Dynamic alteration of operational parameters of a machine
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Solution Overview
Problem
Existing systems fail to dynamically adjust operational parameters of machines in a multi-machine environment, leading to inefficiencies in maintenance and cleaning, particularly in industrial settings where machines accumulate contaminants over time.
Innovation Solution
A computer-based system that utilizes IoT feeds to assess machine health conditions, determines maintenance needs, and adjusts operational parameters by deploying service robots with optimized movement paths and parameter adaptations, including motor stops and RPM reductions, to ensure timely and efficient maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines operate continuously without dynamic parameter adjustment, then productivity is maintained, but machine wear and contaminant accumulation increase leading to reduced reliability
Solution Approach 1:
The system dynamically adjusts operational parameters of machines based on real-time health conditions detected by IoT sensors. Instead of static continuous operation, the system modifies parameters such as speed, load, or temperature thresholds dynamically, allowing machines to operate optimally while preventing excessive wear and contaminant accumulation that would compromise reliability.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where IoT devices continuously monitor machine health parameters, and this information feeds back to automatically adjust operational parameters. This feedback loop enables the system to respond to changing machine conditions in real-time, balancing productivity maintenance with reliability preservation by preventing degradation before it leads to failure.
2Reliability
If maintenance actions are performed immediately when health conditions deteriorate, then reliability is improved, but productivity is reduced due to machine downtime
Solution Approach 1:
The system performs preliminary maintenance actions by proactively adjusting operational parameters before health conditions deteriorate to critical levels. By monitoring trends in IoT sensor data, the system can initiate parameter adjustments that prevent further degradation, allowing maintenance to be performed gradually and proactively rather than reactively, thus minimizing disruptive downtime while maintaining reliability.
Solution Approach 2:
Instead of performing complete maintenance shutdowns, the system applies partial maintenance actions by adjusting only specific operational parameters that are contributing to degradation. This selective approach allows the machine to continue operating at reduced or modified capacity rather than complete shutdown, maintaining partial productivity while still addressing the reliability issue through targeted parameter modifications.
3Duration of action of stationary object
If operational parameters are dynamically adjusted, then machine lifespan is extended, but system complexity increases
Solution Approach 1:
The system employs multi-functional components that serve both monitoring and control functions. IoT devices not only collect health data but also communicate with the control system that adjusts operational parameters, creating a unified platform that extends machine lifespan without requiring entirely separate monitoring and control systems. This universality reduces the overall system complexity despite the dynamic adjustment capabilities.
Data Source
AI summary
An embodiment for altering operational parameters of a machine in a multi-machine environment is provided. The embodiment may include receiving an IoT feed from one or more IoT devices and data relating to an activity in a multi-machine environment. The embodiment may also include identifying a health condition of one or more machines. The embodiment may further include in response to determining at least one machine of the one or more machines requires one or more maintenance actions, identifying a timeframe during which the one or more maintenance actions are able to be performed. The embodiment may also include updating a movement path of one or more service robots in the multi-machine environment. The embodiment may further include deploying the one or more service robots to execute the one or more maintenance actions. The embodiment may also include adapting one or more operational parameters of the one or more machines.