Novel double layer BiLSTM minor soft fault detection for sensors in air-conditioning system with KPCA reducing dimensions

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

“…Due to the advantages of small samples, strong anti-noise ability, and simple network structure, the long short-term memory network has been widely used in the field of defect detection. 56 , 57 , 58 To improve the accuracy of the cantilever defect identification model, the electric signals of the CSF-TENG were first decomposed by using the wavelet packet algorithm. As depicted in Figure 4 B, the decomposed signals were used as training sets and test sets to establish a defect classification model based on the long short-term memory algorithm.…”

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

“…Such faults can be more easily identified if the deviation can also be analysed in reverse chronological order. Therefore, using a BDLSTM/GRU can lead to better performance for detecting minor drifting faults than using a conventional LSTM/GRU [94]. In addition, HVAC systems present coupling and time-varying dynamics in general.…”

An Effective Fault Detection Method for HVAC Systems Using the LSTM-SVDD Algorithm

“…Fan faults, coil faults, pump faults and chiller faults were detected by using this method. Yan et al [7] argue that sensor faults of HVAC systems are not easily detected in the early stages. In order to accurately detect early faults of the system in real time, a method based on a combination of kernel principal component analysis and bi-directional two-layer long-short time memory networks was proposed.…”