Elevator Configuration Prediction for Faster Fault Resolution
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Solution Overview
Problem
Conveyance systems, such as elevator systems, face challenges in efficient problem resolution due to the need for extensive analysis and site visits to update configuration parameters, often without access to proven configuration settings.
Innovation Solution
A predictive system is introduced to receive problem descriptions and current configuration parameters, determine updated configuration parameters to address the issues, and send these updates to user devices, along with impact analyses and reasons for the problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mechanic performs in-depth analysis and multiple site visits to update configuration parameters, then the problem resolution accuracy is improved, but the time required increases significantly
Solution Approach 1:
The system performs preliminary analysis by collecting and analyzing historical service records, customer complaints, and engineering specifications before the mechanic arrives at the site. This pre-processing of information reduces the need for multiple site visits and accelerates the problem resolution process while maintaining accuracy.
Solution Approach 2:
An AI-powered predictive system acts as an intermediary between the mechanic and the conveyance system. This intermediary analyzes configuration parameters, service records, and problem descriptions to provide recommended updates, reducing the mechanic's workload and enabling faster decision-making without sacrificing resolution accuracy.
2Reliability
If multiple site visits are conducted to test different configuration parameters, then the effectiveness of parameter updates is improved, but the productivity decreases
Solution Approach 1:
The system implements a feedback mechanism where configuration parameter updates are tracked and their effectiveness is monitored. Service records and customer complaints serve as feedback data that is continuously analyzed to refine future recommendations, enabling the system to learn from past interventions and improve effectiveness over time without requiring repeated site visits.
Solution Approach 2:
The predictive system performs preliminary simulation and analysis of configuration parameter changes before implementation. By evaluating potential updates against historical data and engineering specifications in advance, the system identifies the most effective parameter changes upfront, reducing the need for iterative testing during site visits and thereby improving productivity.
3Ease of operation
If proven configuration parameters are not available to the mechanic, then the ease of operation is improved (mechanic can work independently), but the problem resolution quality deteriorates
Solution Approach 1:
The predictive system enables self-service by automatically analyzing problem descriptions and current configuration parameters to generate recommended updates. This eliminates the need for mechanics to manually search for proven parameters or rely on external expertise, maintaining ease of operation while improving problem resolution quality through AI-driven analysis of service records, customer complaints, and engineering specifications.
Solution Approach 2:
The AI-powered predictive system serves as an intermediary knowledge base that provides mechanics with access to proven configuration parameters and expert guidance remotely. This intermediary delivers data-driven recommendations based on analyzed service records and engineering specifications, enabling mechanics to work independently with the same quality of insight previously available only through experienced experts.
Data Source
AI summary
A method of providing assisted problem resolution of a conveyance system, the method including receiving, at a predictive system, a problem description of a problem at the conveyance system and current configuration parameters of the conveyance system; at the predictive system, determining at least one set of updated configuration parameters to address the problem; and sending, from the predictive system to a user device, a response including the at least one set of updated configuration parameters.