AI Data Management Architecture for Predictive Maintenance
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Solution Overview
Problem
Current data management systems are outdated, involving manual data acquisition and management, leading to human error, latency, and inability to adapt to advances in machine learning and AI, as well as lacking self-diagnosis, predictive maintenance, and robust data security.
Innovation Solution
The Digital Information Management System (DIMS) addresses these issues by integrating five core functions: continuous data acquisition, transmission, processing, presentation, and user notification, utilizing AI and machine learning for data validation, predictive analytics, self-diagnosis, and robust data security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual or intermittent data acquisition is used, then system complexity is reduced, but productivity and data processing speed deteriorate
Solution Approach 1:
The system performs self-diagnosis and automatic data acquisition without requiring manual intervention. The data acquisition units continuously collect data and the system automatically processes and analyzes the information, enabling the system to serve itself and eliminating the need for human operators to manually collect or initiate data gathering processes.
Solution Approach 2:
The patent replaces manual mechanical data collection methods with automated electronic data acquisition units that continuously collect data from sensors and sources. This substitution of mechanical/manual processes with electronic automation systems dramatically increases data processing speed while managing system complexity through standardized interfaces.
2Ease of operation
If manual data management is used, then ease of operation is improved, but reliability deteriorates due to human error
Solution Approach 1:
The system automatically validates, verifies, and processes data without human intervention. Machine learning models automatically analyze data quality and the system performs self-diagnosis to ensure data integrity, eliminating human error while maintaining operational simplicity through automated processes.
Solution Approach 2:
The system incorporates continuous validation and verification processes that provide feedback on data quality. Machine learning models analyze incoming data and provide feedback on data integrity, automatically correcting or flagging issues to ensure high reliability while keeping the system easy to operate through automated quality control.
3Device complexity
If static data models are used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system transitions from static data models to dynamic machine learning models that continuously learn and adapt to new data patterns. The machine learning models are designed to evolve over time, automatically adjusting to changing conditions and requirements while managing complexity through modular architecture and standardized processing pipelines.
Solution Approach 2:
The system dynamically changes parameters and model configurations based on incoming data and performance requirements. Machine learning models automatically adjust their parameters and structure to adapt to new data patterns and conditions, enabling the system to handle diverse scenarios while maintaining manageable complexity through automated parameter optimization.
4Ease of manufacture
If traditional data processing systems are used, then ease of manufacture is improved, but productivity deteriorates due to data input latency
Solution Approach 1:
The system implements continuous data acquisition and processing operations without interruption or manual intervention. Data acquisition units continuously collect data from sensors and sources, and the processing pipeline operates continuously to analyze and act on data in real-time, maximizing productivity while using standardized components that maintain ease of implementation.
Solution Approach 2:
The patent replaces traditional batch processing and manual data entry systems with continuous automated electronic data processing. Electronic data acquisition and processing systems operate continuously at high speed, eliminating the latency and bottlenecks of manual processes while using standardized interfaces and protocols that maintain ease of system implementation and integration.
5Device complexity
If systems without self-diagnosis capability are used, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The system performs self-diagnosis and self-monitoring automatically without requiring external intervention. Built-in diagnostic capabilities continuously monitor system health and data quality, and the system automatically detects and reports issues, enhancing reliability while managing complexity through integrated self-monitoring functions rather than separate external monitoring systems.
6Ease of operation
If systems without predictive maintenance are used, then ease of operation is improved, but loss of time increases due to unplanned downtime
Solution Approach 1:
The system performs predictive maintenance by analyzing data patterns and predicting potential failures before they occur. Machine learning models identify early signs of equipment degradation or system issues and trigger maintenance actions in advance, preventing unplanned downtime while maintaining operational simplicity through automated predictive analytics and scheduled maintenance workflows.
Data Source
AI summary
Described herein is the Digital Information Management System (DIMS) which uses advances in AI and/or machine learning to automate data management. In some embodiments, DIMS offers self-diagnosis in the interest of providing effective, timely maintenance, and continuing operation while systems are down. Embodiments of anti-malware are also described, covering common attack vectors in an Internet of Things sensor system, and bolstered by AI and/or machine learning. Data processing and presentation is further enhanced by AI and/or machine learning, to validate, verify, and extrapolate/interpolate values in a variety of formats. Embodiments of visualization subsystems are also described, which include a variety of analytics and recommendations to further automate and increase the efficiency of data analysis across a wide range of assets and/or phenomena. Notification connects users to DIMS with tailored updates, notifications, predictive maintenance alerts, queries, and other specified information.