Livestock House Sensor Lifetime Prediction Using LSTM Error Trends
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Poor environments in livestock houses, such as gas and dust, lead to frequent failures and shortened lifetimes of sensors, making it difficult to collect environment data stably.
Innovation Solution
A method and apparatus using a time series-based long short-term memory (LSTM) cell to predict the remaining lifetime of environment data collection sensors by collecting and analyzing livestock house environment information, detecting errors, and generating a dataset for learning to preemptively address sensor malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are installed in livestock houses to monitor environment information, then real-time monitoring capability is improved, but sensor durability deteriorates due to poor environment (gas and dust)
Solution Approach 1:
The system performs preliminary actions by collecting environment information and detecting error trends before actual sensor failure occurs. The error detection unit continuously monitors sensor outputs and identifies degradation patterns, allowing the system to predict remaining lifetime and schedule maintenance proactively, preventing complete sensor failure and data loss.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring sensor performance through error detection and comparing it against expected patterns. The error detection unit provides feedback about sensor health status to the remaining lifetime prediction unit, which adjusts predictions based on observed error trends, creating a closed-loop system that adapts to actual sensor degradation.
2Productivity
If sensors operate continuously in poor environment, then monitoring coverage is improved, but data stability deteriorates due to frequent sensor failures
Solution Approach 1:
The system takes preliminary action by predicting sensor remaining lifetime before failure occurs. The remaining lifetime prediction unit calculates expected sensor lifespan based on collected environment data and detected errors, enabling proactive replacement scheduling that maintains continuous monitoring coverage while preventing data instability from sensor failures.
Solution Approach 2:
The system performs self-service by automatically detecting sensor errors and predicting remaining lifetime without external intervention. The error detection unit and remaining lifetime prediction unit work autonomously to monitor sensor health, identify degradation patterns, and determine optimal replacement timing, reducing the need for manual sensor maintenance while ensuring data stability.
3Reliability
If sensor replacement is performed frequently to ensure data stability, then data collection reliability is improved, but system complexity and maintenance cost increase
Solution Approach 1:
The system substitutes mechanical trial-and-error sensor replacement with an intelligent prediction system. Instead of replacing sensors based on fixed schedules or after failure, the remaining lifetime prediction unit uses machine learning models to calculate optimal replacement timing based on actual sensor degradation patterns, reducing unnecessary replacements and simplifying maintenance planning.
Solution Approach 2:
The system changes parameters by transitioning from fixed-time sensor replacement to condition-based replacement. The remaining lifetime prediction unit dynamically adjusts replacement timing based on predicted sensor degradation, environment conditions, and error trends, optimizing the replacement parameter to balance data stability with maintenance efficiency and reduce overall system complexity.
Data Source
AI summary
Provided is an apparatus and method for predicting the remaining lifetime of an environment data collection sensor in a livestock house. The method for predicting the remaining lifetime of an environment data collection sensor in a livestock house includes (a) collecting livestock house environment information, (b) detecting an error of a sensor from the collected livestock house environment information, (c) generating a dataset for learning, (d) generating a sensor lifetime prediction model to performing learning, and (e) predicting a lifetime of the sensor using a result of the learning.