Engine Wash Selection Using Maintenance History Analytics
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Solution Overview
Problem
Existing engine wash systems lack an efficient method to determine the optimal type of maintenance operation for engines based on their specific deterioration levels and environmental conditions, leading to suboptimal performance and increased costs.
Innovation Solution
A system and method that utilize processors to receive engine history data, compare it to expected data, determine the expected effectiveness of different engine wash types, and select the most appropriate wash type based on the comparison, including factors like cleaning medium, delivery method, and duration, to optimize maintenance operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If different types of engine washes are performed regularly, then engine performance is improved and engine life is extended, but maintenance costs increase
Solution Approach 1:
The system changes parameters of the maintenance operation by selecting different wash types (e.g., water wash, chemical wash, steam wash) based on engine deterioration level, environmental conditions, and operational history. This allows optimization of maintenance effectiveness while reducing unnecessary maintenance costs by avoiding overly aggressive washes when not needed.
Solution Approach 2:
The system enables self-service by using sensors and processors to automatically monitor engine conditions, determine wash needs, and select appropriate wash types without requiring constant human intervention or judgment, thereby optimizing maintenance decisions independently.
2Productivity
If comprehensive engine monitoring and analysis systems are implemented, then maintenance operation selection is optimized, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by combining sensor data acquisition, historical data storage, deterioration analysis, wash type determination, and notification functions into a single integrated platform. This universal system handles multiple maintenance decision-making tasks without requiring separate specialized systems for each function.
Solution Approach 2:
The processor acts as an intermediary between the sensor data/historical data and the maintenance decision-making process. It analyzes the data, determines engine deterioration levels, and selects appropriate wash types, thereby simplifying the overall system architecture by centralizing the analytical function in a single computational component.
Data Source
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AI summary
One example aspect of the present disclosure is directed to a method 500 for enhancing a maintenance operation routine. The method 500 includes receiving (502), at one or more processors, engine history data. The method 500 includes comparing 504, at the one or more processors, the received engine history data to expected engine history data. The method 500 includes determining 506, at the one or more processors, an expected effectiveness of a plurality of maintenance operation types based on the comparison. The method 500 includes selecting 508, at the one or more processors, one of the plurality of maintenance operation types based on the determinations. The method 500 includes transmitting 510, at the one or more processors, a signal indicative of a notification of the selected maintenance operation type.