The use of organic fiber-reinforced polymer composites is growing fast owing to their sustainable, low-density, and eco-friendly nature. The present investigation was carried out to study the mechanical and thermal properties of hybrid epoxy composites reinforced with Murraya koenigii ( M. koenigii) fibers and filled with Syzygium cumini (S. cumini) bio-filler. The composites were prepared by the hand lay-up method with 0, 1, 2, 4, and 6 wt.% S. cumini filler, and the effect of untreated and alkali-treated fibers was studied. Mechanical characterization was carried out by tensile, flexural, impact, and hardness tests (ASTM standards), while thermal behavior was studied by thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA). The results indicated that composites containing 4 wt.% S. cumini filler showed optimum performance with alkali treatment, due to improved interfacial bonding between the fiber, filler, and epoxy matrix. TGA results showed improved thermal stability, with a higher degradation onset temperature and char yield, while DMA showed a higher storage modulus and a higher glass transition temperature ( T g ). However, the properties decreased when the filler loading exceeded 4 wt.% due to filler agglomeration and poor dispersion. The results showed that the performance of the composites can be significantly improved through fiber treatment and the optimal incorporation of S. cumini bio-filler, making them promising candidates for sustainable lightweight structural and semi-structural applications.
During the operation of new energy vehicles, the pressures of the air springs and the reservoir dynamically change in response to road conditions and vehicle loads. Consequently, the air suspension compressor must operate under variable back-pressures to accommodate pressure variations within the air suspension system. This study focuses on a miniature oil-free piston compressor used in the air suspension system of new energy vehicles, with particular emphasis on its operating states and thermodynamic characteristics under unsteady, variable back-pressure conditions (0.1–1.5 MPa). A combined air suspension compressor-reservoir model was established to analyze the thermodynamic performance and inflation characteristics of the compressor. Furthermore, a test platform was constructed for evaluating the variable back-pressure characteristics of the air suspension compressor, and thermal performance tests were conducted on the prototype. These results showed that under extremely high back pressure, the combined effect of expansion within the clearance volume and external leakage was the primary cause of the reduction in the air suspension compressor inflation. Additionally, at high pressure ratios, expansion and external leakage shifted the peak resistance torque of the compressor motor, causing fluctuations in motor speed and subsequently degrading the air suspension compressor inflation performance. Compared to conventional inflation conditions (0.6 MPa), the volumetric efficiency of the air suspension compressor decreased by 69%, and the isothermal indicated efficiency decreased by 48.1% when the maximum pressure of the air suspension system (1.5 MPa) was reached, leading to a sharp deterioration in air suspension compressor performance. Therefore, better oil-free sealing materials, a more compact clearance volume design, and multi-stage configuration are prospects for adapting to higher operating pressures. This study lays a theoretical foundation for subsequent performance optimization and air suspension compressor design.
Polyethylene terephthalate glycol–carbon fibre composite filaments are widely used in fused deposition modelling because of their good strength, stiffness and dimensional stability. In the present study, polyethylene terephthalate glycol–carbon fibre composite filaments were developed by incorporating carbon fibre into polyethylene terephthalate glycol at different weight percentages of 2.5, 5, 7.5 and 10 wt.%. The developed filaments were subsequently processed using fused deposition modelling to fabricate specimens for systematic evaluation of their mechanical, water absorption and microstructural characteristics. Tensile, compressive and flexural tests were conducted to investigate the influence of carbon fibre content on the mechanical performance of the fabricated composites. The results indicate that the incorporation of carbon fibre significantly influences the tensile, compressive and flexural properties of polyethylene terephthalate glycol. Among the investigated compositions, the 92.5%PETG + 7.5%CF composite exhibited the highest tensile strength of 44.58 MPa and flexural strength of 70.01 MPa, while the 90%PETG + 10%CF composition exhibited the highest compressive strength of 55.85 MPa. Water absorption analysis showed that the 92.5%PETG + 7.5%CF composition exhibited the lowest weight gain of 0.23%. Scanning electron microscopy analysis revealed the influence of fibre dispersion, interfacial bonding, fibre pull-out and localized void formation on the mechanical behaviour. The results show that the developed polyethylene terephthalate glycol–carbon fibre compositions exhibit composition-dependent mechanical and moisture-resistance characteristics, with 92.5%PETG + 7.5%CF providing the best overall balance among the investigated compositions.
