Fault tolerance challenges in wearable computing for vital applications: a survey.

Wearable computers can be used in different domains including healthcare. However, due to suffering from challenges such as faults their applications may be limited in real practice. So, in designing wearable devices, designer must take into account fault tolerance techniques. This study aims to investigate the challenging issues of fault tolerance in wearable computing. For this purpose, different aspects of fault tolerance in wearable computing namely hardware, software, energy, and communication are studied; and state of the art research regarding each category is analysed. In this analysis, the performed works using the fault tolerance techniques are included in the form of 25 components and referred to as "fault tolerance plan". Using this fault tolerance plan and the appropriate profile, the fault tolerance of any wearable system can be evaluated. In this article, fault tolerances of several of the most prominent works conducted in the field of wearable computing were evaluated. The obtained results, with the medical profile, showed that only one wearable system had a fault tolerance of 91%, with the other systems having a fault tolerance of 24% or less. Also, the results obtained from evaluating these works, with the military profile, showed that only one wearable system had a fault tolerance of 76%, with the other systems having a fault tolerance of 19% or less. These mean that few studies have been conducted on the fault tolerance of wearable computing.

Dislocation response of ECC-RC composite supporting structures of tunnels passing through active fault.

To address the problems of the conventional composite supporting structures (CCSSs) such as insufficient anti-dislocation performance and deformation capacity, this study used Engineered Cementitious Composite (ECC) lining sections instead of the traditional lining sections and optimized support design parameters, resulting in the development of novel ECC-RC composite supporting structures (ECSSs) of tunnels passing through active fault. The dislocation response characteristics and their parameter sensitivity of the ECSS was revealed by way of 1/25-scale fault dislocation model tests and finite element analysis. The test results show that the mechanical response characteristics and the failure modes of the CCSS and the ECSS are similar under reverse fault dislocation. Compared with the CCSS, the anti-dislocation performance of the ECSS is significantly improved by introducing of the ECC lining and optimizing the design parameters. The vertical deformation of the ECSS and the range of influence under the same dislocation are significantly decreased, and the strain are reduced to different degrees. This phenomenon shows that by improving the material properties, shortening the spacing of aseismatic joints and optimising the thickness of the shock absorption layer, the stress conditions and applicability under deformation of the structure are improved. The ECSS benefits from the crack resistance and toughening effect of fibres, the degree and scope of cracking of the ECSS are significantly reduced compared with those of the CCSS, and internal and external through cracks and local spalling are absent. The results of finite element analysis show that the overall damage degree of the ECSS is decreased and the damage range is increased by decreasing the strength of the surrounding rock in the fault zone. The fault dislocation response pattern of the ECSS varies depending on the fault type. The damage degree caused by different fault types follows the order of normal fault, strike-slip fault, and reverse fault from large to small. However, the damage range caused by the strike-slip fault is significantly larger compared to normal fault and reverse fault. In the design of fault resistance, the surrounding rock conditions of the fault zone and the form of fault dislocation should be considered.

An advanced graph algorithm-based protection strategy for detecting kilometric and cross-country faults in DC microgrid.

Low voltage DC microgrids (LVDC) are on rise, because of increase in usage of electronics-based utility loads. However, the protection and safety aspects of these grids remain unresolved due to the fault current magnitude, unnecessary tripping, and blinding of protection. In this paper, an adaptive differential protection system incorporated with an advanced graph algorithm is proposed for DC microgrid. This graph algorithm is a combination of Fenwick tree and Bidirectional Dijkstra algorithm. Fenwick tree algorithm is used to determine the network configuration and Bidirectional Dijkstra algorithm is used to determine the least distance from the fault location to the nearest distributed generations. The developed protection scheme is applied to a 7 bus, 400 V DC microgrid setup using Real Time simulator (control hardware in loop) to detect and clear the faults such as kilometric faults, and cross-country faults. In terms of efficiency, the proposed algorithm demonstrates superiority over the conventional algorithm by detecting and rectifying faults in 384 µs.The results depict the robustness of the protection setup in clearing the faults rapidly with minimum network disconnection.

Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network.

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

Development of overcurrent relay based on wavelet transform for fault detection in transmission line.

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark's transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark's transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis.

In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those issue, we propose a novel semi-supervised Gaussian Mixed Variational Autoencoder method, SeGMVAE, aimed at acquiring unsupervised representations that can be transferred across fine-grained fault diagnostic tasks, enabling the identification of previously unseen faults using only the small number of labeled samples. Initially, Gaussian mixtures are introduced as a multimodal prior distribution for the Variational Autoencoder. This distribution is dynamically optimized for each task through an expectation-maximization (EM) algorithm, constructing a latent representation of the bridging task and unlabeled samples. Subsequently, a set variational posterior approach is presented to encode each task sample into the latent space, facilitating meta-learning. Finally, semi-supervised EM integrates the posterior of labeled data by acquiring task-specific parameters for diagnosing unseen faults. Results from two experiments demonstrate that SeGMVAE excels in identifying new fine-grained faults and exhibits outstanding performance in cross-domain fault diagnosis across different machines. Our code is available at https://github.com/zhiqan/SeGMVAE.

