Machine Usage Severity Scoring for Telematics-Based Maintenance
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Solution Overview
Problem
Existing machine maintenance systems fail to accurately account for usage severity when recommending maintenance and overhauls, as they do not consider the varying wear and tear caused by different applications, leading to inefficient servicing and potential premature or delayed maintenance.
Innovation Solution
A method and system that calculate a machine usage severity score by collecting repair data and telematics data from sensors, using predictive features such as average fuel rate and ground speed to create a severity model, which recommends maintenance when the score exceeds a threshold, thereby accounting for usage severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If condition-based maintenance or age-based maintenance schedules are used, then maintenance can be performed based on machine age or basic operational data, but the varying wear and tear caused by different applications is not accounted for
Solution Approach 1:
The patent transforms basic operational parameters (fuel rate, ground speed, work time) into a composite severity score that reflects actual wear and tear. By changing the parameters used for maintenance scheduling from simple age or utilization metrics to a multi-factor severity index, the system accurately captures the impact of different applications on machine wear, resolving the contradiction between ease of maintenance scheduling and precision of usage assessment
Solution Approach 2:
The severity score is constructed as a composite metric combining multiple telematics parameters (fuel rate, ground speed, work time) to create a holistic measure of usage severity. This composite approach allows the system to account for varying wear and tear across different applications while maintaining a unified maintenance scheduling framework, thereby achieving both ease of implementation and measurement precision
2Productivity
If traditional maintenance schedules based on age and utilization rate are used, then maintenance planning is simplified, but machines subject to severe usage may not receive timely service
Solution Approach 1:
The system continuously monitors telematics data and updates the severity score in real-time, providing feedback on actual machine wear and tear. This feedback mechanism allows maintenance schedules to be dynamically adjusted based on actual usage patterns rather than static age-based criteria, ensuring machines receive timely service when severity thresholds are exceeded while maintaining overall maintenance efficiency
Solution Approach 2:
The maintenance scheduling system transitions from static age-based intervals to dynamic severity-triggered scheduling. By making the maintenance trigger dependent on real-time severity scores derived from actual operational conditions, the system adapts to varying usage patterns, ensuring reliable machine condition assessment while maintaining productivity through optimized maintenance timing
3Device complexity
If application type is not considered in maintenance schedules, then the maintenance system is simpler to implement, but wear and tear variations across different tasks are ignored
Solution Approach 1:
The severity score model serves as a universal metric that can evaluate wear and tear across multiple different applications and machine types. By creating a multi-functional assessment tool that processes various telematics parameters into a single severity index, the system achieves precise wear measurement across diverse tasks without requiring separate complex evaluation systems for each application
Data Source
AI summary
A method for determining machine usage severity including collecting repair data for multiple machines over a repair time period and telematics data from sensors on the machines over an activity time period. The method can include calculating predictive features from the telematics data for each of the machines and creating a severity model based on the predictive features. The severity model can be validated with the predictive features and corresponding repair data for each of the machines. The method can include receiving telematics data from sensors on a deployed machine for a deployed period of time, calculating a plurality of machine predictive features from the telematics data, and feeding the machine predictive features into the severity model to calculate a severity score for the deployed machine. The method can include displaying a recommendation to perform maintenance on the deployed machine when the machine usage severity score exceeds a selected threshold.