Remote Diagnostic System for Predictive Maintenance
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Solution Overview
Problem
Current systems lack the ability to accurately predict service events in software-controlled mechanical systems, leading to unexpected downtime and costly maintenance in industries like crane operations, where certain components are difficult to inspect without disassembly, resulting in potential additional wear and inefficient maintenance schedules.
Innovation Solution
A remote diagnostic system that utilizes a device model coupled with data collection and analysis to predict service events by integrating data from various sources, including operational profiles, weather, and usage data, allowing for proactive maintenance scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routine inspections and maintenance are performed based on hours of operation and calendar days, then preventive maintenance can be scheduled, but components that are difficult to inspect (such as internal wear pads and seals) cannot be accurately assessed without disassembly
Solution Approach 1:
The patent replaces physical disassembly and mechanical inspection methods with sensor-based detection systems. Sensors mounted on components continuously monitor operational parameters (vibration, temperature, pressure) to detect wear and anomalies without requiring physical access to internal components like wear pads and seals, thereby substituting mechanical inspection with electronic sensing.
Solution Approach 2:
The patent introduces sensors as intermediary devices between the mechanical components and the inspection process. These sensors act as mediators that translate physical component conditions into measurable electrical signals, enabling indirect observation of internal component states without direct physical contact or disassembly.
2Measurement precision
If components are disassembled for inspection, then internal components (wear pads, seals) can be examined, but additional wear is caused and work stoppage occurs
Solution Approach 1:
The patent replaces mechanical disassembly with sensor-based monitoring that detects internal component conditions through non-invasive measurement of operational parameters, eliminating the need to physically open or disassemble components and thereby preventing additional wear from disassembly operations.
Solution Approach 2:
The system enables components to self-report their condition through integrated sensors that continuously monitor their own operational state, eliminating the need for external inspection interventions that would require disassembly and potentially cause wear.
3Productivity
If unexpected service events occur in software-controlled mechanical systems, then system operation can be maintained, but complete work stoppage and substantial costs result
Solution Approach 1:
The patent implements continuous feedback loops where sensors monitor component conditions in real-time, this data is processed by control software that compares actual readings against expected parameters, and corrective actions are triggered before service events occur, enabling proactive maintenance that prevents work stoppages.
Solution Approach 2:
The system performs preliminary detection and diagnosis of potential service events by continuously monitoring operational parameters and identifying trends that precede failures, allowing maintenance to be scheduled in advance before actual service events occur and cause work stoppages.
4Ease of manufacture
If mathematical models are used to emulate mechanical systems during design, then software can be tested without installation, but accurate prediction of actual field service events remains difficult
Solution Approach 1:
The patent transitions from static mathematical models used during design to dynamic sensor-based monitoring systems that adapt to actual field conditions. The system continuously updates its understanding of component behavior based on real-time operational data, enabling accurate prediction of service events despite variations from idealized design models.
Data Source
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AI summary
A remote diagnostic system for remotely diagnosing and developing a dynamic system including a dynamic system being controlled by a first control system and a device model being controlled by a second control system. The device model simulates the dynamic system and inputs and corresponding outputs are recorded to test the control system and operation of the dynamic system. During operation the dynamic systems inputs and outputs are recorded. The dynamic system input and outputs may then be compared to the device model inputs and outputs to check the accuracy of the device model. The device model may then be updated based on the results of the comparison.