Thermal management is essential for improving the efficiency and service life of photovoltaic systems. This study experimentally and numerically investigates a photovoltaic module cooled using an array of aluminum containers filled with lauric acid, an organic phase change material, under transient conditions. The phase change material system reduced the module temperature by up to 10.3 °C, with an average reduction of 8.2 °C, while promoting a more uniform temperature distribution. Over 48 hours, cumulative electricity generation increased by 9.71% compared with an uncooled reference module, corresponding to a net gain of 73.16 Wh. The numerical model closely reproduced the experimental transient response, yielding a mean absolute error of approximately 1.10 °C and a coefficient of determination ( R 2 ) value of 0.975. These results show that integrating phase change material with a suitably designed container array can effectively reduce photovoltaic operating temperatures and improve electrical output.
The inherent microstructural variability, poor surface finish, and dimensional inaccuracies of wire arc additive manufacturing (WAAM)-fabricated Inconel superalloys necessitate effective post-processing to achieve the required functional performance. Therefore, this study quantitatively evaluates the post-processing capability of near-dry electrical discharge machining on WAAM-fabricated Inconel and develops linear regression models. Experiments were conducted using a Taguchi-L9 design with three levels each of three parameters-current, duty cycle, and dielectric flow rate-while material removal rate (MRR), tool wear rate (TWR), and surface roughness were considered as performance variables. All experiments were performed for both copper and brass tool-electrodes, and the resulting response variables were quantitatively compared. The MRR obtained with brass (0.121-3.451 mm³/min) was considerably lower than with copper (0.906-12.34 mm³/min) and showed non-linear behaviour with increased instability at higher discharge currents and duty cycles. Copper consistently exhibited lower TWR, with near-zero or negative wear, while brass ranged from 0.340-5.279 mm³/min. The maximum surface profile height (Rz) ranged from 7-26 μm for brass and 13-41 μm for copper. These results indicate that copper enables high-productivity machining with superior performance, while brass is better suited for finishing applications. Except for the brass TWR model, which showed a lower adjusted R², all regression models were statistically significant and explained a substantial proportion of the variability in MRR, TWR, and Rz. The relatively poor fit of the brass TWR model is attributed to the inherently non-linear relationship between process parameters and TWR, which a simplified linear regression model cannot fully capture.
A physics–data fusion model for tool wear prediction, termed PI-ATBiLSTM, was proposed in the present study to address the limitations of conventional purely data-driven approaches, which do not readily incorporate physical knowledge of the cutting process and depend heavily on large quantities of labelled data. A bidirectional long short-term memory network was integrated with an attention mechanism to capture temporal dependencies and critical wear-related features from the monitoring data obtained from nine identical cutting tools. In addition, a physics-consistent loss function was introduced to embed constraints governing wear evolution into the training process, thereby improving the physical consistency of the predicted wear trajectories. The experimental results showed that, compared with the conventional data-driven model, PI-ATBiLSTM reduced the average root mean square error, mean absolute error and mean absolute percentage error by 60.92%, 60.83% and 62.50%, respectively. Robust predictive performance was maintained under different cutting conditions. The model was further validated on the publicly available PHM2010 dataset, achieving average root mean square error, mean absolute error and mean absolute percentage error values of 1.88 μm, 1.42 μm and 0.014, respectively. Overall, the proposed framework provides an accurate and robust physics–data fusion approach to tool wear prediction for intelligent manufacturing applications.
In this study, the electromagnetic properties of neat and 5 wt.% Cr 2 O 3 -doped aramid/glass fiber hybrid epoxy composites were investigated in the 1–20 GHz frequency range for radome and microwave shielding applications. The complex permittivity, reflection ( S 11 ), and transmission ( S 21 ) behaviors were systematically analyzed with and without metallic backing. Results showed that the real permittivity (ε′) decreased with increasing frequency due to Maxwell–Wagner polarization, while Cr 2 O 3 addition reduced both ε′ and dielectric loss (ε″), indicating lower polarization density. Reflection measurements revealed a pronounced resonance in the 8–10 GHz range, with minimum reflection values of approximately −42 dB for the neat composite and −52 dB for the Cr 2 O 3 -doped composite with metallic backing. Additional resonance minima of about −35 to −37 dB were observed at higher frequencies (17–18 GHz). Transmission results indicated S 21 values of approximately −15 to −30 dB for the neat composite and −15 to −28 dB for the Cr 2 O 3 -doped composite, while metallic backing significantly reduced transmission to −45 to −80 dB, reaching values close to −80 dB in the 15–20 GHz range, corresponding to an additional attenuation of about 20–50 dB. Overall, Cr 2 O 3 primarily influenced dielectric behavior and resonance characteristics, whereas the metallic layer dominated electromagnetic attenuation. The findings indicate that the proposed hybrid composites provide a good balance in terms of electromagnetic performance, making them promising candidates for advanced radome structures. Furthermore, their wide transmission bandwidth and tunable electromagnetic behavior suggest potential applicability as conventional composite core in multifunctional antenna systems.
