This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications.

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process.

Addressing the limitation of traditional deep learning models in capturing the spatio-temporal characteristics of flight data and the constrained prediction accuracy due to sequence length in aero-engine life prediction, this study proposes an aero-engine remaining life prediction approach integrating a kernel slow feature analysis, a Gaussian mixture model, and an improved Autoformer model. Initially, the slow degradation features of gas path performance parameters over time are extracted through kernel slow feature analysis, followed by the establishment of a Gaussian mixture model to create a health state representation using Bayesian inferred distances for quantifying the aero-engine’s health status. Moreover, a spatial attention mechanism is introduced alongside the autocorrelation mechanism of the Autoformer model to augment the global feature extraction capacity. Additionally, a multilayer perceptron is employed to further elucidate the degradation trends, which enhances the model’s learning and predictive capabilities for extended sequences. Subsequently, experiments are conducted using authentic aero-engine operational data, comparing the proposed method with the standard Autoformer and Transformer models. The results demonstrate that the proposed method outperforms both models in swiftly and accurately predicting the remaining life of aero-engines with robustness and high prediction accuracy.