Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm.

The measurement of chemical oxygen demand (COD) is very important in the process of sewage treatment. The value of COD reflects the effectiveness and trend of sewage treatment to a certain extent, but obtaining accurate data requires high cost and labor intensity. To1 solve this problem, this paper proposes an online soft measurement method for COD based on Convolutional Neural Network-Bidirectional Long Short-Term Memory Network-Attention Mechanism (CNN-BiLSTM-Attention) algorithm.

Enhanced Feature Extraction Network Based on Acoustic Signal Feature Learning for Bearing Fault Diagnosis.

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM).