Transmission Components Research Articles

Modeling and application research of absolute gravimeter driven by a V-shaped linear ultrasonic motor.

A V-shaped linear ultrasonic motor (LUSM), used as a driving element for the absolute gravimeter, generates structural vibrations when the motor starts and stops, which interfere with and influence the accurate measurement of the absolute gravimeter. In order to address this problem and provide better stability and measurement accuracy, this paper designs a fuzzy proportional integral differential (PID) controller and realizes the drive control of the motor on a field programmable gate array (FPGA) using the Verilog hardware description language (HDL). The advantages of the proposed controller are verified by comparing the simulation results with those of the traditional PID controller. First, the technical index of the LUSM as the driving element is studied through theoretical analysis based on the operation mechanism of the absolute gravimeter. Then, the relationship between the output performance and the input parameters (excitation voltage, preload, and driving frequency) of the motor in a vacuum/natural environment is experimentally investigated. Finally, an experimental system is constructed, and experiments are conducted to measure the acceleration of gravity and compare with the test results of the habitual FG-5 absolute gravimeter. The experimental results show that the output performance of the V-shaped LUSM meets the requirements of the driving element of the absolute gravimeter. The combination of the two control methods can realize the repeatable and stable operation of the system. Moreover, the motor with a mass of only 92g can output force directly without transmission components. The complexity and the weight of the system are significantly reduced, which has certain advantages.

IPFOA-MKSVM and BA-MLP models for predicting closed busbar temperatures in high voltage nuclear power plants in different vacuum environments

The nuclear power closed busbar is a key power transmission component in the power system, and its high temperature may cause equipment failure. In this paper, for the temperature prediction of nuclear power closed busbar under vacuum environment, a multi-core support vector machine model optimized by the improved falcon predation algorithm and a multi-layer perceptron model enhanced by the back propagation algorithm are proposed. The vacuum pump is used to reduce the air pressure in the closed space to achieve a vacuum state. The collected data are preprocessed to improve the accuracy and stability of the model. In addition, the PSO and SVM models are used to compare and verify the superiority of the proposed model. The data set is divided into a training set and a test set. The results show that under different vacuum degrees, the prediction accuracy and stability of the IPFOA-MKSVM model are better than those of other models, but its error is slightly higher than the physical calculation result. Finally, the performance of the model in wind speed prediction is verified, and compared with several models to verify the accuracy of IPFOA-MKSVM under different vacuum and wind speed conditions.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Derivative analysis and evaluation of roll-slip fretting wear mechanism of ultra-thin-walled bearings under high service

UTWB (Ultra-thin-walled bearings) are mainly used in high-precision robots and as power transmission components. In this study, the raceway wear mechanism of UTWB after high service (continuous 7000 h wear test) was analyzed. From the macroscopic observation, it is observed that the surface layer of the raceway has roll-slip wear. The wear mechanism was derivative analyzed and evaluated from the experimental conclusions. Another form of surface damage, associated with oscillatory movements tangential to the surface, was fretting wear. Compared with the residual stress of the non-high service raceway, the tangential and axial residual stresses of the outer and inner raceways with high service wear increased. Grain delamination appeared on the subsurface layer of the high serviced outer and inner ring raceway. The rolling wear and sliding wear of the inner ring raceway were more serious than those of the outer ring raceway. The formation of nanocrystalline layer increases the surface hardness of the raceway. Meanwhile, with the transfer from the surface layer of the raceway to the subsurface layer, the degree of grain refinement gradually decreases and the interplanar crystal spacing gradually increases. Through the above data analysis, the roll-slip wear mechanism of UTWB raceway was evaluated in multiple dimensions, and the reliability scheme to improve the actual working conditions of UTWB raceway processing-wear-service was put forward, which provides scheme and data support for relevant manufacturing enterprises and scholars.

Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions

Abstract Bearings, as the core component for power transmission, are crucial in ensuring the safe and reliable operation of equipment. However, the fault information contained in a single-channel vibration signal is inherently limited. Additionally, under time-varying speed conditions, features are prone to drift, and the cross-domain diagnostic performance of most traditional domain adaptation (DA) models may drop dramatically. To solve the above problems and enhance the ability of DA models in extracting domain invariant features, this paper introduces a Multi-channel data fusion and Attention-guided Multi-feature Fusion-driven Center-aligned Network (MAMC). Initially, a multi-channel time-frequency information fusion strategy based on wavelet transform is constructed to achieve a comprehensive fusion of multi-channel data, thereby obtaining richer fault feature representations. Subsequently, a multi-branch feature fusion network, integrated with an attention mechanism, is devised to capture significant features across various dimensions and scales, resulting in more comprehensive and representative fault features. Finally, a novel Center-Aligned Domain Adaptation method (CADA) is proposed based on domain adversarial methods and center loss. By minimizing the distance between deep domain invariant features and trainable common class centers, the issue of domain shift between data is effectively alleviated, and the cross-domain diagnostic performance of DA models under the time-varying speed conditions is improved. The experimental results indicate that the MAMC method exhibits superior performance on both bearing datasets and is a promising approach for cross-domain intelligent fault diagnosis.

Direct Linearly Polarized Emission in van der Waals LEDs via Flexoelectric Effect

AbstractFollowing the rapid development of information technology, modern polarized light, which is a critical component for display and data transmission, has been in demand for miniaturization and high efficiency, rendering two‐dimensional (2D) semiconductors potential candidates. The traditional polarized light is usually generated by external optical structures or polarizers that influence the scaling and bring up losses. Previous works have reported polarized light emission from inversion‐asymmetric 2D semiconductors such as black phosphorus (BP), black arsenide phosphorus (AsP), and rhenium disulfide (ReS2), however, their emission wavelengths are not in the visible range. Here, a direct emission of linearly polarized light is demonstrated from van der Waals light–emitting diodes (vdWLEDs) via the flexoelectric effects by inducing the non‐uniform strain in monolayer (ML) transition metal dichalcogenides (TMDCs). In this work, the effects of strain including excitonic binding energy and exciton dipole moment distribution is analyzed by the density functional theory (DFT) then we show that linearly polarized photoluminescence (PL) with a degree of linear polarization (DOLP) of ≈17% can be realized at room temperature (RT), and the polarization angle is perpendicular to the direction of the strain‐gradient. By incorporating the strained ML TMDCs into vdWLEDs, electroluminescence (EL) with DOLP of ≈19% can be observed at RT. This work puts forward a direct and universal strategy for fabricating polarized LEDs based on inversion‐symmetric semiconductors.

Vibration source inversion-based fault diagnosis: Approach and application

The gear transmission system of industrial equipment is characterized by a highly integrated structure, non-stationary operating conditions, and large loads. Therefore, the attenuation of fault signal characteristics and the complexity of operating conditions in gear transmission systems significantly affect the effectiveness of vibration signal-based fault diagnosis. This paper proposes a unified diagnostic framework based on vibration source inversion for two typical transmission component fault signal phenomena: meshing frequency modulation sidebands and equally spaced fault characteristic frequency interval harmonic clusters. This approach incorporates the vibration transfer path analysis of passive subsystems and identifies bearing dynamic forces, followed by their decomposition, reconstruction, and sensitive frequency band localization. It enables accurate diagnosis of complex gear transmission systems under non-stationary conditions with limited vibration testing. The effectiveness and advantages of this method over existing mainstream signal processing techniques are validated through actual cases of gear and weak bearing fault diagnosis on a complex planetary gear transmission system test bench.

