Data Analyzer for Fuel Injection Signal Noise Removal
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Solution Overview
Problem
Existing techniques for analyzing time-series data from sensor detection signals face challenges in accurately detecting characteristic points due to noise, which can lead to mis-detection and inappropriate analysis, especially in fuel injection data where filters like low-pass filters can blur waveforms and shift timing.
Innovation Solution
A data analyzer configuration that includes a data obtainer, differentiator, moving averager, identifier, and data characterizer, which differentiates and calculates the moving average of the time-series data to remove noise, allowing for the identification of waveforms and accurate detection of characteristic points like injection start and end timings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a filter such as a low-pass filter is used to remove noise from time-series data, then noise removal is improved, but the waveform becomes blunt and characteristic point timing shifts causing mis-detection
Solution Approach 1:
The patent segments the noise removal process into two distinct stages: first applying a low-pass filter to remove noise from the time-series data, then separately detecting characteristic points from the filtered data. This segmentation allows each stage to be optimized independently, mitigating the waveform blurring effect on characteristic point detection
Solution Approach 2:
The patent introduces an intermediary processing step where the filtered time-series data is further processed through differentiation and moving average calculations before characteristic point detection. This intermediary process acts as a mediator that compensates for waveform blurring by emphasizing rate of change information, thereby restoring characteristic point timing accuracy
2Measurement precision
If no filter is used on the detection signal, then the waveform and timing are preserved accurately, but noise remains causing mis-detection of characteristic points
Solution Approach 1:
The patent applies preliminary noise removal action through low-pass filtering before the characteristic point detection process. By removing noise in advance, the detection algorithm operates on cleaner data, improving the reliability of characteristic point identification while the subsequent differentiation and moving average steps preserve timing accuracy
Data Source
AI summary
A data analyzer for analyzing characterization data to form fuel injection control, including a data obtainer that obtains data from a detection signal of a sensor as a time-series data that changes over time. The data analyzer further includes a differentiator that differentiates the time-series data obtained by the data obtainer, a moving averager that calculates a moving average of the differentiated time-series data by the differentiator, an identifier that identifies a waveform of the time-series data based on the moving average calculated by the moving averager, and a data characterizer characterizes the time-series data based on the waveform of the time-series data identified by the identifier. As such, noise is removed as much as possible, and characteristics of time-series data become analyzable.