Remote Alarm Response Control for Multi-Machine Recovery
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Solution Overview
Problem
Efficiently operating facilities with multiple machines that experience failures requires significant personnel and training, leading to increased costs and inefficiencies due to the need for human intervention to address machine alarms, which can be time-consuming and labor-intensive.
Innovation Solution
Implementing a Central Command System with central control circuits connected over a network that can remotely interface with machines, using machine learning applications to generate commands and respond to alarms without direct human intervention, allowing for continuous operation and parallel handling of multiple machine failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human personnel are used to respond to machine alarms, then machines can be returned to production, but significant personnel and training are required leading to increased costs and inefficiencies
Solution Approach 1:
The system enables machines to respond to their own alarms through automated control circuits that can autonomously diagnose alarm conditions and execute corrective actions to return machines to production without human intervention
Solution Approach 2:
The patent replaces the mechanical system of human operators physically responding to alarms with an automated control circuit system that remotely interfaces with machines over a network to diagnose and resolve alarm conditions
2Loss of time
If human operators respond to alarms sequentially, then machines receive attention, but response time is increased due to walking distances and sequential handling
Solution Approach 1:
The patent introduces a network-based control circuit intermediary that communicates between multiple machines and operators, enabling simultaneous monitoring and response to alarms across the facility without requiring physical movement between machines
Solution Approach 2:
The automated control system enables continuous monitoring and response to alarm conditions across all connected machines simultaneously, eliminating the interruptions and idle time associated with sequential human response
3Reliability
If extensive personnel training is provided for machine operation, then machines can be properly operated and maintained, but training costs and time consumption increase
Solution Approach 1:
The control circuits are integrated into the machine systems themselves, enabling the machines to autonomously monitor their own status and execute corrective actions, eliminating the need for trained personnel to perform diagnostic and corrective tasks
Data Source
AI summary
A central control circuit is configured to remotely connect to a plurality of machines over a network. Each machine has a respective user interface to indicate a machine state and enable user input. The central control circuit is configured to receive an alarm code, determine whether the alarm code corresponds to a machine state for which a machine learning application has been trained, and obtain an image from the user interface in response to a determination that the machine learning application has been trained for the machine state. The central control circuit is further configured to analyze the image to identify one or more features, generate one or more commands in the machine learning application, and send the one or more commands to the user interface according to the features to change the machine state.