An increasing number of electric vehicles (EVs) are replacing gasoline vehicles in the automobile market due to the economic and environmental benefits. The high penetration of EVs is one of the main challenges in the future smart grid. As a result of EV charging, an excessive overloading is expected in different elements of the power system, especially at the distribution level. In this paper, we evaluate the impact of EVs on the distribution system under three loading conditions (light, intermediate, and full). For each case, we estimate the maximum number of EVs that can be charged simultaneously before reaching different system limitations, including the undervoltage, overcurrent, and transformer capacity limit. Finally, we use the 19-node distribution system to study these limitations under different loading conditions. The 19-node system is one of the typical distribution systems in Jordan. Our work estimates the upper limit of the possible EV penetration before reaching the system stability margins.

In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α₁) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.

Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall’s tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall’s tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure.

Fretting hysteresis loops are multivalued in displacement because loading and unloading branches revisit the same displacement at different tangential forces. This study applies a normalized path coordinate, \( u=\frac{k-1}{N-1} \), to represent displacement and force as the single-valued mappings \( \delta \left(u\right) \)and \( T\left(u\right) \), and compares four first-order Takagi–Sugeno fuzzy models using a 600-point experimental IC25 loop. Ten leakage-c⁠ontrolled 70/30 train–test partition⁠s yielded mean test NRMSEs of 0.00528 for ANFIS–TS, 0.00616 for SC16–TS, 0.00817 for FCM–TS, and 0.01333 for SC9–TS, with no significant difference between ANFIS–T⁠S and SC16–TS after⁠ Holm correcti⁠on (p=0.084)⁠. Physical fidelity and robustness were assessed through dissipated loop energy, peak force, displacement span, closure behavior, cumulative energy evolution, measur⁠ement-nois⁠e sensitivity, residual diagno⁠stics, ru⁠le density, and cycle-origin sensitivity. A same-input RBF–SVR benchmark achieved a lower NRMSE of 0.000838, whereas the fuzzy models retained explicit local rule bases and adjustable complexity. The same nine-rule ANFIS–TS procedure was independently fitted to experimental loops at approximately 1, 14, and 24.5 µm peak-to-peak displacement, demonstrating low-error reconstruction across multiple amplitude conditions without implying zero-shot prediction by a fixed model. Overall, the results establish pat⁠h parameterization as an accurate and int⁠erpretable framework for reconstructing experimental fretting hysteresis rather than as a new friction constitutive law.

Control-valve stiction is a persistent source of nonlinear oscillation, degraded regulation, and excessive actuator activity in process-control systems because identical controller-demand values can correspond to different stem positions depending on motion direction and sticking history. This study develops a path-parameterized data-driven framework for reconstructing the resulting multivalued controller-output–valve-position trajectory using four first-order Takagi–Sugeno fuzzy architectures: fuzzy C-means, 16-rule and nine-rule subtractive-clustering models, and an adaptive neuro-fuzzy inference system. A normalized path coordinate converts the multivalued trajectory into synchronized single-valued representations suitable for fuzzy reconstruction. Validation uses an 801-sample literature-grounded Choudhury valve-stiction benchmark with 3% deadband-plus-stickband and 1% slip jump. To avoid artificial statistical replication, one 201-point, 50-s cycle is used for quantitative identification, while the remaining deterministic cycles are retained only for repeatability visualization. Across ten repeated 70/30 train–test partitions, the adaptive neuro-fuzzy model achieved the lowest mean held-out NRMSE (0.001652), followed by the 16-rule (0.001894), nine-rule (0.002490), and fuzzy C-means (0.008684) models. In the full-cycle descriptive fit, the adaptive neuro-fuzzy model achieved the lowest NRMSE (0.001431), whereas the 16-rule model yielded the smallest loop-area error (0.0106%). Under 5% training-noise contamination, the compact fuzzy C-means model produced the lowest clean-test NRMSE, revealing an accuracy–robustness trade-off. The results demonstrate that path-parameterized fuzzy inference can provide an interpretable surrogate for valve-stiction-loop reconstruction, soft sensing, digital-twin visualization, and control-performance monitoring. Because the benchmark is model-generated rather than a raw plant acquisition, the results establish reconstruction fidelity and within-trajectory generalization rather than independent experimental prediction.