Maintenance Server Prediction Model for Apparatus Abnormality Detection
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Solution Overview
Problem
Complex multifunctional apparatuses pose challenges in predicting and managing abnormalities or failures, leading to increased maintenance costs and downtime, as existing systems struggle to accurately anticipate and prepare for potential issues before they occur.
Innovation Solution
A maintenance system comprising apparatuses that collect log data and transmit it to a maintenance server, which uses machine learning to create a prediction model that identifies potential abnormalities or failures, allowing for proactive maintenance instructions to be issued before actual failures happen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a customer engineer visits the customer to handle abnormality or failure, then the abnormality can be resolved, but maintenance cost increases and downtime occurs
Solution Approach 1:
The system performs preliminary actions by predicting abnormalities before they occur into the future. The prediction model analyzes log data to forecast potential failures, enabling maintenance to be scheduled in advance before the apparatus actually fails, thus avoiding unplanned downtime and emergency service calls
Solution Approach 2:
The system implements feedback by continuously collecting log data from the apparatus, analyzing it through the prediction model, and using the results to issue maintenance instructions. This closed-loop feedback mechanism enables proactive identification and resolution of potential issues before they cause failures
2Reliability
If urgent maintenance work is performed, then apparatus availability is restored, but service cost increases
Solution Approach 1:
The system performs preliminary maintenance actions by predicting abnormalities before they occur. The prediction model identifies potential failures in advance, allowing maintenance to be scheduled during convenient times rather than requiring urgent emergency service calls, thus reducing service costs while maintaining apparatus availability
3Adaptability or versatility
If the apparatus is sophisticated and complicated, then functionality is improved, but difficulty in handling abnormality increases
Solution Approach 1:
The system implements self-service by enabling the apparatus to automatically monitor its own status through log data collection and self-diagnose potential issues through the prediction model. The apparatus can identify its own abnormalities and trigger maintenance instructions without requiring user expertise, thus maintaining high functionality while simplifying abnormality handling
Solution Approach 2:
The system introduces an intermediary maintenance server that acts as a mediator between the complex apparatus and the user. The server handles the complexity of analyzing log data and interpreting prediction results, presenting simplified maintenance information to the user without exposing the underlying system complexity
Data Source
AI summary
A maintenance system includes a plurality of apparatuses and a maintenance server. Each apparatus: transmits log data indicating a state of the apparatus to the maintenance server; receives a prediction model from the maintenance server, the prediction model predicting the occurrence of an abnormal state of the plurality of apparatuses; determines whether the abnormal state of the apparatus occurs based on the prediction model to generate a determination result; and transmits the determination result indicating the occurrence of the abnormal state of the apparatus to the maintenance server. The maintenance server: generates the prediction model based on the log data received from each of the plurality of apparatuses; and issues an instruction of a maintenance work for one or more of the plurality of apparatuses that transmit the determination result.