Search Results (4)

Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single image GAN (ConSinGAN). These models are employed to generate synthetic data, thereby enriching the dataset and enhancing the robustness of the predictive models. The efficacy of this methodology was rigorously evaluated using publicly available milling datasets. The pre-processing of acoustic emission data involved the application of the Walsh-Hadamard transform, followed by the generation of spectrograms. These spectrograms were then used to extract statistical attributes, forming a comprehensive feature vector for model input. Three DL models—encoder-decoder long short-term memory (ED-LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN)—were applied to assess their tool wear prediction capabilities. The application of 10-fold cross-validation across these models yielded exceptionally low RMSE and MAE values of 0.02 and 0.16, respectively, underscoring the effectiveness of this approach. The results not only highlight the potential of TGAN and ConSinGAN in mitigating data scarcity but also demonstrate significant improvements in the accuracy of tool wear predictions, paving the way for more reliable and precise predictive maintenance in manufacturing processes. Full article

The accurate prediction of the remaining useful life (RUL) of Li-ion batteries holds significant importance in the field of predictive maintenance, as it ensures the reliability and long-term viability of these batteries. In this study, we undertake a comprehensive analysis and comparison of three distinct machine learning models—XDFM, A-LSTM, and GBM—with the objective of assessing their predictive capabilities for RUL estimation. The performance evaluation of these models involves the utilization of root-mean-square error and mean absolute error metrics, which are derived after the training and testing stages of the models. Additionally, we employ the Shapley-based Explainable AI technique to identify and select the most relevant features for the prediction task. Among the evaluated models, XDFM consistently demonstrates superior performance, consistently achieving the lowest RMSE and MAE values across different operational cycles and feature selections. However, it is worth noting that both the A-LSTM and GBM models exhibit competitive results, showcasing their potential for accurate RUL prediction of Li-ion batteries. The findings of this study offer valuable insights into the efficacy of these machine learning models, highlighting their capacity to make precise RUL predictions across diverse operational cycles for batteries. Full article

Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility. Full article

Introduction: Asthma is a chronic inflammatory disorder of the airways often characterized by airway remodeling and influx of inflammatory cells into the airways. Saffron (C. sativus) has been reported to possess anti-inflammatory, anti-allergic and immunomodulatory properties. Salbutamol is known to relax airway smooth muscles. Objective: To investigate the combined anti-asthmatic effect of C. sativus extract (CSE) and salbutamol in an ovalbumin (OVA)-induced asthma in rats. Materials and methods: Airway hyperresponsiveness (AHR) was induced in male Sprague-Dawley rats by OVA challenge and treated with CSE (30 mg/kg and 60 mg/kg i.p.) and salbutamol (0.5 mg/kg p.o) for 28 days. After the induction period, various hematological, biochemical, molecular (ELISA) and histological analyses were performed. Results: OVA-induced alterations observed in hematological parameters (total and differential cell counts observed in Bronchoalveolar Lavage Fluid (BALF) were significantly attenuated (p < 0.01) by CSE (30 mg/kg and 60 mg/kg) and salbutamol (0.5 mg/kg). The treatment combination also significantly decreased (p < 0.01) the levels of total protein and albumin in serum, BALF and lung tissues. Treatment with CSE and salbutamol significantly attenuated (p < 0.01) increase in OVA induced Th2 cytokine levels (TNF-α, IL-1β, IL-4, IL-13). Histopathological analysis of lung tissue showed that combined effect of CSE and salbutamol treatment ameliorated OVA-induced inflammatory influx and ultrastructural aberrations. Conclusion: The results obtained from this study show that the combined effect of CSE and salbutamol exhibited anti-asthmatic properties via its anti-inflammatory effect and by alleviating Th2 mediated immune response. Thus, this treatment combination could be considered as a new therapeutic strategy for management of asthma. Full article