Modular Control System with ID Tags for Predictive Maintenance
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Solution Overview
Problem
Complex industrial and commercial machinery often experience downtime due to component failures, requiring multiple skill sets for diagnosis and repair, leading to increased costs and inefficiencies in maintenance and operation.
Innovation Solution
A modular control system utilizing ID Tags and sensors for real-time monitoring and management, enabling automatic identification, configuration, and maintenance of machine components, along with user community-generated analytics and marketplace data for predictive maintenance and component replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and predictive analytics are implemented, then downtime and maintenance costs are reduced, but device complexity increases
Solution Approach 1:
The system segments machinery into modular components, each with embedded sensors and ID tags. This segmentation allows independent monitoring of individual components while maintaining overall system reliability, reducing the complexity burden on the central control system.
Solution Approach 2:
Components perform self-diagnosis and self-identification through embedded sensors and ID tags. The system automatically detects component status, identifies failures, and predicts maintenance needs without requiring complex external monitoring infrastructure.
2Measurement precision
If multiple skill sets are required for diagnosis and repair, then component failures can be accurately diagnosed, but loss of time increases due to coordinating multiple specialists
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor component parameters and automatically diagnose failures. Diagnostic information is fed back to the control system which identifies the specific component and required repair actions, eliminating the need for multiple specialists to coordinate.
Solution Approach 2:
An automated diagnostic intermediary system processes sensor data and translates it into actionable repair information. This intermediary eliminates the need for multiple human specialists by providing precise diagnostic data and repair guidance to a single technician.
3Ease of repair
If machines sit idle while repairs are performed, then necessary maintenance can be completed, but productivity decreases
Solution Approach 1:
The system performs preliminary actions by predicting component failures before they occur. Maintenance is scheduled proactively based on predicted failure timelines, allowing repairs to be performed during planned downtime rather than causing unexpected machine idle time.
Solution Approach 2:
The system enables rapid diagnosis and repair by automatically identifying failed components and providing repair guidance. This allows technicians to quickly locate and fix issues, minimizing the time machines need to remain idle during repairs.
Data Source
AI summary
Embodiments are directed towards providing analytics and marketplace data to members of a user community. In some embodiments, the analytics and marketplace data are generated based on machine data provided by the members. The analytics and marketplace data may enable automatically identifying, configuring, monitoring, controlling, managing, and/or maintaining a machine or a collection/system of machine components. The analytics may include, but are not limited to analytics related to machine component reliability, machine maintenance conditions, machine prohibited conditions, machine usages, machine alert conditions, and the like. The marketplace data may include information relating to the maintenance of the machine, replacement components or alternative components for the machine, and the like for various machines. Marketplace data may include an aggregation of electronic (e)-commerce data. The marketplace data may be provided is based on data aggregated from various sources, including vendors, suppliers, buyers, sellers, online auctioneers, or other members of the user community.