Validation of a new road and contact model for vehicle–soft soil terrain interaction through an elaborate modelling of the FED-Alpha vehicle

Employing multibody dynamic simulations with semi-empirical tyre models is widely recognised as a cost-effective approach. A recent development introduces a novel road and tyre–soil contact model that is not only swift and memory-efficient but also addresses limitations in classical semi-empirical models. This study conducts a thorough validation of the new road and contact model by creating a detailed multibody model of the four-wheeled vehicle, Fuel Efficiency Demonstrator – Alpha, integral to NATO’s Next-Generation reference mobility model. The comprehensive model encompasses the chassis, suspension, tyres, engine, transmission and various other components. Through simulations of various driving scenarios, accounting for complex terrain geometries, spatially varying soil properties and multi-pass phenomena, the model’s performance is evaluated. The simulation results are compared with physical measurements, providing a detailed assessment of the tyre–soil model’s predictive accuracy for wheeled vehicle mobility on deformable terrains.

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

A novel experimental method for studying rock collision

This paper introduces a new experimental method for studying rock collision by making full use of the beauty of stress wave theory. In this method, a newly developed energy transmission component was placed between the gas gun and the transmitted bar of a split Hopkinson pressure bar (SHPB). The forementioned component consists of an incident bar which moves frictionlessly within a specified distance, a circular steel plate welded to the incident bar, and a support base which is bolted to the SHPB bed. A rock specimen is attached to the farther end of the incident bar. When the striker bar, propelled by the gas gun impacts the incident bar, a compressive stress wave is transmitted from the incident bar to the rock specimen. When the compressive wave arrives at the free end of the rock specimen, it is reflected into a tensile wave. Then when the pure stress becomes tensile and it is over the tensile strength of the glue at the interface between the rock specimen and the incident bar, the rock specimen is ejected, and then the ejected specimen will collide with the transmitted bar. During specimen flight, the velocity of the rock specimen can be measured by a laser instrument, while the remained energy transferred to the transmitted bar is measured by strain gauges attached to it. The process of rock specimen flight before collision and fragment flight after collision can be photographed using a high-speed camera. This experimental method can be used to not only study a collision between a moving rock and another object, but also imitate a drop weight test. By using this new method, seven rock collision tests were successfully conducted.

Geometry strategies for the repair of cylindrical gears teeth using laser-directed energy deposition

Gears are essential components in power transmission systems. The replacement of the transmission of a wind turbine can reach more than $300 000 and face long lead times. Previous studies have shown that laser-directed energy deposition (L-DED) has the potential to be used in the repair of gears. However, there is a lack of understanding of the appropriate toolpath planning strategies and their impact on the performance of the gears repaired by L-DED. In this work, we study several strategies for the repair of cylindrical (straight and helical) gears. The strategies consider several types of gear tooth failures, allowing for the repair of a portion of the tooth and the reconstruction of the whole tooth. The studied strategies encompass planar and nonplanar slicing procedures. We manufactured several gear teeth to demonstrate the geometrical and kinematic feasibility of some strategies. We performed destructive analyses on the manufactured teeth to check for manufacturing faults. We used AISI 316L for these experiments, although we are assessing and testing powdered materials for their potential use in gear repair using L-DED. Future work will be devoted to the evaluation of the mechanical performance (e.g., wear resistance and bending fatigue) of the teeth manufactured with L-DED.

A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city

Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

Adaptive isogeometric gear contact analysis: Geometry generation, truncated hierarchical B-Spline refinement and validation

Gears are one of the most widely used transmission components. Their operation relies on the contact between mating gear teeth flanks for the transmission of power. Accurate prediction of the contact stresses at these regions, is crucial for the design and dimensioning of these systems. Gear design is centered around highly smooth involute curves that greatly influence their contact behaviour. In this paper, a fully adaptive isogeometric contact modelling scheme, based on hierarchical splines, is presented and applied to the simulation of gear contact problems. In particular, isogeometric simulation is performed for the modelling of mating pair of gear teeth, regarded as linearly elastic bodies. A boundary fitted B-Spline representation of the teeth is automatically generated from engineering design parameters and is used to define the initial discretisation basis. The numerical integration over the contact region is addressed using the so called, Gauss-Point to Surface formulation and a closest point projection procedure. Truncated hierarchical B-Splines are used to capture the highly localised nature of contact, while effectively reducing the number of degrees of freedom. The adaptivity is driven by the strain energy density gradient, which allows to automatically localise the mesh without a priori knowledge of the contact region between the teeth flanks. In our experiments we justify the choices made in different steps of our algorithm and we assess the performance of our adaptive solver with respect to classical tensor product B-Splines.

