Machine Component Life Risk Prediction for Proactive Maintenance
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Solution Overview
Problem
Existing systems lack effective methods to predict the risk of machine components not achieving their agreed life, leading to premature replacement or rebuilding, which can result in significant commercial risks and costs due to unplanned downtime.
Innovation Solution
A system and method that utilize processors and memories to receive and process component-specific time series data, generate model inputs, and apply them to a time-series model to predict the life of machine components, determining a risk score based on the agreed life and predicted life, thereby alerting maintenance entities for proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If component condition monitoring is performed to avoid unplanned downtime, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary risk prediction by analyzing historical component data and operational parameters before actual failure occurs. The risk prediction model calculates probability scores in advance, allowing proactive maintenance scheduling that prevents unplanned downtime without requiring complex real-time monitoring infrastructure.
Solution Approach 2:
The patent replaces complex mechanical condition monitoring systems with a data-driven risk prediction model that uses historical records and operational parameters. Instead of deploying sophisticated sensors and real-time analysis infrastructure, the system substitutes a computational model that processes available data to generate risk assessments, thereby improving reliability while avoiding excessive device complexity.
2Loss of time
If risk prediction models are implemented to prevent premature replacement, then loss of time is reduced, but manufacturing precision is required
Solution Approach 1:
The system applies partial action by using a subset of available data (historical component data and operational parameters) rather than requiring complete precision from all possible data sources. The risk prediction model accepts approximate inputs and generates sufficiently accurate predictions to prevent premature replacement, avoiding the need for excessive manufacturing precision in data collection and processing.
Solution Approach 2:
The patent transforms the problem from requiring precise manufacturing measurements to utilizing operational parameter variations. The risk prediction model analyzes changes in operational parameters over time to infer component health status and replacement risk, thereby reducing time loss without demanding high manufacturing precision in the input data.
3Measurement precision
If historical data is collected and processed to generate predictions, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system extracts only the most relevant features and patterns from historical component data and operational parameters, rather than processing and storing all raw data. The risk prediction model identifies and utilizes key predictive indicators while discarding redundant information, thereby achieving precise life predictions without excessive data processing losses.
Solution Approach 2:
The patent performs preliminary data processing and feature extraction during the model training phase, preparing refined inputs before actual prediction. This preliminary action organizes historical data into meaningful patterns and relationships that the risk prediction model can efficiently utilize, improving measurement precision while minimizing information loss during operational predictions.
Data Source
AI summary
The present disclosure is directed to systems and methods for predicting risk of machine components not achieving their agreed life based on historical data. In some implementations, the predicting component risk system can obtain historical component specific time series data and component life data that correspond to a specific component of a type of machine. Using the component specific time series data and component life data, the predicting component risk system can train a time-series model. After training the model, the predicting component risk system can obtain current component specific data that includes information on the current condition and usage of the component. Using the trained time-series model, the predicting component risk system can determine a risk score for the current component specific data and notify maintenance entities of the risk. The risk score can represent the risk that the specific component does not achieve agreed life.