Trailer Fault Code Evaluation for Early Failure Detection
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Solution Overview
Problem
Existing telematics data sets and fault codes for utility vehicle trailers only allow for inferences about the state of the trailer, limiting effective countermeasures to trailer faults.
Innovation Solution
A method and system that utilize servers to obtain, evaluate, and generate user information from multiple fault codes, enabling early detection of increased probabilities of undesired behaviors in utility vehicle trailers through statistical analysis and digital representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If fault codes are transmitted at a certain frequency, then data transmission is achieved, but only inferences about trailer state can be made and countermeasures can only be taken after faults occur
Solution Approach 1:
The system performs preliminary evaluation of fault codes before actual trailer failures occur. By analyzing fault code patterns, frequencies, and combinations in advance, the system can identify potential failures and alert users proactively, enabling countermeasures to be taken before the actual fault manifests in the trailer operation.
Solution Approach 2:
The system establishes a feedback loop where fault codes from multiple trailers are collected, evaluated, and used to generate information that feeds back into the monitoring system. This feedback mechanism enables continuous improvement of fault detection accuracy and proactive identification of patterns that indicate impending failures.
2Reliability
If statistical evaluation of fault codes is performed, then early detection of failure probabilities is enabled, but system complexity increases
Solution Approach 1:
The system segments the evaluation process into distinct functional components: fault code collection, statistical evaluation, pattern recognition, and information generation. Each component handles specific aspects of the analysis independently, making the overall complex system more manageable and maintainable while enabling sophisticated fault detection capabilities.
Solution Approach 2:
The server acts as an intermediary between the telematics units and the user. It collects fault codes from multiple trailers, performs statistical evaluation, and generates meaningful information that users can act on. This intermediary function consolidates complexity in a centralized system that processes and translates raw data into actionable insights.
3Measurement precision
If multiple fault codes from multiple trailers are evaluated, then statistical anomalies can be detected, but data processing requirements increase
Solution Approach 1:
The system merges fault code data from multiple trailers into a unified evaluation process. By combining and analyzing fault codes across the entire fleet rather than processing each trailer independently, the system achieves higher statistical precision in anomaly detection while efficiently utilizing aggregate data patterns.
Solution Approach 2:
The system changes the parameters of analysis by evaluating fault codes based on their frequency, temporal patterns, and relationships with trailer characteristics. This parameter transformation enables statistical anomalies to be detected more effectively by analyzing fault codes in different dimensional spaces, improving detection precision without proportionally increasing processing requirements.
Data Source
AI summary
A method includes obtaining a plurality of fault codes for a plurality of utility vehicle trailer. Each of the fault codes represents an undesired behavior of a respective utility vehicle trailer of the plurality of utility vehicle trailers or one or more components of the respective utility vehicle trailer of the plurality of utility vehicle trailers. The method includes evaluating the obtained fault codes and based on the evaluation, generating user information.