Chiller Fault Prediction Using Recurrent Neural Networking Method

• Main Authors: Lin, Yu-Hsiang, 林于翔
• Other Authors: Chen, Chiun-Hsun
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/b9ebj3

碩士 === 國立交通大學 === 機械工程系所 === 106 === This paper uses the deep learning algorithm to classify the fault mode of the chiller and predicts it's faulty level. The data used in this paper are from ASHRAE experimental program[3, 4]. This paper uses a deep learning model, recursive neural network(RNN), to sequentially build a model that uses supervised learning for training. At the same time, the architecture of RNN, LSTM(Long Short-Term Memory) and GRU(Gated Recurrent Unit) was compared. Since the neural network model must be artificially configured with some hyperparameters, 12 scenarios were established for cross validation, and the suitable hyperparameters、architecture were selected for the model based on verification results. Under the eight-category classification of failure modes, the accuracy of test data set prediction can reach more than 99%, and the severity’s MAPE (Mean Absolute Percentage Error) is also below 2.5%. It indicates that the model developed in this paper has excellent predictive performance and can be applied to time series data analysis.