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Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units
Highlights A simultaneous fault diagnosis model is developed with a transformer architecture. Time-series data are utilized to diagnose simultaneous faults in the early stage. A novel multi-head attention mechanism attending time steps and features is adopted. The proposed model is verified by experiments on an on-site air handling unit. The model outperforms GRU model with less training time under the same accuracy.
Abstract An advanced deep learning-based method that employs transformer architecture is proposed to diagnose the simultaneous faults with time-series data. This method can be directly applied to transient data while maintaining the accuracy without a steady-state detector so that the fault can be diagnosed in its early stage. The transformer architecture adopts a novel multi-head attention mechanism without involving any convolutional and recurrent layers as in conventional deep learning methods. The model has been verified by an on-site air handling unit with 6 single-fault cases, 7 simultaneous-fault cases, and normal operating conditions with satisfactory performances of test accuracy of 99.87%, Jaccard score of 99.94%, and F1 score of 99.95%. Besides, the attention distribution reveals the correlations between features to the corresponding fault. It is found that the length of the sliding window is key to the model performance, and a trade-off is made for the window length between the model performance and the diagnosis time. Based on the similar idea, another sequence-to-vector model based on the gated recurrent unit (GRU) is proposed and benchmarked with the transformer model. The results show that the transformer model outperforms the GRU model with a better Jaccard score and F1 score in less training time.
Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units
Highlights A simultaneous fault diagnosis model is developed with a transformer architecture. Time-series data are utilized to diagnose simultaneous faults in the early stage. A novel multi-head attention mechanism attending time steps and features is adopted. The proposed model is verified by experiments on an on-site air handling unit. The model outperforms GRU model with less training time under the same accuracy.
Abstract An advanced deep learning-based method that employs transformer architecture is proposed to diagnose the simultaneous faults with time-series data. This method can be directly applied to transient data while maintaining the accuracy without a steady-state detector so that the fault can be diagnosed in its early stage. The transformer architecture adopts a novel multi-head attention mechanism without involving any convolutional and recurrent layers as in conventional deep learning methods. The model has been verified by an on-site air handling unit with 6 single-fault cases, 7 simultaneous-fault cases, and normal operating conditions with satisfactory performances of test accuracy of 99.87%, Jaccard score of 99.94%, and F1 score of 99.95%. Besides, the attention distribution reveals the correlations between features to the corresponding fault. It is found that the length of the sliding window is key to the model performance, and a trade-off is made for the window length between the model performance and the diagnosis time. Based on the similar idea, another sequence-to-vector model based on the gated recurrent unit (GRU) is proposed and benchmarked with the transformer model. The results show that the transformer model outperforms the GRU model with a better Jaccard score and F1 score in less training time.
Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units
Wu, Bingjie (author) / Cai, Wenjian (author) / Cheng, Fanyong (author) / Chen, Haoran (author)
Energy and Buildings ; 257
2021-10-05
Article (Journal)
Electronic Resource
English
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