Electronic Component Fault Detection Using Normal Behavior Time Series
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional maintenance strategies for Printed Circuit Board Assembly (PCBA) production are inadequate in adapting to the evolving needs of modern production environments, lacking a robust approach to equipment maintenance.
Innovation Solution
A method involving measuring physical values over time using a test machine, constructing a second time series with an unsupervised machine learning model like an autoencoder to predict component faults, and calculating errors to identify defects, followed by proactive replacement of faulty components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance strategies are used for PCBA production, then equipment maintenance is simpler and less complex, but reliability and ability to adapt to evolving production needs deteriorate
Solution Approach 1:
The patent applies preliminary action by continuously monitoring physical values (temperature, voltage, current, etc.) of electronic components before actual failure occurs. The system establishes baseline behavior patterns and detects deviations early, enabling proactive maintenance decisions rather than waiting for component failure. This is achieved through continuous data collection and comparison against learned normal operating patterns.
Solution Approach 2:
The patent implements feedback mechanisms by continuously comparing measured physical values against learned baseline patterns and providing real-time fault detection. The system learns normal operating patterns from historical data and feeds this information back to identify anomalies, creating a closed-loop maintenance system that adapts and improves over time through continuous monitoring and pattern recognition.
2Adaptability or versatility
If traditional maintenance strategies are used, then device complexity is lower, but adaptability to evolving production needs deteriorates
Solution Approach 1:
The patent applies dynamics by implementing a maintenance system that continuously adapts to changing production conditions and component behavior patterns. The system dynamically learns new baseline patterns as production evolves, adjusting its monitoring thresholds and anomaly detection criteria to match current operating conditions rather than relying on static maintenance schedules.
Solution Approach 2:
The patent utilizes parameter changes by monitoring multiple physical parameters (temperature, voltage, current, frequency, etc.) simultaneously and analyzing their evolution over time. The system detects faults by identifying abnormal changes in these parameters compared to learned baseline patterns, enabling adaptability to different component types and operating conditions through multi-parameter analysis.
3Measurement precision
If continuous monitoring of physical values is implemented, then fault detection accuracy improves, but measurement and data processing requirements increase
Solution Approach 1:
The patent applies the extraction principle by isolating and analyzing specific physical parameters (temperature, voltage, current, frequency) that are most indicative of component health. Rather than attempting to monitor all possible parameters simultaneously, the system extracts and focuses on the most relevant measurements, reducing data processing complexity while maintaining high fault detection accuracy.
Solution Approach 2:
The patent uses copying by creating virtual models or digital twins of component behavior patterns based on learned baseline data. Instead of directly analyzing raw measurement data, the system compares actual measurements against copied ideal behavior patterns, simplifying the detection process while maintaining high precision in fault identification.
4Productivity
If proactive replacement of faulty components is implemented, then productivity is maintained through minimized downtime, but component replacement frequency increases
Solution Approach 1:
The patent applies preliminary action by identifying and replacing components before they actually fail. The system detects early signs of degradation through continuous monitoring of physical parameters and schedules replacement during planned maintenance windows rather than during unexpected failures, minimizing disruption to production productivity.
Data Source
AI summary
A method, implemented by a computer, for evaluating an electronic component on an electronic board. The method includes: measuring an evolution over time of a physical value of the component with a test machine, to obtain a first time series; defining a second time series corresponding to an evolution over time of the physical value of the component without defects; and calculating an error expressing the differences between both time series.