected, the predictive maintenance module 120 automatically generates maintenance warnings and work orders, prioritizing actions based on severity and potential impact on operations. Furthermore, the predictive maintenance module 120 supports continuous learning, updating its predictive model based on new data and feedback from completed maintenance activities, thereby improving the accuracy of trend degradation analysis over time.

[0024] Furthermore, the Major Overhaul Management Module 130 facilitates the planning, scheduling, and execution of a wide range of maintenance activities, especially those requiring downtime (e.g., major overhauls). This module allows for the creation of detailed maintenance plans, incorporating data from the Predictive Maintenance Module 120 to prioritize industrial equipment requiring attention. The Major Overhaul Management Module 130 manages the allocation of resources such as personnel, tools, and spare parts for maintenance activities and integrates with the external ERP system 20 to ensure resource availability. It can coordinate maintenance tasks across multiple departments, ensuring efficient execution with minimal disruption. Moreover, the Major Overhaul Management Module 130 can dynamically adjust maintenance plans based on real-time status data of the industrial equipment 10 and unforeseen operational changes, thereby optimizing resource allocation and minimizing disruption to production processes.

[0025] Specialty Industrial Equipment Compliance Group 140 ensures that specialized industrial equipment 11 complies with regulatory standards and safety requirements. This Specialty Industrial Equipment Compliance Group 140 manages the mandatory inspection schedule for specialized industrial equipment 11, such as boilers, pressure vessels, and elevators, and maintains records of inspection, certification, and compliance status. In one embodiment, it generates reports to be submitted to regulatory agencies as needed and provides warnings of upcoming inspections, renewals, and any compliance-related deadlines to prevent overdue payments. The Specialty Industrial Equipment Compliance Group 140 can be configured to adapt to the specific regulatory requirements of different regions or countries, allowing for localization as required by law. By managing inspection schedules and compliance tracking, this module helps maintain safety standards and regulatory compliance across various regions.

[0026] The ERP integration module 150 facilitates close communication between the predictive maintenance and overhaul management integration system 100 and the external ERP system 20. It ensures that maintenance schedules, work orders, inventory levels, and resource allocations are continuously updated between the equipment predictive maintenance and overhaul management integration system 100 and the ERP system 20 via a standardized application programming interface (API). This bidirectional data exchange supports multiple ERP systems, enabling comprehensive synchronization of personnel needs for asset management, procurement, and maintenance tasks. Integration with the ERP system 20 improves inventory management by tracking spare parts usage, reorder levels, and procurement needs. Furthermore, in one embodiment, the equipment predictive maintenance and overhaul management integration system 100 also includes a financial tracking module 160 to allow monitoring of maintenance costs, budgets, and financial reporting when integrated with the ERP system 20, thereby improving overall operational efficiency and ensuring the accuracy of maintenance records.

[0027] User interface module 170 provides users with an interactive interface to access the integrated system 100 for predictive maintenance and overhaul management. It features a customizable dashboard 172 that displays real-time industrial equipment status, maintenance schedules, and key performance indicators (KPIs). Data visualization through charts, graphs, and trend lines provides an intuitive view of the data and aids in rapid decision-making. User interface module 170 implements role-based access control, tailoring interfaces and access permissions for different user roles such as maintenance personnel, managers, and compliance officers. Furthermore, user interface module 170 allows for extensive customization, enabling users to define maintenance schedules, inspection criteria, and data input parameters according to their operational needs. This user-friendly user interface module 170 ensures that maintenance personnel and managers can easily navigate the system, access the necessary information, and perform their tasks efficiently.

[0028] The feedback mechanism is a core component of the continuous improvement process of the integrated predictive maintenance and overhaul management system 100. This mechanism involves recording completed maintenance and repair activities in a database 180, including the work performed, the parts replaced, and the time spent. These maintenance results are then fed back to the predictive maintenance module 120 to improve the predictive maintenance algorithm, enhancing the system's ability to accurately predict potential industrial equipment problems. The feedback mechanism also enables performance analysis by tracking indicators such as mean time between failures (MTBF), maintenance costs, and industrial equipment uptime. These analyses provide insights into maintenance effectiveness, industrial equipment reliability, and overall system performance, facilitating continuous improvement of data-driven decision-making and maintenance strategies.

