Digital Input Edge Detection With Smart Filtering for Invalid Samples
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Solution Overview
Problem
Current systems lack an effective method to identify systemic failures and root causes of incidents across multiple events, often focusing on individual incidents, which limits the effectiveness of corrective actions and increases the likelihood of short-term fixes rather than long-term solutions.
Innovation Solution
A digital input edge detection method with smart filtering is introduced, capable of ignoring interference from self-tests, contact bounce, and external influences, using a configurable filter time constant and exclusion masks to accurately determine the primary cause of failure sequences in low-latency sequence of events reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a digital filter with configurable time constant is used to ignore interference from self-tests and contact bounce, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by implementing a configurable filter time constant that can be adjusted based on the specific application requirements. This allows the filtering algorithm to adapt its behavior - using longer time constants to suppress contact bounce and shorter ones to respond quickly to actual edges. The parameter-based approach resolves the contradiction by making the complexity adjustable rather than fixed, allowing users to balance precision and complexity based on their needs.
Solution Approach 2:
The patent implements dynamics by using a state machine that dynamically transitions between different filtering states based on the input signal characteristics. The filter adapts its behavior in real-time, switching between ignoring samples during self-tests, filtering contact bounce, and detecting actual edges. This dynamic approach allows the system to maintain high measurement precision while managing complexity through adaptive rather than static filtering logic.
2Reliability
If a smart filtering algorithm is used to exclude invalid samples during self-tests, then reliability is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-defining exclusion masks that identify time periods during which self-tests are performed. Rather than analyzing each sample to determine if it's valid, the system has already marked these exclusion periods in advance. This allows the filtering algorithm to quickly skip over invalid samples without performing complex analysis, thereby improving reliability while minimizing processing time loss.
Solution Approach 2:
The patent implements segmentation by dividing the time domain into distinct segments - valid sampling periods and excluded self-test periods. By segmenting the data stream and tagging each segment with validity information, the system can efficiently process only the relevant portions. This segmentation approach improves reliability by ensuring invalid samples are never misinterpreted as events, while reducing processing time by avoiding analysis of excluded segments.
3Productivity
If continuous data collection is performed during self-tests with exclusion masks, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The patent applies the intermediary principle by introducing exclusion masks as a mediating layer between the raw data collection process and the event detection algorithm. The exclusion masks act as a filter that marks certain time periods as invalid without preventing data collection. This intermediary mechanism allows continuous productivity - data is collected throughout - while protecting measurement precision by ensuring invalid samples are properly identified and excluded from event detection.
Solution Approach 2:
The patent implements taking out by extracting the validity determination function from the main event detection process. Rather than having the event detection algorithm worry about distinguishing valid from invalid samples, the exclusion masks pre-extract and mark the validity information. This separation allows continuous data collection at high productivity while the extraction of invalid samples ensures measurement precision is maintained in the remaining valid data.
Data Source
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AI summary
A method is provided that provides data analysis for sequence of events reporting in the operation of an industrial process. A digital filter and edge detector are provided that combines a method for excluding known invalid samples and a method for excluding samples taken while the input in the traveling range. The filtering method reduces the overhead on the CPU from managing the sequence of events machine and allows it to focus on performing safety functions.