Physical mechanism-corrected degradation trend prediction network under data missing.

Accurate degradation trend prediction (DTP) is crucial for optimizing equipment operation and maintenance, thereby boosting production efficiency. This study introduces a novel Data Repair and Dual-data-stream LSTM (DR-DLSTM) network to tackle the challenge of missing data in equipment DTP. The proposed DR-DLSTM framework employs convex optimization to consider both the trend and periodic variations in the data, incorporating polynomial and trigonometric functions into the implicit feature matrix to construct latent vectors for missing data rectification.

A gradient aligned domain adversarial network for unsupervised intelligent fault diagnosis of gearboxes.

Unsupervised domain adaptation alleviates the dependencies of conventional fault diagnosis methods on sufficient labeled data and strict data distributions. Nonetheless, the current domain adaptation methods only concentrate on the data distributions and ignore the feature gradient distributions, leading to some samples being misclassified due to large gradient discrepancies, thus affecting diagnosis performance. In this paper, a gradient aligned domain adversarial network (GADAN) is proposed.

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data.

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work.

Dual-frequency enhanced attention network for aircraft engine remaining useful life prediction.

Accurately evaluating the remaining useful life (RUL) of aircraft engines is crucial for ensuring operational safety and reliability, and serves as a critical foundation for making informed maintenance decisions. In this paper, a novel prediction framework is proposed for forecasting the RUL of engines, which utilizes a dual-frequency enhanced attention network architecture built upon separable convolutional neural networks. First, the information volume criterion (IVC) index and information content threshold (CIT) equation are designed, which are applied to quantitatively quantify the degradation features of the sensor and remove redundant information.

Health prediction of partially observable failing systems under varying environments.

The modern engineering systems often operate under varying environments and only partial information can be observed at discrete monitoring epochs. For such systems, few works have been done for the prognostics of health status using the available environment and monitoring information. Therefore, the aim of this article is to present a new health prediction method for modern engineering systems whose condition is partially observable under varying environments.

Quantum weighted long short-term memory neural network and its application in state degradation trend prediction of rotating machinery.

Classical long short-term memory neural network (LSTMNN) generally faces the challenges of poor generalization property and low training efficiency in state degradation trend prediction of rotating machinery. In this paper, a novel quantum neural network called quantum weighted long short-term memory neural network (QWLSTMNN) is proposed. First, quantum bits are introduced into the long short-term memory unit to express network weights and activity values.