[0029] In this embodiment, the feedback mechanism is primarily implemented through the major overhaul management module 130 and the user interface module 170. These two modules work together to provide comprehensive feedback to the predictive maintenance module 120, enabling it to refine its predictive algorithm and improve prediction accuracy. Once a maintenance task is completed—whether routine, corrective, or part of a major overhaul—maintenance personnel use the user interface module 170 to record detailed information about the work performed. This information includes the specific nature of the repair, the parts replaced, the time spent on the task, the observed equipment condition, and any anomalies encountered during the maintenance process. The user interface module 170 provides an intuitive platform for inputting this data, ensuring that the data is recorded accurately and efficiently.

[0030] The Major Overhaul Management Module 130 plays a crucial role in recording the results of maintenance activities and updating the equipment status in the system. It maintains a comprehensive record of all maintenance actions, including deviations from planned schedules, resource utilization, and any problems encountered during execution. This Major Overhaul Management Module 130 ensures that the maintenance history of each piece of equipment is meticulously documented.

[0031] This feedback is provided to the predictive maintenance module 120. By analyzing the actual results of maintenance activities, the predictive maintenance module 120 can adjust its predictive models and algorithms. It learns from the discrepancy between predicted equipment behavior and actual performance, allowing it to recalibrate thresholds, update machine learning models, and improve anomaly detection capabilities. This iterative process enhances the module's ability to accurately predict potential equipment failures and recommend more effective maintenance interventions.

[0032] Furthermore, ensuring data protection and integrity is also crucial in this embodiment, and the security module 190 addresses these issues with robust measures. The security module 190 connects to the user interface module 170, managing user authentication, authorization, and permissions to protect sensitive information, implement role-based access control, and restrict data access according to user roles. A data encryption protocol 192 is used to protect data at rest and in transit, preventing unauthorized access and ensuring compliance with GDPR and regionally equivalent data privacy regulations. In addition, the security module 190 maintains an audit trail, logs user activity and system events, provides accountability, and facilitates compliance audits. These security measures ensure the system is resistant to potential threats and unauthorized access, protecting the integrity and confidentiality of operational data.

[0033] Please refer to Figure 2 , Figure 2 The diagram illustrates the information flow within the integrated predictive maintenance and overhaul management system. Arrows in the diagram represent the direction of information flow. The operation of the integrated predictive maintenance and overhaul management system 100 involves multiple interconnected processes that work together to ensure efficient maintenance and operational reliability. Real-time monitoring is achieved by continuously collecting data from sensors 105 attached to the industrial equipment 10. The data acquisition module 110 transmits this information to the predictive maintenance module 120. The predictive maintenance module 120 processes the incoming data, performing trend degradation analysis to detect anomalies and potential industrial equipment problems. When industrial equipment parameters exceed predetermined thresholds, the predictive maintenance module 120 generates warnings and automatically creates work orders, prioritizing maintenance actions based on urgency and potential impact.

[0034] Maintenance planning and execution are managed by integrating work orders and maintenance tasks into the schedule of the major overhaul management module 130. The ERP integration module 150 ensures that necessary resources are allocated and that inventory levels are sufficient to support planned maintenance activities. Maintenance personnel execute tasks according to the schedule, update task status, and report any additional issues through the user interface module 170. The special industrial equipment scalability group 140 manages the inspection schedule for specialized industrial equipment 11, ensuring all compliance activities are updated and generating reports for submission to regulatory agencies when necessary.

[0035] The feedback mechanism in the predictive maintenance and overhaul management integrated system 100 records details of completed maintenance tasks in the database 180 and feeds this information back to the predictive maintenance module 120 to refine the predictive maintenance algorithm and improve future maintenance plans. This continuous improvement process enhances the reliability of industrial equipment and extends its mechanical life over time. Performance indicators tracked by the predictive maintenance module 120, such as MTBF, maintenance costs, and industrial equipment uptime, provide valuable insights into maintenance effectiveness and system performance, facilitating data-driven decision-making and optimization of maintenance strategies.

