Data Harvesting System for Electrical Unit Reliability Testing
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Solution Overview
Problem
Existing quality control methods fail to accurately assess the performance and reliability of electrical or electro-chemical units over their lifetime, as laboratory testing often lacks real-world representation and may only identify atypical failures, failing to predict mean time between failures for properly manufactured products.
Innovation Solution
A system and method for systematically collecting and testing representative product samples returned from customers, comparing their performance characteristics to new units, and incorporating results into a database accessible to customers and manufacturers, allowing for feedback and analysis across varying operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If laboratory testing is used to evaluate product performance, then testing can be conducted in a controlled environment, but the testing results become non-representative of real world performance
Solution Approach 1:
Instead of testing products in controlled laboratory environments, the invention inverts the approach by collecting and testing products in their actual field usage environments. Field-collected samples naturally reflect real-world operating conditions, customer applications, and usage patterns, thereby providing representative performance data that accurately predicts real-world behavior.
Solution Approach 2:
The invention introduces a third-party logistics provider as an intermediary to facilitate the collection, transportation, and management of field-collected product samples. This intermediary enables systematic acquisition of representative samples from diverse field locations while maintaining product integrity and establishing proper chain of custody.
2Reliability
If testing focuses on failed or defective product returned from the field, then atypical process failures can be identified, but product design failures and mean time between failures cannot be predicted
Solution Approach 1:
The invention changes the selection criteria from binary (failed vs. non-failed) to a spectrum based on product age and usage intensity. By stratifying samples according to operational parameters such as time-in-service, mileage, or cycle count, the system captures both early-life and end-of-life performance characteristics, enabling prediction of mean time between failures for properly manufactured products.
Solution Approach 2:
The system establishes a feedback loop where field performance data from collected samples is analyzed to identify patterns, update reliability models, and provide actionable insights back to manufacturers and customers. This feedback mechanism enables continuous improvement of product design and predictive maintenance capabilities.
3Reliability
If representative samples are collected from diverse customers and operating conditions, then comprehensive performance evaluation is achieved, but system complexity increases
Solution Approach 1:
The invention creates a universal sample collection framework that can accommodate multiple product types, applications, and customer segments through a single standardized process. The system uses standardized collection forms, universal shipping containers, and a centralized database structure that handles diverse sample types, thereby reducing operational complexity while maintaining comprehensive evaluation capabilities.
Data Source
AI summary
Data harvesting can be carried out relative to performance or reliability information associated with one or more groups of electrical units. Ambient condition detectors associated with a variety of industrial or commercial installations and subject to a variety of different conditions can be returned for performance and reliability testing after predetermined usage intervals. Analysis of test results can be maintained in a database. Customers can be provided multilevel access to the information in the database. Reliability and test results for a class of detectors can be provided to a number of customers that have provided samples for evaluation. Application specific information can be limited to a particular customer or customers.