Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

• Published in: 东华大学学报（英文版） Vol. 40; no. 5; pp. 548 - 559
• Main Authors: LUO Xin, CHEN Jing, YUAN Dexin, YANG Tao
• Format: Journal Article
• Language: English
• Published: 31.10.2023
• Subjects: long short-term memory(LSTM); genetic algorithm(GA); anomaly detection; production equipment; principal component analysis(PCA)
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202204002

TP305; The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.