[0036] The predictive maintenance module will be described in more detail below. Please also refer to... Figure 3 , Figure 3 The diagram illustrates an architecture of one embodiment of the predictive maintenance module of the present invention. As previously described, the predictive maintenance module 120 is designed to predict and mitigate potential equipment failures. This predictive maintenance module 120 utilizes advanced data analytics and machine learning algorithms to analyze real-time and historical operating data collected from various sensors 105 attached to the industrial equipment 10. The data acquisition process involves continuous monitoring of key parameters such as vibration levels, temperature fluctuations, pressure changes, rotational speed, and oil quality indicators. These parameters are important indicators of equipment health and performance.

[0037] After collection, the preprocessing component 121 of the predictive maintenance module 120 preprocesses the operational data to ensure its accuracy and reliability. The preprocessing performed by the preprocessing component 121 includes data cleaning, which uses statistical methods to remove noise and outliers to prevent bias in the analysis results. Subsequently, standardization is performed to scale the data to a standard range, facilitating meaningful comparisons between different sensor and device types. Furthermore, the preprocessing component 121 performs time-series alignment to ensure synchronization of data from different sources, allowing for accurate correlation analysis.

[0038] The predictive maintenance module 120's data analysis engine 122 combines predictive maintenance algorithms such as statistical models and machine learning algorithms to perform trend degradation analysis and anomaly detection. Statistical analysis techniques, such as descriptive statistics and trend analysis, are used to establish baseline operating parameters and identify long-term changes in equipment behavior. Control charts, as a fundamental tool of statistical process control (SPC), are used to monitor process stability and detect variations exceeding control limits.

[0039] Machine learning algorithms play a crucial role in improving the predictive capabilities of modules. Linear and nonlinear regression models are used to predict future device behavior based on historical data. Time series forecasting methods, including Autoregressive Integrated Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) networks, are implemented to capture time dependencies and accurately predict future parameter values. Anomaly detection algorithms, such as Isolation Forests and One-Class Support Vector Machines (SVMs), are deployed to identify deviations from normal operating conditions, even if these deviations have not yet immediately exceeded predetermined thresholds.

[0040] In addition to these technologies, domain-specific analytical methods are integrated to address the unique aspects of industrial equipment. For example, vibration analysis uses the Fast Fourier Transform (FFT) to convert time-domain vibration signals to the frequency domain, thereby identifying characteristic frequencies associated with specific mechanical faults, such as bearing defects or misalignment. Oil quality analysis monitors lubricant properties, such as viscosity and dielectric strength, to detect contamination or degradation, thus assessing equipment wear. Acoustic emission monitoring analyzes high-frequency stress waves generated by material deformation, indicating crack formation or propagation.

[0041] The predictive maintenance module 120 includes a threshold management system 123 that allows maintenance engineers to set predetermined limits for various equipment parameters based on manufacturer specifications, industry standards, or historical performance data. When real-time data shows that these thresholds are exceeded, the predictive maintenance module 120 issues a warning, prompting preventative maintenance actions. Furthermore, the anomaly detection capability of the predictive maintenance module 120 can identify subtle changes and emerging patterns that may not yet immediately exceed thresholds but indicate potential problems, providing early warnings for timely intervention.

[0042] The automated decision-making unit 124 within the predictive maintenance module 120 uses a risk assessment matrix to evaluate the severity and urgency of detected anomalies. This matrix considers the criticality of industrial equipment, the degree of parameter deviation, and their potential impact on production operations. Based on this assessment, the predictive maintenance module 120 generates maintenance recommendations and creates prioritized work orders, which are then transmitted to the major overhaul management module 130 to ensure that maintenance actions are both proactive and aligned with operational priorities.

[0043] The predictive maintenance module 120 is based on a continuous learning framework, allowing it to improve and enhance its predictive model over time. As maintenance tasks are performed and results are recorded, the predictive maintenance module 120 updates its predictive maintenance algorithm with new data stored in the database, improving the accuracy of trend degradation analysis and anomaly detection. This adaptive learning process ensures that the module can adapt to changes in equipment condition and operating environment, maintaining its effectiveness in predicting and preventing equipment failures.

[0044] The following section will provide a more detailed introduction to the major overhaul management module. Please also refer to... Figure 4 , Figure 4 The diagram illustrates an architecture of one embodiment of the critical maintenance management module of the present invention. As mentioned in the preceding paragraphs, the critical maintenance management module 130 is responsible for coordinating the planning, scheduling, and execution of a wide range of maintenance activities that typically require significant downtime or production interruptions. This critical maintenance management module 130 ensures efficient maintenance operations, optimized resource utilization, and minimal disruption to ongoing operations. It facilitates strategic maintenance planning and dynamic execution through a comprehensive suite of functionalities.

[0045] At the heart of the critical maintenance management module 130 lies its powerful maintenance scheduling component 132. This maintenance scheduling component 132 allows for the creation of detailed maintenance schedules based on insights provided by the predictive maintenance module 120. By prioritizing equipment according to its condition and failure risk, the maintenance scheduling component 132 ensures that maintenance efforts are focused on the most critical assets, thereby maximizing operational reliability and minimizing the likelihood of unplanned downtime.

[0046] The maintenance scheduling component 132 uses scheduling tools, such as Gantt charts, to visually represent the timing of maintenance tasks. This visualization helps planners coordinate maintenance activities with production schedules, ensuring that maintenance work aligns with planned production downtime or periods of reduced operational demand. The scheduling process includes resource availability checks, assessing the availability of necessary personnel, tools, and spare parts to prevent bottlenecks and ensure smooth maintenance activities.

[0047] The maintenance scheduling component 132 has dynamic scheduling and rescheduling capabilities. This allows it to adjust maintenance plans in real time, reallocate resources, and modify schedules to meet unexpected needs in the face of unexpected equipment failures or operational changes. For example, if a sudden equipment failure requires immediate repair, the maintenance scheduling component 132 can re-prioritize tasks, integrating emergency maintenance into the existing schedule without causing significant disruption to production.

[0048] The resource allocation management component 134 in the major overhaul management module 130 ensures that each maintenance task is assigned to the appropriate personnel, tools, and materials. This resource allocation management component 134 connects to the ERP integration module 150 to verify resource availability and manage inventory levels, ensuring that necessary spare parts are in stock and readily available. Personnel allocation is optimized based on skill sets, certifications, and availability, ensuring that maintenance tasks are performed by qualified and competent teams.

[0049] The monitoring and tracking component 136, part of the major overhaul management module 130, is used for monitoring and progress tracking. The monitoring and tracking component 136 allows the maintenance team to record task status, report issues, and document completed work through an intuitive user interface module 170. Performance metrics, such as schedule adherence, resource utilization, and maintenance costs, are tracked and analyzed to assess the effectiveness of maintenance activities and identify areas for improvement.

[0050] Post-maintenance analysis further enhances the effectiveness of the major overhaul management module 130, with reporting component 138 providing a detailed assessment of completed maintenance tasks. The generated comprehensive report records the work performed, the time spent, the costs incurred, and any deviations from the planned schedule. Lessons learned from each overhaul are captured and incorporated into future maintenance plans, fostering a culture of continuous improvement. Furthermore, a feedback mechanism ensures that data from completed overhauls is fed back to the predictive maintenance module 120, refining predictive maintenance algorithms and improving future maintenance strategies.

[0051] The integration of the major overhaul management module 130 with the ERP system 20 is crucial for synchronizing financial data, resource allocation, and inventory management. This integration ensures accuracy in cost tracking, budgeting, and financial reporting, providing a transparent view of maintenance expenditures and resource utilization. Automated purchase orders streamline the procurement process for required parts and materials, ensuring timely availability and reducing delivery times. Asset management is also improved, with maintenance history and equipment status updated in the ERP system 20, facilitating informed decision-making and strategic planning.

