Multi-Machine Automation Control for Failure Prediction and Peak Power
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Solution Overview
Problem
In automated facilities with multiple machines, existing systems fail to effectively consolidate and analyze sensor inputs from individual machines, leading to missed opportunities for efficiency improvements and increased downtime due to the lack of centralized data analysis.
Innovation Solution
An automation operating and management system that consolidates data from multiple machines, predicts failures, and generates proactive countermeasures to prevent downtime, while also optimizing power consumption by coordinating the operational sequences of machine elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines are operated independently with individual controllers, then each machine can be controlled autonomously, but opportunities to increase facility efficiency and decrease facility downtime are missed due to lack of consolidated data analysis
Solution Approach 1:
The patent merges data from multiple independent machine controllers into a centralized server system. The server consolidates sensor inputs, operational data, and diagnostic information from all machines to perform comprehensive facility-wide analysis, enabling identification of efficiency improvement opportunities and predictive maintenance insights that would be invisible at the individual machine level.
Solution Approach 2:
The patent introduces a server as an intermediary between individual machine controllers and facility management. This intermediary consolidates data from multiple sources, performs centralized analysis using machine learning algorithms, and generates facility-wide optimization recommendations, bridging the gap between autonomous machine operation and coordinated facility efficiency.
2Reliability
If machines are operated independently, then each machine can function autonomously, but facility downtime increases due to inability to predict failures across the facility
Solution Approach 1:
The patent implements preliminary action through predictive maintenance by analyzing consolidated data from multiple machines to identify potential failures before they occur. The system uses machine learning algorithms to detect patterns and anomalies in sensor data, predicting failures in advance and enabling proactive maintenance scheduling that prevents unplanned downtime and maintains continuous facility operation.
Solution Approach 2:
The patent establishes a feedback loop where the server continuously receives operational data from all machine controllers, analyzes trends using machine learning, and provides real-time insights for preventing failures. This feedback mechanism enables the facility to adjust operations and maintenance schedules based on predictive analytics, reducing unplanned downtime and improving overall reliability.
3Productivity
If multiple machines operate simultaneously, then production output increases, but peak power consumption demand increases
Solution Approach 1:
The patent applies periodic action by using the server to monitor and coordinate the operational cycles of multiple machines. The system analyzes timing data from sensor inputs to identify patterns in machine operation cycles and schedules maintenance, upgrades, or operational adjustments in periodic intervals that smooth out peak power demands while maintaining continuous production output.
Solution Approach 2:
The patent introduces dynamics by enabling the facility to adapt machine operations based on real-time power consumption patterns analyzed by the server. The system dynamically adjusts operational parameters, schedules maintenance during low-demand periods, and coordinates machine cycles to balance production requirements with power consumption constraints, reducing peak demands without sacrificing overall productivity.
Data Source
AI summary
An automation operating and management system consolidates and analyzes inputs from multiple machines within an automated enterprise to predict failures and provide instructions for counteractions to prevent failures during machine operation, and to identify opportunities for efficiency improvement, including actions for reduction in peak power consumption demand within a facility including multiple machines. A machine can include a machine controller and at least one base layer controller, where the base layer controller acts as a low level controller to directly control the motion of elements in communication with the base layer control, according to parameters set by the machine controller. The base layer controller collects timing data for the elements under its control, compares the timing data with the parameters and sets an alarm when the timing data is outside of tolerance limits defined by the parameters.