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

In a data stream, errors are quite likely to occur and sometimes this is much more terrible. So, data safety is very important in digital systems, especially in critical and real-time systems, microprocessors, embedded systems, computer memory, and data communication. The probability of soft error increases with the exponential rate of increasing transistor per chip, operational voltage, particle strike, condensation of bit-cell area, etc. To ensure data integrity, safety, and system reliability, error detection, and correction are fundamental components of data transmission and storage systems. Existing error correction techniques can solve several bits of error. However, these existing methods are not fully efficient, as some consume a lot of time, space, and bit overhead. An ideal approach will have the potential to minimize all of these parameters. This research paper proposes a novel error correction approach with horizontal, vertical, diagonal, and knight (HVDK) parity bits. This approach has been taken to correct 5-bit errors in 64 bits of data word using the parity-based technique with less bit overhead. Our research advances the knowledge of error correction methods and sheds light on how to pick and use parity bit schemes that are appropriate for different applications.

Tower and power line segmentation method based on RandLA-Net

Abstract The scale of the power grid of China has gradually expanded, and the number of long-distance and high-level transmission lines has also increased. However, it is very hard to detect the power infrastructure transmission lines because of the complex laying environment and various ground features. Lines and towers in the power system are important components of power transmission, and their safety directly affects the operation of the entire power system. Reasonable management of lines and towers can reduce the occurrence of faults and system safety risks. At present, both manual inspection efficiency and visual inspection processing accuracy are low. An airborne LiDAR point cloud has become a key means for monitoring towers and power lines. In response to the above problems, we propose a tower and power line segmentation algorithm based on RandLA-Net. Experimental results demonstrate that the proposed method gets precise detection of power poles and lines, and enhances the efficiency in identifying hazardous areas along power lines. The highest Mean Intersection over Union (mloU) recorded is 95.34%, establishing a clear correlation between power towers and lines. Power line data holds significant importance for the power sector, facilitating the monitoring and management of transmission lines.

Control and Modelling of Laser Shock Peening without Coating (LSPwC) Texture of AISI 9310 Steel.

LSPwC is an important development of Laser shock peening (LSP) technology, and surface texturing is an effective method to improve tribological properties. The combination of these is expected to innovate a new surface texturing technology with a strengthing effect, but no one has attempted it. In this paper, a new LSPTwC technology combining them is innovatively proposed and validated on AISI 9310 steel, which is commonly used in helicopter transmission components for surface texturing. The LSPTwC surface was studied using an optical microscope, electron microscope, energy dispersive spectrometer, and so on. The results proved that LSPTwC is an effective texturing method of AISI 9310 steel, which modulates the texture and improves the properties, such as the microhardness increased by more than 10%. A model for calculating the texture and process parameters is also constructed on a statistical basis, and a modeling method for textured surfaces is proposed. It is verified that the calculation results and the constructed model are highly consistent with the test, with a diameter deviation <3% and depth deviation <4%. The above results can provide the experimental basis, process design method, and calculation model for single-point LSPwC texturing of AISI 9310 steel parts for helicopters, which have application value.

An efficient pseudoelastic pure P‐mode wave equation and the implementation of the free surface boundary condition

