Sensor Array Quality Separation for Faster Fault Differentiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor arrays in production lines require significant response time and manual intervention for changes or fault detection, making it difficult to differentiate between marking defects and sensor impairments, leading to inefficiencies and potential errors.
Innovation Solution
A sensor array with a processing unit that uses matrix factorization algorithms to separate sensor quality values and operating means quality values, enabling independent detection and management of sensor and marking qualities, and providing real-time feedback for improved operational control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor arrays are used to monitor production lines, then detection capability is improved, but response time deteriorates due to manual intervention requirements
Solution Approach 1:
The system automatically separates sensor quality values from operating means quality values using matrix factorization algorithms, enabling self-diagnosis and self-monitoring without manual intervention. The processing unit autonomously determines whether reading errors stem from sensor impairments or marking defects, eliminating the need for planner involvement in routine quality assessments.
2Device complexity
If traditional sensor quality detection is used, then system simplicity is maintained, but fault differentiation capability deteriorates
Solution Approach 1:
The quality assessment is segmented into two independent components: sensor quality values and operating means quality values. The matrix factorization algorithm decomposes the overall quality matrix into these separate factors, enabling independent evaluation of sensor performance and marking quality without requiring complex additional hardware.
Solution Approach 2:
The system transforms the quality assessment from a single aggregate metric into multiple independent parameters (sensor quality values and operating means quality values). This parameter transformation enables differentiated fault detection while maintaining computational efficiency through established matrix factorization techniques.
3Device complexity
If manual planner intervention is required for sensor quality assessment, then system complexity is reduced, but productivity deteriorates
Solution Approach 1:
The manual mechanical process of planner intervention is replaced with an automated computational system. The processing unit executes matrix factorization algorithms to automatically separate and assess quality values, substituting human analysis with computational processing that operates continuously without interruption to production flow.
Data Source
AI summary
A sensor array (100) with one or more sensors (201-204), with one or more operating means (101-104), each of which labels an object (801, 802), and with a processing unit (501) that is connected to the sensor or the sensors (201-204) via a communication connection (401), wherein each sensor (201-204) is designed for reading and unambiguously identifying an operating means (101-104). In each sensor (201-204), the time at which an operating means (101-104) was detected, the operating means identification of the operating means (101-104), a sensor identification that unambiguously identifies the sensor, and a quality variable q(t, s, b) determined based on time, sensor and operating means for the detection of the operating means (101-104) is sent to the processing unit (501). The invention further relates to a corresponding method.