Automated Data Validation for Machine Monitoring Systems
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Solution Overview
Problem
Current machine monitoring systems face inefficiencies due to the labor-intensive and time-consuming process of validating large quantities of data for accuracy, which often results in missed inaccuracies and requires costly, disruptive on-site installation of factory information systems for data validation and certification.
Innovation Solution
A system and method utilizing a machine monitoring data mining and analysis engine with algorithms executable by electronic processors to validate data by identifying patterns in real-time machine monitoring data, allowing for the recording and output of data to a factory information system without on-site installation, thereby facilitating remote validation and reducing logistical and operational burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a subject matter expert manually reviews machine monitoring data to validate accuracy, then data validation can be performed with human judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated electronic processing system that uses algorithms to validate machine monitoring data. The controller automatically compares actual machine data against expected parameter ranges and logical relationships, eliminating the need for manual subject matter expert review while maintaining validation accuracy.
Solution Approach 2:
The machine monitoring system performs self-validation through automated algorithms embedded in the controller. The system independently validates its own data by comparing readings against predefined parameters and logical constraints, eliminating the need for external human validation while reducing time loss.
2Quantity of substance
If thousands of data entries are generated by machine monitoring systems, then comprehensive information about machine condition is captured, but the sheer quantity of data becomes impossible to completely review
Solution Approach 1:
The patent replaces manual data review with automated electronic processing that can handle large volumes of data entries. The controller systematically processes thousands of data points by comparing them against expected parameters and logical relationships, enabling comprehensive review of all machine monitoring data without human limitations.
Solution Approach 2:
The validation process is segmented into multiple automated checks including parameter range validation, logical relationship validation, and anomaly detection. This segmentation allows the system to process large volumes of data through systematic, modular validation steps that can be executed rapidly by the controller.
3Reliability
If on-site factory information system installation is performed for data validation and certification, then accurate validation can be achieved, but the process becomes costly and operationally disruptive
Solution Approach 1:
The patent extracts the validation functionality from the factory information system and integrates it directly into the machine's controller. This allows data validation to be performed locally at the machine level without requiring separate on-site installation of external validation systems, eliminating operational disruption and reducing costs.
Solution Approach 2:
The controller is designed to perform multiple functions including data collection, processing, and validation. By integrating validation capabilities into the existing controller, the system eliminates the need for separate validation hardware or software installations, reducing complexity while maintaining validation reliability.
4Measurement precision
If highly trained subject matter experts are used to validate machine data, then accurate anomaly identification can be achieved, but labor costs and time requirements increase significantly
Solution Approach 1:
The patent replaces the need for highly trained subject matter experts with automated algorithms embedded in the controller. The system uses predefined parameter ranges, logical relationships, and anomaly detection algorithms to identify data inaccuracies automatically, maintaining detection accuracy while eliminating the complexity of human training and deployment.
Data Source
AI summary
A system and method for validating data for a machine arrangement includes a controller, such as a programmable logic controller, connected to a processor, which is configured to execute an algorithm using the data collected by the controller. The algorithm includes one or more rules that are applicable to the collected data. Each rule is applied to the data to determine if any of the data entries violate one or more of the rules. An output is generated indicating whether the data violates any of the rules, thereby providing information regarding the validity of the data being captured.