Ignitor Remaining-Life Prediction from Engine Sensor Data
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Solution Overview
Problem
Existing spark plug degradation prediction methods in spark-ignited engines are inaccurate due to varying degradation rates caused by manufacturing differences and engine conditions, leading to premature replacement or failure.
Innovation Solution
A method and system for determining the remaining useful life (RUL) of spark plugs by processing sensor data, aggregating relevant information, and using models to predict degradation based on engine duty cycles and operating conditions, adjusting for service events and noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spark plug replacement is based on engine run time, then maintenance scheduling is simplified, but premature replacement of healthy spark plugs occurs
Solution Approach 1:
The patent replaces the mechanical/time-based maintenance scheduling system with an electronic monitoring and prediction system. Sensors collect electrical parameters (voltage, current, power) from the spark plug ignition system, and a processing system analyzes this data to predict remaining useful life, substituting the simple time-based replacement approach with an intelligent prediction-based approach.
Solution Approach 2:
The system implements continuous feedback by monitoring electrical parameters from the spark plug in real-time during engine operation. The processing system uses this feedback to update the degradation model and adjust the remaining useful life prediction, allowing dynamic adaptation to actual spark plug condition rather than relying on fixed time intervals.
2Loss of energy
If spark plug replacement is delayed to extend service life, then maintenance costs are reduced, but unplanned downtime increases
Solution Approach 1:
The system performs preliminary action by predicting the remaining useful life of the spark plug before actual failure occurs. The processing system analyzes electrical parameter trends and projects future degradation, providing advance warning that allows planned maintenance scheduling, thereby avoiding both premature replacement and unexpected failure.
3Measurement precision
If individual spark plug degradation is monitored, then replacement timing accuracy is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by using a multi-functional processing system that performs multiple tasks: collecting electrical parameters from multiple cylinders, filtering and preprocessing data, running degradation models, predicting remaining useful life for multiple spark plugs, and generating maintenance recommendations. This single system handles all monitoring and prediction functions rather than requiring separate systems for each function.
Data Source
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AI summary
Systems and methods for determining a remaining useful life (RUL) of a component of an engine system are provided. A method includes: receiving component data comprising sensor data corresponding to a component and engine data; pre-processing the component data by removing a first set of sensor data values that correspond to a first set of engine data values of the engine data; aggregating the pre-processed component data by grouping a second set of sensor values of the sensor data; determining a RUL for the component based on a RUL model that correlates at least a portion of the aggregated data to RUL values; adjusting the RUL based on at least one of detecting a service event or determining that one or more sensor data values of the sensor data are outside a predetermined range of values; and providing the RUL to a user device.