Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

In this study, a direct recompression supercritical CO2 Brayton cycle, using parabolic trough solar concentrators (PTC), is developed and analyzed employing a new simulation model. The effects of variations in operating conditions and parameters on the performance of the s-CO2 Brayton cycle are investigated, also under varying weather conditions. The results indicate that the efficiency of the s-CO2 Brayton cycle is mainly affected by the compressor outlet pressure, turbine inlet temperature and cooling temperature: Increasing the turbine inlet pressure reduces the efficiency of the cycle and also requires changing the split fraction, where increasing the turbine inlet temperature increases the efficiency, but has a very small effect on the split fraction. At the critical cooling temperature point (31.25 °C), the cycle efficiency reaches a maximum value of 0.4, but drops after this point. In optimal conditions, a cycle efficiency well above 0.4 is possible. The maximum system efficiency with the PTCs remains slightly below this value as the performance of the whole system is also affected by the solar tracking method used, the season and the incidence angle of the solar beam radiation which directly affects the efficiency of the concentrator. The choice of the tracking mode causes major temporal variations in the output of the cycle, which emphasis the role of an integrated TES with the s-CO2 Brayton cycle to provide dispatchable power.

Lately, ceramic fuel cells (CFCs) have held exceptional promise for joint small- and large-scale applications. However, the low-oxygen reduction response of cathode materials has hindered the low operating temperature of CFCs. Herein, we have developed a semiconductor based on La and Ce co-doped SrCoO₃ and embedded them in porous Ni-foam to study their electrochemical properties. The porous Ni-foam-pasted La_0.2Sr_0.8Co_0.8Ce_0.2O_3‒δ cathode displays small-area-specific resistance and excellent ORR (oxygen reduction reaction) activity at low operating temperatures (LT) of 450–500 °C. The proposed device has delivered an impressive fuel cell performance of 440 mW-cm⁻², using La_0.2Sr_0.8Co_0.8Ce_0.2O_3−δ embedded on porous Ni-foam substrate cathode operation at 550 °C with H₂ fuel and atmospheric air. It even can function well at a lower temperature of 450 °C. Moreover, La_0.2Sr_0.8Co_0.8Ce_0.2O_3−δ embedded on porous Ni-foam shows very good activation energy compared to individual SrCoO₃ and La_0.1Sr_0.9Co_0.9Ce_0.1O_3−δ embedded on porous Ni-foam, which help to promote ORR activity. Different characterization has been deployed, likewise: X-ray diffraction, photoelectron-spectroscopy, and electrochemical impedance spectroscopy for a better understanding of improved ORR electrocatalytic activity of prepared La_0.2Sr_0.8Co_0.8Ce_0.2O_3−δ embedded on porous Ni-foam substrate. These results can further help to develop functional cobalt-free electrocatalysts for LT-SOFCs.

This study developed a two-dimensional multiphysics-coupled model for co-electrolysis of CO₂ and H₂O in solid oxide electrolysis cells (SOECs) using COMSOL Multiphysics, systematically investigating the influence mechanisms of key operating parameters including temperature, voltage, feed ratio, and flow configuration on co-electrolysis performance. The results demonstrate that increasing temperature significantly enhances CO₂ electrolysis, with the current density increasing over 12-fold when temperature rises from 923 K to 1423 K. However, the H₂O electrolysis reaction slows beyond 1173 K due to kinetic limitations, leading to reduced H₂ selectivity. Higher voltages simultaneously accelerate all electrochemical reactions, with CO and H₂ production at 1.5 V increasing by 15-fold and 13-fold, respectively, compared to 0.8 V, while the water–gas shift reaction rate rises to 6.59 mol/m³·s. Feed ratio experiments show that increasing CO₂ concentration boosts CO yield by 5.7 times but suppresses H₂ generation. Notably, counter-current operation optimizes reactant concentration distribution, increasing H₂ and CO production by 2.49% and 2.3%, respectively, compared to co-current mode, providing critical guidance for reactor design. This multiscale simulation reveals the complex coupling mechanisms in SOEC co-electrolysis, offering theoretical foundations for developing efficient carbon-neutral technologies.

Flexible operation alters the thermodynamic performance of coal-fired boilers, but conventional heat-loss indicators do not fully describe the quality of energy discharged from the system. This study evaluates a 660 MW ultra-supercritical boiler and its regenerative gas–air heater (GAH) using thermal-performance-test data at 264, 330, 396, and 660 MW. A data-traceable framework is established to distinguish conventional lower-heating-value-based heat-loss indicators from independently resolved exergy-loss terms. The second-law assessment focuses on the physical and chemical exergies of the dry flue gas and the chemical exergy associated with unburned carbon in solid residues, all of which can be independently reconstructed from the archived field-test records. The LHV-based boiler efficiency increases from 93.75% at 264 MW to 94.32% at 660 MW. The dry-stack chemical-exergy-loss fraction ranges from 0.0294 to 0.0312 and exceeds the corresponding physical-exergy-loss fraction of 0.0060–0.0069 at all four loads, while the chemical exergy associated with unburned carbon remains below $8.7\times {10}^{-4}$ of the fuel chemical exergy. The total explicitly resolved exergy-loss fraction increases from approximately 0.0360 at 264 MW to 0.0389 at 660 MW. Heat-loss-based proxies are numerically higher than the explicitly calculated dry-stack exergy-loss fractions at all four loads and cannot be substituted directly for second-law exergy-loss quantities; the numerical difference reflects distinct thermodynamic definitions rather than an additional physical loss. Because sufficiently complete air-side mass-flow and leakage data are unavailable, the GAH is characterized using directly traceable temperature-change indicators rather than a quantitative exergetic efficiency. The results demonstrate that first-law heat-loss indicators and explicitly resolved second-law exergy losses provide complementary but non-interchangeable diagnostics of wide-load boiler operation, while the proposed framework provides a traceable basis for thermodynamic assessment using archived industrial field-test data when complete boundary information is unavailable.