Boe technology group co., ltd. (20240185115). METHOD AND APPARATUS FOR EARLY WARNING OF DRY PUMP SHUTDOWN, ELECTRONIC DEVICE, STORAGE MEDIUM AND PROGRAM simplified abstract

METHOD AND APPARATUS FOR EARLY WARNING OF DRY PUMP SHUTDOWN, ELECTRONIC DEVICE, STORAGE MEDIUM AND PROGRAM

Organization Name

boe technology group co., ltd.

Inventor(s)

Qing Zhang of Beijing (CN)

Quanguo Zhou of Beijing (CN)

Lijia Zhou of Beijing (CN)

Jiuyang Cheng of Beijing (CN)

Zhidong Wang of Beijing (CN)

Ruwang Guo of Beijing (CN)

Lirong Xu of Beijing (CN)

Junrui Zhang of Beijing (CN)

Xuehui Zhu of Beijing (CN)

Meng Guo of Beijing (CN)

METHOD AND APPARATUS FOR EARLY WARNING OF DRY PUMP SHUTDOWN, ELECTRONIC DEVICE, STORAGE MEDIUM AND PROGRAM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240185115 titled 'METHOD AND APPARATUS FOR EARLY WARNING OF DRY PUMP SHUTDOWN, ELECTRONIC DEVICE, STORAGE MEDIUM AND PROGRAM

Simplified Explanation

The disclosure provides a method and apparatus for early warning of dry pump shutdown, an electronic device, a storage medium and a program, and belongs to the technical field of automatic control. The method comprises: obtaining historical operating data of a dry pump; building a Kalman filter model by using the historical operating data; predicting predicted operating data of the dry pump through the Kalman filter model; training a shutdown prediction model by using the historical operating data and the predicted operating data; and inputting current operating data of the dry pump into the trained shutdown prediction model to obtain shutdown early warning information of the dry pump.

• Obtaining historical operating data of a dry pump
• Building a Kalman filter model using the historical operating data
• Predicting future operating data of the dry pump through the Kalman filter model
• Training a shutdown prediction model using historical and predicted data
• Inputting current operating data into the trained model for early warning of shutdown

Potential Applications

The technology can be applied in industrial settings where dry pumps are used, such as semiconductor manufacturing, chemical processing, and vacuum systems.

Problems Solved

This technology addresses the issue of unexpected dry pump shutdowns, which can lead to production delays, equipment damage, and increased maintenance costs.

Benefits

The early warning system allows for proactive maintenance and troubleshooting of dry pumps, reducing downtime and improving overall efficiency in industrial operations.

Potential Commercial Applications

Potential commercial applications include selling the early warning system as a standalone product or integrating it into existing dry pump systems for enhanced monitoring and control.

Possible Prior Art

Prior art may include similar predictive maintenance systems for industrial equipment, but the specific application to dry pump shutdown prediction may be novel.

How does the Kalman filter model improve the accuracy of shutdown predictions compared to other methods?

The Kalman filter model is known for its ability to estimate the state of a system based on noisy measurements, but it is not clear how this specifically benefits the early warning system for dry pump shutdowns.

What are the limitations of the shutdown prediction model in terms of accuracy and reliability?

While the technology provides early warning information, it is important to understand the potential limitations of the shutdown prediction model in real-world scenarios, such as varying operating conditions and system complexities.

Original Abstract Submitted

the disclosure provides a method and apparatus for early warning of dry pump shutdown, an electronic device, a storage medium and a program, and belongs to the technical field of automatic control. the method comprises: obtaining historical operating data of a dry pump; building a kalman filter model by using the historical operating data; predicting predicted operating data of the dry pump through the kalman filter model; training a shutdown prediction model by using the historical operating data and the predicted operating data; and inputting current operating data of the dry pump into the trained shutdown prediction model to obtain shutdown early warning information of the dry pump.