Inconel X-750 is extensively used in aerospace, nuclear and power generation industries because of its excellent mechanical strength and oxidation resistance at elevated temperatures. However, its high strength, work-hardening tendency and poor thermal conductivity make conventional machining difficult. Laser-assisted turning (LAT) has been widely adopted to improve the machinability of hard-to-cut materials. The influence of machining parameters on cutting temperature and the associated changes in surface and tool morphology during LAT of Inconel X-750 have not been systematically investigated. In this study, LAT experiments were performed on Inconel X-750 using a Taguchi L27 orthogonal array by considering laser power, spindle speed, feed rate and depth of cut (DoC) as the machining variables, with cutting temperature selected as the primary response. The experimental data were analysed using analysis of variance and regression analysis to establish the relationship between machining parameters and cutting temperature. Scanning electron microscopy and microhardness measurements were used to characterize the machined surface and cutting tool. Laser power had the greatest influence on cutting temperature, contributing 84.11%, followed by DoC (6.38%). The regression model showed good agreement with the experimental results, with an R 2 value of 97.57%. The validation test confirmed the model with an accuracy of 94.83%. Moderate cutting temperatures produced more uniform surface morphology, whereas excessive temperatures promoted material smearing, adhesion and diffusion-related changes on the cutting tool. LAT reduced the microhardness because of thermal softening, although the hardness remained above the annealed condition of the alloy.
Additive manufacturing (AM) of titanium alloy is emerging in various aerospace, marine, automobile, and medical implants applications because of high strength-to-weight ratio, enhanced mechanical, biocompatibility, wear, and corrosion resistance properties. AM processes are meant for near-net-shape components with higher mechanical properties due to difference in microstructure and lacks ductility. Optimization of additively manufactured (Amed) process parameters (laser power, scan speed, and layer thickness etc) and post-processing techniques such as stress relief heat treatment and sustainable machining are essentially required to achieve required microstructure, mechanical properties, and surface quality to meet the strict industrial tolerances. Micro-textured tools used with MQL improved the machinability of AMed Ti-6Al-4V reducing chip-tool contact by 38%, feed force by 28.9%, and surface roughness by 10.4% respectively. Reduction of crater wear for cryogenic machining than dry is 58% for direct metal laser sintering and 80% for both heat-treated DMLS and wrought Ti6Al4V alloys. Substantial improvements in drilling performance of Ti-6Al-4V have been observed under hybrid hBN-GNP nanofluids i.e. reductions in energy consumption by 28.8% (wrought) and 24.36% (wire arc additive manufacturing [WAAM]), along with improvement of surface finish by 48.57% and 49.47% respectively. Machining wrought Ti-6Al-4V yields 39.52% higher CO 2 emission than WAAM alloy as revealed from sustainability assessment under nanofluids application. It is worthwhile to analyse machining performance of AMed Ti-6Al-4V under novel sustainable cooling and lubrication environments such as nanofluids as it enhances thermo-physical characteristics and beneficial for sustainable manufacturing. Therefore, this paper reviews the fabrication, characterization, and sustainable machinability aspects of novel additive manufactured Ti-6Al-4V followed by challenges, research gaps, and future scopes.
High-temperature, high-pressure control valves in steam systems experience coupled flow acceleration, heat transfer, pressure loading, and constrained deformation, making repeated structural assessment expensive. A one-way thermal-fluid-structural procedure is established for a normally open control valve, and a POD-RBF reduced-order model is developed to reconstruct the stress field along a measured heat-up and pressurization path. Wall pressure and temperature from the CFD model are transferred to the solid domain, and stress snapshots from six training conditions are aligned on a common reference mesh before modal reduction and radial-basis interpolation. The representative 17.2 MPa/575 °C condition gives a maximum velocity of 15.926 m/second, maximum equivalent stress of 244.96 MPa, and maximum deformation of 0.61765 mm. For the unseen G7 condition, the maximum-stress error is 0.80%. Extreme-condition stress classification identifies the passage transition as the location with the lowest safety margin, although the calculated stresses remain within the allowable limits.
