Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the air handling unit (AHU) system's time-varying dynamic properties. However, since the derived slow features are the linear combinations of all original variables, the conventional SFA based methods may suffer from poor model interpretability and result in inaccurate fault detection and diagnosis (FDD) performance. To enhance the dynamic AHU system FDD effectiveness, this paper presents a novel feature sparse representation based SFA algorithm through imposing the sparsity on the slow features, which is called the sparse three-way data based dynamic SFA (STBDSFA). We make two contributions to propose the STBDSFA approach. One contribution is to infuse the feature sparse representation technique into the constructed SFA based monitoring model by performing the sparse restriction on the loading vector, which can effectively eliminate the meaningless variables' coupling and select the key variable responsible for the fault detection. Before building the STBDSFA algorithm, the other contribution is to construct a new three-way data based dynamic SFA (TBDSFA) monitoring model to handle the AHU system's two-directional dynamic properties. In the established TBDSFA model, the auto-regressive moving average exogenous (ARMAE) model is first adopted to reveal the variables' auto-correlation relationships. Then, the multiway data analysis is applied to figure out the batch-wise dynamic property in multiple batch runs. After that, the dynamic SFA model is further set up to sufficiently tackle the time-wise dynamic nature within a batch run. To effectively diagnose the pattern of detected fault, another innovative work is to develop a long short-term memory (LSTM) based fault identification approach to classify the sparse slow features of fault snapshot dataset, owning to the LSTM's superiority of coping with the dynamic time-varying nature of the sparse slow features. By comparing with some traditional and closely related FDD methods, the case study on the ASHRAE Research Project RP-1312 experimental datasets are performed to verify the performance and effectiveness of the proposed FDD scheme for the AHU system. To be specific, in comparison with the sparse principal component analysis, sparse dynamic SFA, kernel locality preserving projection and sparse three-way data based SFA approaches, the developed STBDSFA based monitoring method not only reveals no fault detection delays to alert the eleven test faults, but also achieves the highest average fault detection rates, i.e., 99.31% for the T2 statistic and 99.77% for the SPE statistic. Contrasted with the support vector machine, temporal convolutional network, convolutional neural net-work and deep belief network based classification algorithms, the suggested STLSTM based fault identification approach obtains the highest average fault diagnosis rate, i.e., 95.51% for the eleven fault snapshot datasets. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel SFA algorithm STBDSFA based on sparse feature representation to enhance the effectiveness of dynamic AHU system FDD. The algorithm shows high fault detection and diagnosis rates on experimental datasets.