Digital Input Edge Detection with Smart Filtering for False Event Rejection
Find Innovative SolutionsGenerate Solutions
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 system 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 provide accurate sequence of events analysis and pinpoint primary causes of failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital filtering is applied to remove 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 to optimize the balance between filtering effectiveness and response speed. The hysteresis threshold parameter is also modified to prevent false edge detection during signal transitions, thereby improving measurement precision without requiring complex hardware modifications.
Solution Approach 2:
The patent replaces complex hardware filtering circuits with a software-based digital filtering algorithm. This substitution uses computational methods (exponential moving average calculation and hysteresis comparison) to achieve the same noise rejection that would traditionally require complex RC circuits or active filters, thereby reducing device complexity while maintaining or improving measurement precision.
2Adaptability or versatility
If a configurable filter time constant is used to adapt to different transition speeds, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamics by making the filter time constant configurable and adaptable to different application requirements. The system can dynamically adjust the filtering characteristics based on the expected transition speed of the input signal, allowing optimal performance across various operating conditions without requiring multiple fixed-filter designs.
Solution Approach 2:
The patent achieves universality by designing a single filtering algorithm that can handle multiple types of digital inputs (slow transitions, fast transitions, noisy signals, clean signals) through parameter configuration. The same core exponential moving average algorithm serves multiple filtering purposes by adjusting the time constant and hysteresis parameters, eliminating the need for separate filtering circuits for different signal types.
3Reliability
If hysteresis is applied to ignore samples during signal transitions, then reliability is improved, but measurement precision may worsen
Solution Approach 1:
The patent applies preliminary anti-action by using hysteresis to preemptively prevent false edge detections during signal transitions. The hysteresis mechanism creates a dead zone around the transition threshold that temporarily ignores samples, preventing the system from reacting to transient noise or incomplete transitions. This preliminary protection ensures that only stable, genuine transitions are detected as events.
Solution Approach 2:
The patent applies partial action by implementing hysteresis only during the critical transition period when the signal is near the threshold. Once the signal stabilizes beyond the hysteresis band, normal edge detection resumes with full precision. This selective application of hysteresis provides reliability improvement during vulnerable transition periods without unnecessarily degrading measurement precision during stable states.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the ability to determine the root cause of incidents with high accuracy, reduces CPU overhead, and allows for continuous monitoring and diagnostics, improving the detection of real events and reducing false reporting, thereby supporting more effective corrective actions and long-term improvements.
Implementation Method 1
The exponential moving average is calculated using the formula: ema[i] = (1 - alpha) * ema[i - 1] + alpha * input[i]
Implementation Method 2
The goal is to apply a timestamp at which sample the transition actually occurs, while preventing false events being reported when the sample data is influenced by internal or external factors
Data Source
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.