Accurate prediction of the remaining useful life for ageing oil pipelines is critical for integrity management; however, conventional machine learning methods are limited by sparse inspection data. This study introduces a support vector regression-based approach for corrosion-induced wall thickness prediction and risk-based inspection prioritization, applied to eight oil transmission pipelines, each with only three inspection records spanning 15–32 years. Using a polynomial kernel with hyperparameter optimization, support vector regression was compared against the linear extrapolation model standard in industry practice. Although both models fit the training data comparably well, their predicted failure years diverged by up to 12.5 years, indicating a non-linear degradation behaviour that linear extrapolation cannot capture even when the training error is small. These predictions were integrated into a first-order second-moment probability-of-failure model, propagating corrosion rate and prediction uncertainty into quantitative risk scores and inspection priorities for practical risk-based inspection planning.
This work investigates numerically the effects of the inclination angle of the burner cap on the thermal performance and efficiency of liquefied petroleum gas stove. The simulations carried out using ANSYS Fluent were validated using the experimental data to ensure reliability. Inclination angles were first analyzed in 15° increments, followed by finer 5° steps near the optimal range using the validated model. Results indicate that an inclination angle of 85° yields the best performance, with thermal efficiency reaching approximately 61%. At this angle, heat transfer to the cooking surface is maximized, while heat losses are minimized. On the other hand, the 90° configuration results in flame instability, uneven heat distribution, and a significant reduction in efficiency by nearly 7%. These results show that optimizing burner geometry can significantly enhance thermal efficiency, and that computational fluid dynamics analysis provides a reliable and effective tool for guiding the design of more sustainable cooking appliances.
This study investigates transient flow fluctuations in a dual-pump deep-sea mud lifting system during transitions from single-pump to dual-pump series and parallel operation. An indoor test platform was combined with Computational Fluid Dynamics–Amesim modeling to characterize the transient responses and evaluate model accuracy. The predicted steady-state flow rates deviated from the reference value by 0.83% and 5.83% for series and parallel operation, respectively. An L81 orthogonal design was applied independently to the two switching modes, generating 162 simulation cases for the prediction of the normalized flow fluctuation amplitude. On independent test sets, the RIME-extreme learning machines model achieved root mean square error (RMSE) values of 1.3087 and 0.6695, mean absolute percentage error values of 5.7560% and 3.9586%, and R 2 values of 0.9855 and 0.9895 for series and parallel switching, respectively. Compared with conventional extreme learning machines, the corresponding RMSE values were reduced by 55.9% and 72.3%. Extended Fourier amplitude sensitivity test analysis identified pump speed as the most influential factor in both switching modes (S i > 0.28; S Ti > 0.45), while the interaction between pump speed and the switching-valve opening exhibited the strongest second-order effect (S ij > 0.06). These results indicate that coordinated control of pump speed and valve opening is particularly important for mitigating transient flow fluctuations during series switching.
Electromagnetic forming is a non-contact, high-speed metal-forming process in which the Lorentz force is responsible for the deformation of the workpiece. The coil's geometry and dimensions used to generate magnetic pressure play an important role in the sheet's uniform deformation. The novelty of this work is to investigate the effect of novel coil geometries, namely circular spiral coils (CSC) and rectangular spiral coils (RSC), on the formability of a 0.8 mm-thick AA1100 workpiece under electromagnetic forming (EMF). The coils used in the EMF process have identical spacing, equal numbers of turns, and a constant cross-sectional area. Experimental trials were performed by gradually increasing the discharge voltage and energy to the workpiece's fracture limit. In addition, numerical simulations were performed using the LS-DYNA solver. Formability, in terms of dome height, of the deformed workpiece was compared between experimental and numerical analyses. Some additional important parameters for the coils and the workpiece were analyzed, including current density, magnetic field, and Lorentz force density, as well as structural parameters, such as effective plastic strain and von Mises stress, developed in the workpiece. Experimental and numerical analysis results at a 10.05 kV discharge voltage reveal that the formability of the workpiece is higher in RSC than in CSC by 29% and 32%, respectively. However, the CSC demonstrates a more uniform material flow and magnetic field distribution. This research provides critical insights into how coil geometry influences high-strain-rate forming, establishing it as a primary factor in determining forming performance.
In this work, the influence of the waste pig fat-based biodiesel in the diesel fuel on the emission profiles and performance characteristics of a compression-ignition engine is investigated. At a fixed 25% load, tests were performed with engine speeds extending from 800 to 1400 r/min. The emission profiles such as CO, hydrocarbon, CO 2 , NO x , and smoke opacity, along with the evaluation of brake-specific fuel consumption and brake thermal efficiency in dual-fuel operation, were considered important aspects of this investigation. At an engine load of 25% and a rotational speed of 800–1400 r/min, the increase of waste pig fat biodiesel percentage resulted in a remarkable decrease of CO, hydrocarbon, and smoke opacity emissions. B80 decreased the CO emission from 0.37% to 0.21% (diesel: 0.75% to 0.43%) and the hydrocarbon emission from 34 to 19 ppm (diesel: 55 to 34 ppm). Also, the smoke opacity was reduced from 11% to 4.5% for B80 and for diesel from 14.5% to 8.5%. However, CO 2 concentrations were increased from 0.85 to 1.94% with increasing biodiesel content, as also NO x concentrations from 99 to 312 ppm. The engine performance was deteriorated with an increase in brake specific fuel consumption from 3.94 to 5.42 kg/kWh and a decrease in brake thermal efficiency from 24.2% for diesel to 17.82% for the B80 blend. After weighting among these competing factors, B20 is the best compromise, in that it offers a good balance of substantial reductions in CO, hydrocarbons, and smoke emissions, with moderate increases in NO x and decreases in efficiency that are acceptable for non-tempered engines.
In the present study, evolution of sol-gel based coatings on the glass substrates from basic precursors: tetraethoxysilane (TEOS), methyl triethoxysilane (MTES) to hybrid systems i.e., the combination of TEOS/MTES and TEOS/MTES/HAp (hydroxyapatite) have been discussed to present a comparative study between the coatings formed. The main objective is to investigate effect of molar ratio (MR) of water on hydrolysis and precursor molar ratio to formulate effective sol and prevent phase segregation with the aim of developing stable and effective coating formulations. The coating is prepared through sol-gel processing involving hydrolysis and condensation of silane precursors, which is then combined with HAp to form a coating material in a composite matrix of TEOS and MTES and further depositing the material on glass surfaces. The study also explores microstructural evolution, thickness progression, elemental, chemical and thermal analysis of the coated samples via characterization techniques. The time of gelation is found to be affected by varying content of water, where higher water corresponds to increased rate of reaction and vice versa suggesting the need to regulate such process parameters. The microstructural analysis reveals dense structure for TEOS while globular morphology for MTES and slightly homogeneous microstructure for combined coating while TEOS/MTES/HAp shows rough and interconnected network like morphology. FTIR confirms reduced hydroxyl peak for coatings with MTES, implying induced hydrophobic character and TGA suggests increased thermal stability of TEOS/MTES and HAp incorporated coating with approximately 45% residue while TEOS coating showed 25%, MTES showed 0% and TEOS/MTES combination showed 20–25% residue.
This research delves deeply into the flow properties and heat transfer in impingement jets using a new hybrid nanofluid (SiO 2 -multiwalled carbon nanotube/water). Various volume fractions of SiO 2 -multiwalled carbon nanotube/water are used as a working fluid, including 0.2% and 1.0%. The research aims to establish the effects of multiple variables, such as jet Reynolds number, plate velocity, nanoparticle morphology, and volume fraction, on the target surface's heat-transfer behavior. There are three jet Reynolds numbers (ranging from 35,000 to 65,000), three plate velocities (V = 0, 0.25, and 0.35 m/second), and three distinct nanoparticle shapes (spherical, cylindrical, and platelet) in this study. The results show that increasing the Reynolds number and the nanoparticles’ volume fraction to their maximum levels significantly increases the system's heat transfer rate. It has been found that, across all nanoparticle volume fractions, Reynolds numbers, and plate velocities, the SiO 2 -multiwalled carbon nanotube/water hybrid nanofluid exhibits the maximum heat transfer rate when the nanoparticles are platelet-shaped. Additionally, the results show that increasing the plate velocity inherently enhances the thermal performance across all tested domains. Specifically, a peak enhancement of up to 15% in the average Nusselt number is achieved at the maximum plate velocity (V = 0.35 m/second) under the optimal conditions (Re = 65,000 and 1.0% platelet-nanoparticle volume fraction) compared to the stationary target plate scenario. Ultimately, this study provides valuable information for making heat transfer systems more efficient in real-world settings.