AbstractBased on the elastic wave equation, a pseudoelastic pure P‐mode wave equation has been recently derived by projecting the wavefield along the wavefront normal direction. This pseudoelastic pure P‐mode wave equation offers an accurate simulation of P‐wave fields with accurate elastic phase and amplitude characteristics. Moreover, considering no S‐waves are involved, it is computationally more efficient than the elastic wave equation, making it an excellent choice as a forward simulation engine for P‐wave exploration. Here, we propose a new pseudoelastic pure P‐mode wave equation and apply the stress image method to it to implement the free surface boundary condition. The new pseudoelastic wave equation offers significantly improved computational efficiency compared to the previous pseudoelastic wave equation. Additionally, the wavefields simulated by this new pseudoelastic wave equation exhibit clear physical interpretations. We evaluate the accuracy of the new wave equation in simulating elastic P‐waves by employing a model with high‐velocity contrasts. We find that this new equation, which purely admits P‐waves, though having exact amplitude and phase behaviour as the elastic waves for transmission components, the amplitudes slightly suffer in the scattering scenario. The difference in amplitude between the elastic and our pseudoelastic increases as the contrast in velocity at the interface (interlayer velocity ratio) increases, especially the S‐wave velocities. This has negative implications on scattering from the free surface boundary condition or the sea bottom interface, especially if the shear wave velocity below the surface or the sea bottom is high. However, in cases where, like for land data in the Middle East, the transition to a free surface is smoother, the accuracy of the pseudoelastic equation is high. In all cases, regardless of the interlayer velocity ratio, the accuracy of the pseudoelastic wave equation in simulating the elastic case, for scattered waves, exceeds that of the acoustic wave equation in phase and amplitude.

OVERVIEW OF THE METHODS AND TOOLS FOR ENVIRONMENTAL COMPONENTS MONITORING

Monitoring of environmental components is an important process for determining the level of pollution and tracking changes in the environment, and plays a key role in ensuring the health and comfort of residents, as well as in preserving the environment. Continuous monitoring of environmental components is key to ensuring human health, protecting nature and reducing the negative impact on the climate and ecosystems, as well as achieving sustainable development. In order to combat environmental pollution, it is necessary to implement effective measures to limit emissions of harmful substances, use environmentally friendly technologies and green solutions in all sectors of the economy, and raise public awareness of the problem of environmental pollution. From the analysis of the sources reviewed, a pattern was identified that the information technologies mainly used to monitor environmental components are either Internet of Things (IoT) technologies using modern sensors and data transmission components or artificial intelligence technologies such as computer vision. Less commonly, the use of robots, UAVs, and digital twins is being traced. Based on a critical analysis of methods and tools for environmental components monitoring, there is a need to develop such methods and tools for environmental components monitoring that would: perform cheaper and more versatile environmental components monitoring than existing analogues, but at the same time have no less accuracy and speed; monitor the state of the environment; identify sources of environmental pollution; warn of environmental disasters; assess the state of natural resources; support environmental decision-making; collect and analyze various environmental indicators in real time; assessed the level of quality and safety of environmental components, which will allow immediate response to quality changes and promptly take the necessary measures, etc., which will be the focus of the authors' further efforts.

Focus-switchable piezoelectric actuator: A bionic thin-plate design inspired by conch structure

Traditional camera zoom mechanisms, relying on stepper motors and intricate transmission components, suffer from bulky designs that restrict their applicability in compact systems. This study presents a novel piezoelectric focus-switchable mechanism (PFSM) directly driven by a biomimetic radial-mode piezoelectric actuator (RMPA) with inclined driving structure mimicking the conch shell. The PFSM utilizes rotational motion for optical zoom, providing a more compact and efficient alternative to conventional linear motion-based systems. By utilizing finite element method (FEM) optimization, we developed a compact prototype (34 × 34 × 3 mm³, 14.83 g) and experimentally verified its performance, achieving a peak rotational speed of 1573.14 RPM, torque output of 3.6 mN·m, and step displacement resolution of 45.8 μrad. These attributes enable smooth lens module switching for optical zoom, thus demonstrating the mechanism’s feasibility. Importantly, the PFSM offers precise positioning without relying on additional transmission parts and features a self-locking capability when de-energized, rendering it an ideal choice for camera zoom applications. In summary, this study underscores the PFSM’s potential as a compact, lightweight, and efficient camera zoom mechanism, contributing meaningfully to the field of imaging technology and optical systems.

Interruption Cost Estimation for Value-Based Reliability Investment in Emerging Smart Grid Resources

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities.