Modern intermittent electromechanical drive systems demand reliable heavy-load startup capability, while conventional squirrel-cage induction machines suffer from an inherent trade-off between high starting torque and severe transient inrush current. The single-end segmented rotor induction machine adopts a dual-state operating mechanism to effectively alleviate this contradiction. However, existing studies mainly focus on static electromagnetic performance verification, and load-adaptive recommended switching timing for dynamic operating scenarios remains insufficiently explored, restricting its practical engineering application. This paper addresses this research gap by investigating load-matched switching timing strategies for the proposed dual-state machine. Theoretical modeling of the dual-state operating principle and multi-branch equivalent circuit is performed, combined with finite element simulations and prototype experiments to characterize transient startup and state-transition behaviors at different switching instants and variable loads. The results confirm that the proposed machine reduces startup inrush current by nearly 30% and improves the starting torque multiple by 20% compared with conventional counterparts, based on equivalent-circuit and finite-element analyses. Verified switching rules indicate that pre-synchronization switching best balances light-load transients, whereas under heavy load the winding should be switched as early as the current constraint permits, since delayed switching neither reduces the surge nor shortens startup. This work provides a feasible technical basis for the high-reliability engineering deployment of segmented rotor induction machines in intermittent heavy-load drive systems.
The bogie transmission system is the core power chain of a railway vehicle, and the health of its traction-motor bearings, gearbox bearings and gears directly determines running safety. Early-stage fault impulses, however, are weak and are usually buried in strong background vibration from gear meshing, electromagnetic interference and wheel-rail excitation, which makes fault feature extraction extremely difficult. This paper proposes a cascade denoising method that combines complementary ensemble empirical mode decomposition (CEEMD) with an improved wavelet threshold function for the fault diagnosis of train bogie transmission systems. The vibration signal is decomposed by CEEMD, sensitive intrinsic mode functions are selected by the kurtosis criterion, the reconstructed signal is denoised by a piecewise improved wavelet threshold function whose transition band is controlled by a parameter alpha, and Hilbert envelope demodulation extracts the fault characteristic frequencies. The method is validated on simulated inner-race fault signals at four input noise levels (−10 to +5 dB) and on the public BJTU-RAO bogie dataset covering five health states and three operating speeds. At −10 dB input SNR the output SNR is improved from −10.0 dB to −2.2 dB, close to the best soft-threshold result, and at 0 dB the proposed method outperforms both the hard and soft thresholds. On the real bogie data, the method improves the fault-frequency identification SNR of the tooth-root crack by 3.6 dB and of the rolling-element fault by 1.6 dB at 20 Hz supply (with smaller gains at 40/60 Hz) and clearly preserves the 293.2 Hz outer-race fault frequency. The whole pipeline processes a 2 s, 64 kHz record within about 3 s, which is potentially applicable to quasi-real-time monitoring with a processing time of about 3 s per 2 s record. It should be noted that the improvement is fault-dependent: on already-visible strong faults the universal threshold may over-smooth the feature, and on pure Gaussian white noise direct wavelet thresholding without CEEMD can outperform the full cascade. The method is therefore recommended for weak incipient fault detection rather than as a universal denoiser.
Battery capacity and resistance affect different vehicle responses, and their apparent importance depends on the range criterion. This study compares these effects in a fixed 2011 Nissan LEAF ZE0 model using measurements linked to the same vehicle identification number (VIN). Beginning-of-test (BOT) and end-of-test (EOT) pack measurements define controlled capacity-only, resistance-only, and coupled states. Under repeated Worldwide Harmonized Light Vehicles Test Cycle (WLTC) operation from 100% to 20% state of charge (SOC), replacing the rated pack capacity of 66.2 Ah with the measured BOT capacity of 57.6 Ah reduced the simulated distance from 113.59 to 100.48 km (−11.54%). BOT resistance scaling alone yielded 113.42 km (−0.15%); the coupled state yielded 100.02 km (−11.95%). The 66.2- Ah rating is not a measured beginning-of-life capacity, so these differences are nominal reference simulation contrasts, not measured lifetime range losses. Capacity dominance is expected under the SOC stopping rule. Resistance scaling was more visible in the diagnostic integral of current squared times effective resistance (I2R): BOT increases were 12.34% under WLTC and 13.01% under the US06 supplemental driving schedule, while terminal energy changes were −0.10% and <0.01%, respectively. Under the severe EOT resistance sensitivity, these diagnostic increases reached 197.27% and 231.12%, and the US06 operational minimum battery terminal voltage fell from 360.84 to 277.55 V. The contribution is a controlled comparison of range, terminal energy, voltage, and resistance-sensitive diagnostics. Numerical effect sizes apply to the tested model and boundaries; voltage-, power-, and temperature-limited operation require separate evaluation.
Machining deformation causes local variations in wall thickness in large rotational thin-walled components, while limited access to the inner surface complicates measurement and compensation. This study uses raw points (RPs) on accessible reference regions left unmachined by the relevant operations, together with the reference thickness, to reconstruct the local inner profile. The component is partitioned according to process features, and the deviations in each partition are arranged as an axial sequence. The lowest polynomial order satisfying a maximum residual threshold is selected, and the resulting continuous compensation functions are mapped to densified numerical control (NC) toolpaths. Three adaptively compensated regions were compared with three manually adjusted regions on one aero-engine grid casing. The average regional wall-thickness range and sample standard deviation decreased by 67.9% and 73.4%, respectively. All 42 inspection points in the adaptive group met the 1.10–1.40 mm tolerance, compared with 32 points in the manual group. Data processing and NC-program regeneration after measurement took approximately 6 min, compared with at least 2 h for the manual workflow, a reduction of approximately 95%. These results support improved wall-thickness uniformity and program-preparation efficiency for the tested casing.
Conventional ball bearing interference fit verification relies heavily on labor-intensive finite element (FE) simulations, while existing data-driven approaches suffer from non-standard dataset preparation, single-objective optimization, and a lack of uncertainty-aware risk assessment. This paper develops an integrated rapid verification framework to address these limitations. First, an automated dataset construction pipeline combining dimension chain decoupling and Latin Hypercube Sampling (LHS) is established, enabling fully automatic CAE modeling, solving and result extraction. Second, a multi-output fully connected neural network is optimized via a composite-objective Bayesian algorithm, which balances prediction accuracy, output stability and model complexity. Third, Monte Carlo Dropout is introduced to quantify epistemic uncertainty and generate probabilistic prediction intervals. Simulation results show that the dataset construction efficiency for 400 samples is more than 90 times higher than the manual approach, with a sample validity rate of 100%. The proposed model achieves an average test SMAPE of 12.48% under the single split used for comparison, and 16.12 ± 2.07% under 5-fold cross-validation. The value of this model lies in rapid evaluation of geometric variants: a single forward pass takes about 2 ms, and the full uncertainty prediction takes about 0.4–0.5 s. It outperforms classical surrogate models, including Kriging and response surface methodology on average, while a classical response surface is more accurate for the ZC_U1 output. The 95% prediction interval yields an empirical coverage rate of approximately 92.5–95.0% for epistemic uncertainty; corresponding 95% confidence intervals (Clopper-Pearson) for these coverage estimates are also reported, providing a quantitative reference for risk-informed design verification. This framework upgrades traditional deterministic point verification to interval verification with uncertainty characterization. It targets the axisymmetric interference fit between a deep groove ball bearing inner ring and a hollow journal between a shoulder and a snap ring groove, under linear elasticity and a constant friction coefficient. The training data cover parameter ranges representative of automotive drive-motor bearing fits; the specific ranges are not disclosed due to commercial confidentiality.