AISI S7 tool steel finds application in aerospace, automotive, and tooling industries for its high toughness and impact resistance. Machining this material with acceptable quality remains challenging. This study investigates the influence of Wire Electrical Discharge Machining (WEDM) process parameters, including pulse–on time (T on ), pulse–off time (T off ), discharge voltage (V), and wire tension (WT), on the surface roughness of AISI S7 tool steel using a machine learning–assisted predictive framework. Experimental WEDM data were used to develop and evaluate regression models, namely Linear Regression, Lasso, Elastic Net, Polynomial Regression, Random Forest, XGBoost, LightGBM, Support Vector Regression, and Voting Regressor. Model performance was assessed using the determination coefficient ( R ²), root mean square error, and absolute error. The results revealed that pulse–on time was the most influential parameter affecting surface roughness, followed by pulse–off time and discharge voltage, whereas wire tension exhibited a comparatively lower effect. Among the evaluated models, LightGBM achieved the highest prediction accuracy, with a mean R ² of 0.968 ± 0.011, RMSE of 0.100, and MAE of 0.081 under 5–fold cross–validation. Bayesian optimization was employed to identify optimal conditions, and experimental validation confirmed close agreement between predicted and measured surface roughness values. Scanning electron microscopy analysis revealed WEDM surface features, including craters and re–solidified debris, supporting the machining trends. The study demonstrates that integrating machine learning and Bayesian optimization provides an effective approach for predicting and optimizing surface roughness in WEDM of AISI S7 tool steel. It is recommended that pulse–on time be carefully controlled to achieve improved surface quality and machining performance.
Cold metal transfer wire arc additive manufacturing has emerged as a viable additive manufacturing technique for fabricating large scale components from corrosion-resistant alloys with minimal distortion. In this study, 904L super austenitic stainless steel was deposited using cold metal transfer wire arc additive manufacturing and its quantitative microstructural evolution and mechanical behavior were systematically compared with the base metal. The as-deposited wall exhibited a fully austenitic microstructure with 99.7% γ-phase and negligible secondary phases. Electron backscatter diffraction (EBSD) analysis revealed grain coarsening in the deposit, with the average grain size increasing from 24.27 ± 15.91 μm in the base metal to 39.25 ± 101.84 μm in the wire arc additive manufacturing wall. A pronounced crystallographic texture was also observed with the texture intensity increasing from 2.53 to 17.07 attributable to directional solidification and epitaxial grain growth during layer-wise deposition. The horizontal specimens attained an ultimate tensile strength of 641 ± 23 MPa, a yield strength of 302 ± 14 MPa, and an elongation of 42.4 ± 2%. Microhardness was the highest in the bottom region of the deposit at 202.41 ± 11 HV, compared to 167.2 ± 4 HV for the base metal. The findings show that cold metal transfer wire arc additive manufacturing can produce 904L components with controlled microstructures and superior mechanical performance compared to conventional base metal.
To improve diagnostic accuracy and online output stability, this study proposes a hierarchical fault diagnosis method for sanitation-vehicle hydraulic systems based on pressure–time series. The hydraulic circuit of the bucket tipping cylinder in the QDT5120ZYSA6 sanitation vehicle was modeled in AMESim, and the normal condition together with three representative simulated fault scenarios, namely air mixed into the hydraulic oil, abnormal oil temperature, and internal pump leakage, were established. A hierarchical framework consisting of normal/fault discrimination and fault subtype identification was then developed, and offline modeling was performed by combining offline sliding-window segmentation, class-reliability refinement, and evidence fusion. The offline results show that the proposed hierarchical method outperforms the non-hierarchical four-class diagnosis method, as indicated by the confusion matrices, accuracy, and F1-score. In the Stage-2 fault subtype identification task, Reliability-DS achieved 100.00% accuracy and 100.00% macro-F1, outperforming RF, XGBoost, and CNN. Furthermore, online validation was carried out using pressure signals generated from AMESim/Simulink co-simulation, and a label-retention strategy was introduced to improve temporal consistency. The proposed method improves offline diagnostic performance and online output stability under the simulated operating conditions.
