Lubricating Oil Volume Control Using Motor Current and Temperature Prediction
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Solution Overview
Problem
Conventional lubricating oil supply systems in machines like crankshaft stamping presses often supply excessive oil due to fixed volume settings, leading to waste and inefficiency, as they are not dynamically adjusted based on real-time machine conditions such as temperature and motor operation.
Innovation Solution
A lubricating oil volume adjustment system that uses a machine learning model, trained with data from a machine's operation and temperature values, to predict temperature and calculate the optimal lubricating oil volume needed, which is then adjusted in real-time through a processor and data acquisition device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed volume of lubricating oil is supplied at a fixed time, then the system is simple to operate, but the lubricating oil volume does not adapt to changing machine conditions, leading to excessive oil supply and waste
Solution Approach 1:
The lubricating oil supply system transitions from a fixed, static volume supply to a dynamic supply that adjusts oil volume in real-time based on machine operating conditions. The system uses a processor to receive real-time temperature data from sensors and automatically adjusts the lubricating oil pump's output, enabling the oil supply to adapt dynamically to changing machine conditions and preventing both excessive and insufficient lubrication
Solution Approach 2:
The system implements a closed-loop feedback mechanism where temperature sensors continuously monitor machine component temperatures and feed this data to a processor. The processor analyzes the temperature information and adjusts the lubricating oil supply volume accordingly, creating a self-regulating system that responds to actual machine needs and eliminates waste from excessive oil supply
2Reliability
If the automatic lubrication system adopts the most stringent processing conditions for lubricating oil supply, then the machine is well-lubricated under all conditions, but excessive lubricating oil is supplied most of the time, causing waste and recycling problems
Solution Approach 1:
The system dynamically changes the lubricating oil supply volume parameter based on real-time machine operating conditions, particularly temperature. Instead of maintaining a constant high supply volume that ensures lubrication under all conditions, the system adjusts the oil volume parameter upward when temperatures indicate high stress conditions and reduces it when conditions are normal, thereby maintaining reliable lubrication while minimizing oil waste
Solution Approach 2:
The system applies partial action by supplying lubricating oil only in the amounts needed for current operating conditions rather than continuously supplying excessive oil. The processor evaluates real-time temperature data and determines the precise oil volume required, supplying only that amount rather than using a conservative over-design approach that would ensure adequate lubrication but result in constant excess oil supply and waste
Data Source
AI summary
A lubricating oil volume adjustment system and a lubricating oil volume adjustment method are provided. The system includes a storage device and a processor and is connected to a machine including a motor through a data acquisition device acquiring current information of the motor. The storage device stores a machine learning model trained by a training data set including a plurality of pieces of the current information of the motor during operation and a plurality of temperature values measured during operation of the machine. The processor is configured to acquire the current information of present operation of the motor by using the data acquisition device, predict a temperature value of the machine when the motor operates under the current information by using the machine learning model, and calculate and adjust a lubricating oil volume suitable to be used by the machine during operation according to the predicted temperature value.