Development Of Machine Learning User Interface For Pump Diagnostics - Lee, Zhao Yang (Universiti Sains Malaysia, 2022)
The main objectives for this project are focusing on the development of user interface that can connect with the machine learning build in Microsoft Azure for pump diagnostic purpose. Water pump is a very common hydraulic machine which will convert the rotational mechanical energy become hydraulic energy while the hydraulic energy is in the form of pressure energy. Even though the water pump is designed to be low-maintenance, highly efficient, and simple to operate, but we cannot observe the blockage condition of the water pump from its exterior due to the fully enclosed system. The blockage of the pump inlet could result in cavitation or mechanical parts breakdown which would increase the maintenance cost. Machine Learning is one of the ways as a preventive method by applying the data collected from the clogging experiment in the vibration lab to build up a machine learning model for classification of flow blockage levels in the centrifugal pump. The data collected for this machine learning model is using the statistically significant features from vibration and acoustic analysis. The features extracted of time domain and frequency domain in vibration and acoustic will use as database of a Support Vector Machine (SVM) algorithms by using MATLAB R2021a. The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.