Due to the space environment's hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identifying any failures or unusual behaviors can be recognized as the fundamental and critical role of aerospace systems' predictive health management process. This paper proposes a novel fault diagnosis approach using Deep Learning (DL) technique. DL has recently become a popular approach in artificial intelligence (AI) due to its supremacy in accuracy and fast inference with the huge amount of data and highest order network structures. The proposed approach consists of two main phases; the feature selection phase by Binary Grasshopper Optimization Algorithm (BGOA), and the learning and prediction phase by Artificial Neural Networks (ANNs) with voting ensemble method. The proposed approach named BGOA-EANNs has been validated and evaluated its efficacy by comparing two existing diagnosis techniques using two types of aerospace health diagnosis datasets; satellite power system and aircraft engines. The experimental results demonstrated the effectiveness and superiority of the proposed approach.

Over the decades, as industrialization progressed, energy has been a critical topic for scientists and engineers. Particularly, photovoltaic technology has drawn great attention in the renewable energy industry as an environmentally clean technology for converting sunlight into electricity. However, the complexity of energy chemistry and the need for novel materials to improve solar cell efficiency and cost-effectiveness have led to challenges in establishing rules beyond empirical observations. Machine learning models are being developed to streamline the prediction process and efficiently predict photovoltaic parameters. This paper proposes a novel hybrid-optimized multi-objective predictive model to predict the photovoltaic parameters: open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (PCE). The proposed model is based on Bayesian Optimization (BO) with the ensemble Bootstrap Aggregating (Bagging) decision tree. The proposed model integrates with the Explainable Artificial Intelligence (XAI) using the SHAP (Shapley Additive Explanations) values to introduce feature importance analysis that provides valuable insights into the impact of individual features on prediction outputs. The proposed model, named BO-Bagging, achieves high prediction accuracy, with an average high correlation coefficient of r = 0.92, a coefficient of determination of R² = 0.82, and a Mean Square Error (MSE) of 0.00172. In terms of complexity, the BO-Bagging model has a short processing time that is indicated with an average training time of 182.7 s and an average inference time averaging 0.00062 s. Also, the number of predicted observations per second is measured by prediction speed, which results in good prediction accuracy with an average of 2188.4 and model size with an average of 10,740.4 KB. Finally, the proposed model’s primary critical operations across each phase, from training to predicting the final outputs, are represented by 108 floating-point operations per second (FLOPS). All of these results demonstrate the proposed model’s accuracy and high efficiency in intelligent chemical applications.