Sensor Data Event Detection for Hardware Modification
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Solution Overview
Problem
Existing hardware devices lack an efficient method to analyze and respond to significant events in sensor data, leading to suboptimal performance and maintenance.
Innovation Solution
A processor-implemented method that standardizes and analyzes time series data from sensors to identify significant events, generates a modified time series, and adjusts the hardware device based on this analysis, using techniques like de-trending, decay parameter determination, and smoothing to reduce noise and isolate critical events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the hardware device continuously monitors and responds to all sensor events, then the device can detect all potential issues, but the system becomes overwhelmed by noise and less effective at identifying significant events
Solution Approach 1:
The system applies de-trending to remove long-term trends from sensor data, transforming the data to highlight deviations from normal behavior. This parameter transformation enables the system to distinguish significant events from noise by changing the statistical parameters of the time series data.
Solution Approach 2:
The system uses a decay parameter to selectively process only the most recent and relevant portions of sensor data, rather than analyzing all historical data equally. This partial action approach focuses computational resources on the most significant events while ignoring outdated or less relevant information.
2Reliability
If the system analyzes detailed time series data to identify significant events, then event identification improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs de-trending and smoothing operations in advance to pre-process the time series data before event analysis. This preliminary action reduces the computational burden during actual event detection by working with simplified data representations.
Solution Approach 2:
The system extracts and removes the trend component from the time series data through de-trending, separating the signal of interest from the background noise. This extraction allows the system to focus computational resources only on analyzing the residual variations that indicate significant events.
3Productivity
If the hardware device modifies itself based on sensor data analysis, then performance improvement is achieved, but the complexity of the device increases
Solution Approach 1:
The system implements a feedback loop where sensor data is continuously analyzed and used to trigger modifications to the hardware device. This feedback mechanism enables the device to automatically adjust its operation based on real-time performance data, improving productivity without requiring external intervention.
Solution Approach 2:
The hardware device performs self-diagnosis and self-modification based on its own sensor readings. The device analyzes its performance data and autonomously determines what modifications are needed, eliminating the need for external monitoring and control systems.
Data Source
AI summary
A method, system, and/or computer program product modify a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant by comparing means and trends of time sub-series corresponding to the time ranges before, during, and after each event, and then generate a modified time series of data by reducing a number of significant events described by the time series of data, which is used to modify the hardware device.