E-Bike Decision-Tree Diagnosis for Faster Fault Isolation
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Solution Overview
Problem
Existing methods for diagnosing electric bikes are inefficient and lack a systematic approach to optimize diagnostic processes, leading to suboptimal fault detection and resolution.
Innovation Solution
A computer-implemented method using a decision tree with adaptable decision-making processes, incorporating fault and symptom notifications, actuations, and diagnostic signal detection, optimized by machine learning and feedback loops, to enhance diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional diagnostic methods are used for electric bikes, then the diagnostic process can be performed, but the efficiency is low and the process lacks systematization
Solution Approach 1:
The diagnostic process is segmented into discrete decision tree nodes, each representing a specific diagnostic step or question. This breaks down the complex diagnostic process into manageable, systematic segments that can be efficiently processed and evaluated.
Solution Approach 2:
The decision tree structure allows the diagnostic process to dynamically adapt based on previous diagnostic outcomes. The system can branch into different diagnostic paths depending on the symptoms detected, optimizing the diagnostic efficiency by avoiding unnecessary steps while maintaining systematic coverage.
2Measurement precision
If a comprehensive diagnostic approach is implemented, then fault detection accuracy improves, but the time and effort required increases
Solution Approach 1:
The decision tree is pre-structured with the most informative and high-yield diagnostic steps positioned early in the process. Common faults are addressed first through preliminary diagnostic questions that quickly eliminate obvious causes, allowing the system to achieve high accuracy without requiring exhaustive examination of all possible faults.
Solution Approach 2:
The diagnostic system incorporates feedback loops where each diagnostic outcome informs the next step. The system continuously evaluates diagnostic signals and adjusts the diagnostic path based on previous results, optimizing the balance between thoroughness and time efficiency by focusing subsequent steps on the most likely remaining causes.
Data Source
AI summary
A method for diagnosing a bike, in particular an electric bike, by way of a decision tree includes (i) providing a fault notification or a symptom notification, (ii) determining a diagnostic method step for the bike based on the fault notification or the symptom notification, (iii) actuating the bike based on the diagnostic method step determined, (iv) detecting a diagnostic signal based on the actuated bike, and (v) determining a further diagnostic method step, a fault, or a fault underlying a symptom based on the diagnostic signal detected.