Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units

2021 ◽  
pp. 111608
Author(s):  
Bingjie Wu ◽  
Wenjian Cai ◽  
Fanyong Cheng ◽  
Haoran Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Air Handling Units

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals A deep learning unsupervised approach for fault diagnosis of household appliances

2020 ◽  
Vol 53 (2) ◽  
pp. 10749-10754
Author(s):  
Francesco Cordoni ◽  
Gianluca Bacchiega ◽  
Giulio Bondani ◽  
Robert Radu ◽  
Riccardo Muradore
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Household Appliances ◽  
Unsupervised Approach
Sign in / Sign up

Export Citation Format

Share Document