ML-Based Equipment Component Impact Remediation
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Solution Overview
Problem
Recurring technical issues in refurbished equipment resold to new customers often go undetected, leading to multiple repair or replacement cycles, resulting in significant costs and inconvenience for OEMs and customers.
Innovation Solution
A machine learning-based approach using a relevance tree processing engine identifies and proactively remedies impacted components in equipment, constructing a data structure that represents the order of components affected by an issue-reported component, and generates a remedial plan before resale or redeployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive support is used where issues are addressed only after customer reporting, then support team response is straightforward, but recurring issues in refurbished equipment lead to multiple repair cycles and increased costs
Solution Approach 1:
The system performs preliminary identification of potentially impacted components before they fail, using machine learning models to analyze equipment data and predict which components may be affected by reported issues. This allows proactive remediation to be scheduled before actual failures occur, preventing recurring repair cycles in refurbished equipment.
Solution Approach 2:
The system implements a feedback loop where equipment data is continuously collected, analyzed by machine learning models, and used to generate remediation plans. The outcomes of these remediation actions are fed back into the system to improve future predictions and identification accuracy, creating a continuous improvement cycle for equipment reliability.
2Reliability
If proactive identification of impacted components is implemented using machine learning, then future repair requests are reduced, but system complexity and computational resources increase
Solution Approach 1:
The system segments the equipment into individual components and creates separate machine learning models or analysis modules for different component types. This modular approach allows the complex analysis to be divided into manageable segments, reducing overall system complexity while maintaining comprehensive coverage of all components.
Solution Approach 2:
The patent introduces an intermediary processing layer that sits between raw equipment data and the machine learning models. This intermediary layer performs data preprocessing, feature extraction, and filtering, which simplifies the input data and reduces the computational complexity required by the underlying machine learning algorithms.
3Reliability
If comprehensive remediation plans are generated for all potentially impacted components, then equipment reliability improves, but time and resources for remediation increase
Solution Approach 1:
The system applies local quality by prioritizing remediation efforts based on the predicted impact and likelihood of failure for each component. Instead of treating all components uniformly, the machine learning models identify which specific components require immediate attention versus those that can be monitored or addressed later, optimizing the allocation of remediation resources.
Solution Approach 2:
The system implements partial action by generating remediation plans for only the most critical impacted components rather than all potentially affected components. The machine learning models prioritize components based on risk assessment, allowing the system to achieve sufficient reliability improvement with reduced remediation time and resources by focusing on the most significant risks.
Data Source
AI summary
Techniques are disclosed for equipment support management comprising proactive identification and remediation for one or more impacted components of the equipment using a machine learning-based approach. By way of one example, a method identifies, for given equipment comprising a plurality of components, one or more components of the plurality of components that may be impacted by another component of the plurality of components for which an issue has been reported, wherein one or more machine learning-based algorithms are used to perform at least a portion of the identification. The method then generates, for the given equipment, a plan to proactively remedy the one or more identified components in conjunction with remedying the issue-reported component. In some further examples, a relevance tree is used to identify the one or more impacted components.