Predictive Maintenance System for Work Vehicle Alert Sequences
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Solution Overview
Problem
Current maintenance schedules for work vehicles often lack predictive capabilities, leading to unexpected machine failures and increased downtime, as they rely solely on routine maintenance rather than data-driven predictive analysis.
Innovation Solution
A method and system that access and compare alert sequences from work vehicles to a predictive model, identifying substantial similarities between observed diagnostic codes, sensor data, and vehicle status data to predict machine failures and determine the probable parts required for repair, using a centralized data processing center that updates in real-time with historical data from a large population of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routine maintenance schedules are used based on fixed intervals, then maintenance can be performed regularly, but unexpected machine failures still occur and downtime increases
Solution Approach 1:
The system performs preliminary actions by analyzing alert sequences and diagnostic data to predict machine failures before they occur. The predictive maintenance system identifies patterns in sensor data and diagnostic codes that precede failures, allowing maintenance to be scheduled in advance rather than reacting to actual failures, thus reducing unexpected downtime and improving reliability.
2Reliability
If predictive maintenance is implemented using data analysis, then machine failures can be predicted and downtime reduced, but system complexity increases
Solution Approach 1:
The predictive maintenance system is designed to be universal and multi-functional, handling multiple vehicle types, sensor data formats, and diagnostic code standards through a single platform. The system can process various alert sequences and diagnostic data from different sources using common analysis algorithms, reducing the need for separate specialized systems for each vehicle type or data format, thereby managing complexity while maintaining high prediction accuracy.
3Measurement precision
If real-time data monitoring is implemented across a vehicle fleet, then predictive accuracy improves, but data processing requirements and computational load increase
Solution Approach 1:
The system segments the fleet into groups based on vehicle type, usage patterns, and failure modes, processing data for each segment separately. This segmentation allows the system to focus computational resources on relevant data patterns for each group rather than processing all fleet data uniformly, reducing overall computational energy requirements while maintaining high predictive accuracy through targeted analysis of segment-specific alert sequences and diagnostic data.
Data Source
AI summary
Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example apparatus includes a text miner to text mine first alert data to identify a first machine failure within the first alert data; a failure alert sequence identifier to identify a first alert sequence associated with the first machine failure; a conditional probability determiner to determine a conditional probability of the first alert sequence leading to failure based on i) a number of work machines in which the first alert sequence ended in failure and ii) a number of work machines in which the first alert sequence did not end in failure; and a collator to update a model by correlating the first alert sequence with the first machine failure and first probable parts used to repair the first machine failure.