Electronic Circuit Fault Localization with Discriminating Test Vectors
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Solution Overview
Problem
Existing methods for identifying faulty components in complex electrical and electronic systems are inefficient, time-consuming, prone to errors, and lack scalability, making it difficult to maintain and repair these systems effectively.
Innovation Solution
A method and device for generating a set of test vectors using simulated faulty situations and sensor data to identify faulty components, utilizing an electronic device with a processor and memory, and employing SMT solvers to determine discriminating input vectors and sensor signatures, allowing for efficient and accurate fault location identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test methods are used to identify faulty components, then test coverage can be achieved, but test duration and complexity increase significantly
Solution Approach 1:
The system pre-generates a test plan during system design that identifies all necessary tests and their expected outcomes. This preliminary action allows the test execution phase to simply follow the pre-determined plan, dramatically reducing test duration while maintaining comprehensive coverage. The test plan is created before any actual testing occurs, specifying exactly which tests to run and how to interpret results.
Solution Approach 2:
The system creates a virtual model of the system under test and generates test cases by simulating various fault conditions in this virtual model. Instead of manually designing tests for each possible failure mode, the system copies the system structure into a virtual environment and systematically explores failure scenarios there, then applies the same tests to the actual system.
2Reliability
If comprehensive test coverage is implemented to identify all possible faults, then reliability improves, but the number of tests and device complexity increase
Solution Approach 1:
The system automatically generates its own test plan and executes tests without requiring external test equipment or manual intervention. The system under test serves its own testing needs by utilizing its existing sensors and actuators, eliminating the need for complex external test equipment. The system self-diagnoses by comparing actual sensor readings against expected values from the virtual model.
Solution Approach 2:
The system uses its existing sensors and actuators for dual purposes: normal operation and self-testing. The same sensors that monitor system operation during normal use are also used for fault detection during testing. This multi-functionality eliminates the need for separate dedicated test equipment, reducing overall system complexity while maintaining comprehensive test coverage.
3Ease of operation
If manual test interpretation is performed, then flexibility is maintained, but error risk and time consumption increase
Solution Approach 1:
The system automatically compares actual sensor readings against expected values from the virtual model and immediately determines whether faults are present. This closed-loop feedback mechanism eliminates manual interpretation by continuously monitoring sensor data and automatically determining test results. The system provides real-time feedback on system health status without requiring human analysis.
Solution Approach 2:
The system replaces manual test interpretation with automated electronic processing. Instead of human operators analyzing test results, the system uses electronic comparison of sensor readings against expected values to automatically determine fault conditions. This substitution of mechanical/manual processes with electronic automation eliminates interpretation errors and reduces time consumption.
Data Source
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AI summary
The disclosure relates to a method for generating test vectors to locate faulty components within an electronic circuit. The method involves: - Generating (S01) simulated faulty situations for the circuit and recording sensor data for each situation. - Encoding (S02) the recorded data into encoded triplets. - Using (S03) the encoded triplets to determine a subset of test vectors that can discriminate between different faults. - Storing t(S04) he determined discriminating input test vectors and their associated sensor signatures in a test data structure.