[0052] By providing a more detailed and comprehensive explanation of the Predictive Maintenance Module 120 and the Major Overhaul Management Module 130, this paper demonstrates how these two components form the core of the Integrated System 100 for Predictive Maintenance and Overhaul Management. The Predictive Maintenance Module 120 utilizes advanced algorithms and analytics to anticipate potential equipment failures, prompting proactive maintenance actions and improving operational reliability. Simultaneously, the Major Overhaul Management Module 130 coordinates the strategic planning and execution of a wide range of maintenance activities, ensuring efficient resource utilization and minimizing disruption to production processes.

[0053] The synergy between these modules enables a shift in maintenance from a reactive to a predictive and strategic paradigm, significantly improving industrial equipment reliability, reducing downtime, optimizing resource allocation, and ensuring regulatory compliance. The Integrated System 100 for Predictive Maintenance and Overhaul Management continuously learns and adapts through feedback mechanisms, further enhancing its effectiveness and fostering an environment conducive to continuous improvement and operational excellence. This integrated approach not only extends the lifespan of industrial equipment but also delivers substantial cost savings and productivity gains, establishing the Integrated System 100 for Predictive Maintenance and Overhaul Management as a powerful and innovative solution for comprehensive equipment lifecycle management.

[0054] The following will use several hypothetical scenarios to enable those with general knowledge in the art to understand the application of the integrated system 100 for predictive maintenance and overhaul management of equipment.

[0055] Scenario 1: Early detection of industrial equipment failures

[0056] In a manufacturing plant, a vibration sensor attached to a critical industrial piece of equipment detected an abnormal increase in vibration levels. Predictive maintenance module 120 analyzes this data and identifies deviations from operating conditions, triggering an alert. An automatically generated work order is sent to the maintenance team. ERP integration module 150 allocates necessary resources, ensuring the availability of spare parts and personnel. The maintenance team quickly addresses the issue, preventing catastrophic failures and avoiding unplanned downtime.

[0057] Scenario 2: Dynamic Adjustment of Maintenance Plan

[0058] During routine operations, the predictive maintenance module 120 detected the gradual degradation of several components originally scheduled for maintenance in six months. Given the accelerated wear, the critical maintenance management module 130 dynamically adjusted the maintenance plan to address these components ahead of schedule. The ERP integration module 150 updated resource allocation accordingly, and production plans were adjusted to accommodate the changed maintenance activities. This proactive adjustment ensured the reliability of the industrial equipment without causing significant disruption to the production process.

[0059] Scenario 3: Ensuring regulatory compliance for specialized industrial equipment

[0060] Special Industrial Equipment Compliance Group 140 reminds management that, according to regional regulations, an inspection of a pressure vessel is due within one month. Management uses Special Industrial Equipment Compliance Group 140 to schedule the inspection, which allocates necessary resources and notifies relevant departments. Following the inspection, Special Industrial Equipment Compliance Group 140 records the results, updates the compliance status, and generates a report for submission to regulatory agencies. This process ensures compliance with legal requirements and maintains safety standards, prevents potential regulatory violations, and enhances workplace safety.

[0061] The integrated predictive maintenance and overhaul management system offers several technological advantages. Its tight integration of various maintenance processes and data sources eliminates data silos and improves overall efficiency. Automation of alert generation, work order creation, and data analysis reduces the need for manual intervention and minimizes errors. Real-time decision-making capabilities, supported by the latest industrial equipment data, allow for evidence-based actions to improve operational reliability. The modular design of this integrated predictive maintenance and overhaul management system supports scalability, enabling it to grow with organizational needs while maintaining flexibility through extensive customization options to meet the requirements of specific industries and organizations.

