Degradation prognostics of aero-engine are a well-recognized challenging issue. Data-driven prognostic techniques have been receiving attention because they rely on neither expert knowledge nor mathematic model of the system. But they are highly dependent on the quantity and quality of degradation data. To solve the problems caused by unlabeled, unbalanced condition monitoring (CM) data and uncertainties of the prognostics process, a novel data-driven aero-engine degradation prognostic strategy is proposed in this article. First, two indicators are defined to remove redundant degradation features. Then, the number of discrete states of health is determined by a fuzzy c-means algorithm, and the health state labels can be automatically assigned for health state estimation, where the uncertain initial condition and the uncertainty of health state's transition are fully considered. Finally, a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model are proposed for remaining useful life estimation for aero-engines. Verification results using the aero-engine data from NASA can show that the proposed data-driven degradation prognostic strategy is effective and feasible.

On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.

Effective fault diagnosis of critical components is essential to ensure the safe and reliable operation of the entire system. This paper deals with the fault diagnosis of transmitter/receiver module, which is a critical component in the phased array radar system, by proposing a novel deep belief network learning method. A sparse deep belief network based on Gaussian function is first constructed to automatically learn the relationship between monitoring data and component health conditions. With the trained sparse deep belief network, the pseudo-labels are produced for unlabeled samples, while the information entropy is employed to calculate the confidence levels reflecting their certainty to reduce the effect of pseudo-label noise. The pseudo-labeled samples with high confidence levels are added to the training set to retrain the network. Optimal model configuration parameters are obtained through a chaos game optimization algorithm. The effectiveness of the proposed method is verified on a real-world dataset from a certain type of phased array radar. The experiments show that the mean identification rate of this method can reach 96.33%, which not only exceeds some deep belief network-based modeling methods, but also exceeds other intelligent methods.

Bearings serve as vital elements within mechanical systems, and their condition significantly influences the safety and operational efficiency of the entire setup. However, existing bearing fault diagnosis methods often exhibit poor performance under high-noise conditions, struggling to effectively extract discriminative fault features. To mitigate this challenge, this paper proposes a noise-robust bearing fault diagnosis approach that integrates multi-scale spatial-channel reconstructive convolution, a hybrid attention mechanism, and a quaternion Transformer model. Specifically, multi-scale convolution is employed to capture features at various scales, and a weighted fusion strategy is utilized to strengthen the model’s adaptability to challenging noisy conditions. In addition, the hybrid attention mechanism combines spatial, channel, and global attention modules, significantly improving the expressiveness of the learned features. The quaternion Transformer module incorporates quaternion-based operations, replacing the conventional multi-head self-attention and feed-forward network, thereby further optimizing both global and local feature extraction capabilities. Experimental validation on bearing fault datasets from both CaseWestern Reserve University and Nanjing Tech University confirms the proposed method’s significant performance improvement, demonstrating enhanced diagnostic accuracy and noise immunity compared to conventional approaches under high-noise scenarios.