[0062] Implementing an integrated predictive maintenance and overhaul management system requires consideration of multiple factors to ensure successful deployment and operation. Hardware requirements may include installing compatible sensors and establishing a robust network infrastructure to support real-time data transmission. Software integration with existing ERP systems may require developing custom APIs or using middleware solutions to facilitate tight data exchange. User training is also crucial to ensure personnel can effectively utilize the system's capabilities and adhere to new maintenance workflows. Furthermore, implementing robust security measures is essential to protect sensitive operational data and comply with data privacy regulations, ensuring the integrity and confidentiality of information managed by the system.

[0063] Beyond large-scale manufacturing (e.g., the oil refining industry), the principles of this invention's integrated system for predictive maintenance and overhaul management can be applied to a wide range of other industries. In facilities management, this system can manage the maintenance of building systems such as HVAC, elevators, and safety systems, ensuring operational efficiency and compliance with safety standards. In the transportation industry, it can oversee the maintenance of fleets, including vehicles, aircraft, or ships, where predictive maintenance and compliance are critical to safety and reliability. In the energy industry, this system can manage industrial equipment in power plants, including turbines and generators, which require stringent regulatory compliance and maintenance to ensure continuous operation and safety.

[0064] This invention provides a comprehensive solution for improving the maintenance and operational efficiency of industrial equipment through an integrated predictive maintenance and overhaul management system. By integrating predictive maintenance, major overhaul management, and compliance with specific industrial equipment requirements into a single system, this invention overcomes the limitations of existing systems. This system collects and analyzes real-time data, automates maintenance processes, integrates with ERP systems, and continuously improves through feedback mechanisms, offering significant advantages including reduced industrial equipment downtime, optimized resource utilization, ensured regulatory compliance, and extended industrial equipment lifespan. These benefits contribute to increased productivity and cost savings in industrial operations, establishing the system as a powerful and multifunctional solution for comprehensive industrial equipment lifecycle management.

[0065] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make some modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An integrated system for predictive maintenance and overhaul management of equipment, characterized in that, include: A data acquisition module is configured to collect operational data from multiple industrial devices, wherein the operational data includes real-time measurements from multiple sensors; A predictive maintenance module, configured as follows: The operational data is analyzed using a predictive maintenance algorithm to perform a trend degradation analysis. Detect industrial equipment anomalies based on a predetermined threshold and trend; and Automatically generate at least one maintenance warning and at least one work order when an industrial equipment malfunction is detected; A major overhaul management module is configured as follows: Receive input from the predictive maintenance module regarding the status of industrial equipment; Schedule at least one maintenance task during a planned production stoppage; and Coordinate resource allocation and task scheduling among multiple departments; A special industrial equipment assembly, configured as follows: Manage the inspection schedule of at least one special industrial piece of equipment; and Provide notifications and warnings for upcoming and overdue inspections; and A user interface module that presents a data visualization dashboard of the real-time status and maintenance records of the industrial equipment to at least one user.

2. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, The data acquisition module integrates IoT industrial equipment and sensors for measuring vibration, pressure, temperature, and oil quality parameters.

3. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, The predictive maintenance module uses machine learning algorithms to improve the predictive maintenance algorithm over time.

4. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, The automatic generation of this work order is triggered by deviations of standard industrial equipment parameters from predetermined limits.

5. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, This major overhaul management module dynamically adjusts the inspection schedule based on real-time industrial equipment status data.

6. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, The operational data collected by this data acquisition module includes manually entered data.

7. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, This special industrial equipment includes boilers, pressure vessels, or elevators.

8. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, It also includes a feedback mechanism that records completed maintenance activities to a database and sends them back to the predictive maintenance module to continuously improve and enhance the predictive maintenance algorithm.

9. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, It further includes a security module connected to the user interface module, which manages user access control, data encryption, and complies with data privacy regulations.

10. The integrated system for predictive maintenance and overhaul management of equipment as described in claim 1, characterized in that, It also includes an ERP integration module that connects to at least one external ERP system to perform: Simultaneously check schedules and work orders; and Manage the inventory and resource utilization for this maintenance task.

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