A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection.

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.

Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots.

The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model's long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm's effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples.

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil.

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Sealing faults and fluid-induced seismicity.

Sealing faults are nearly impermeable barriers that can form boundaries between subsurface pore-pressure domains. In hydrocarbon systems, sealing faults commonly form part of a structural trap; they are thus important elements for future storage of CO2 and other gases in depleted reservoirs. The Triassic Montney Formation in western Canada hosts low-permeability gas reservoirs containing sealing faults that have previously been assumed to compartmentalize pressure domains. In this study, we show that the distribution of induced seismicity associated with hydraulic fracturing (HF) exhibits a statistically significant spatial correlation with zones of high lateral gradient in pore pressure. These high-gradient zones are interpreted as sealing fault systems. The largest induced seismicity sequence, including a 4.5 ML mainshock on 30 November 2018, occurred during HF treatments in two horizontal wells, between which there is an exceptionally large contrast (~10 MPa) in measured pore pressure. Numerical simulation of a simplified model of a hydraulic fracture intersecting a nearby vertical fault, followed by fault rupture using rate-and-state friction rheology, generates results that are in good agreement with observed strike-slip faulting near one of the HF wells. Our study demonstrates that sealing faults exhibit previously unrecognized behaviour that may be important for understanding induced seismicity risk. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.

Stacking Fault-Enriched MoNi4/MoO2 Enables High-Performance Hydrogen Evolution.

Producing green hydrogen in a cost-competitive manner via water electrolysis will make the long-held dream of hydrogen economy a reality. Although platinum (Pt)-based catalysts show good performance toward hydrogen evolution reaction (HER), the high cost and scarce abundance challenge their economic viability and sustainability. Here, a non-Pt, high-performance electrocatalyst for HER achieved by engineering high fractions of stacking fault (SF) defects for MoNi4/MoO2 nanosheets (d-MoNi) through a combined chemical and thermal reduction strategy is shown. The d-MoNi catalyst offers ultralow overpotentials of 78 and 121 mV for HER at current densities of 500 and 1000 mA cm-2 in 1 M KOH, respectively. The defect-rich d-MoNi exhibits four times higher turnover frequency than the benchmark 20% Pt/C, together with its excellent durability (> 100 h), making it one of the best-performing non-Pt catalysts for HER. The experimental and theoretical results reveal that the abundant SFs in d-MoNi induce a compressive strain, decreasing the proton adsorption energy and promoting the associated combination of *H into hydrogen and molecular hydrogen desorption, enhancing the HER performance. This work provides a new synthetic route to engineer defective metal and metal alloy electrocatalysts for emerging electrochemical energy conversion and storage applications.

Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District.

Earthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.

Application of C-InGAN Model in Interpretable Feature of Bearing Fault Diagnosis.

Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the interpretable feature fault diagnosis of bearings. Initially, the vibration signal is denoised and transformed into a frequency domain signal. The model consists of the two primary networks, each employing a convolutional layer and an attention module, generator (G) and discriminator (D), respectively. Latent code was incorporated into G to constrain the generated samples, and a discriminant layer was added to D to identify the interpretable features. During training, the two networks were alternately trained, and the feature mapping relationship of the pre-normalized encoder was learned by maximizing the information from the latent code and the discriminative result. The encoding that represents specific features in the vibration signal was extracted from the random noise. Ultimately, after completing adversarial learning, G is capable of generating a simulated signal of the specified feature, and D can assess the interpretable features in the vibration signal. The effectiveness of the model is validated through three typical experimental cases. This method effectively separates the discrete and continuous feature coding in the signal.

Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping.

Vibration monitoring and analysis are important methods in wind turbine gearbox fault diagnosis, and determining how to extract fault characteristics from the vibration signal is of primary importance. This paper presents a fault diagnosis approach based on modified hierarchical fluctuation dispersion entropy of tan-sigmoid mapping (MHFDE_TANSIG) and northern goshawk optimization-support vector machine (NGO-SVM) for wind turbine gearboxes. The tan-sigmoid (TANSIG) mapping function replaces the normal cumulative distribution function (NCDF) of the hierarchical fluctuation dispersion entropy (HFDE) method. Additionally, the hierarchical decomposition of the HFDE method is improved, resulting in the proposed MHFDE_TANSIG method. The vibration signals of wind turbine gearboxes are analyzed using the MHFDE_TANSIG method to extract fault features. The constructed fault feature set is used to intelligently recognize and classify the fault type of the gearboxes with the NGO-SVM classifier. The fault diagnosis methods based on MHFDE_TANSIG and NGO-SVM are applied to the experimental data analysis of gearboxes with different operating conditions. The results show that the fault diagnosis model proposed in this paper has the best performance with an average accuracy rate of 97.25%.

Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows.

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments.

Worker's compensation and no-fault insurance are associated with decreased patient reported outcomes and higher rates of revision at 2 and 5 years follow-up compared to patients with commercial insurance undergoing hip arthroscopy for femoroacetabular impingement.

PURPOSE: To investigate the patient reported outcomes (PROs) of patients undergoing hip arthroscopy (HA) for femeroacetabular impingement syndrome (FAIS), a condition where irregular bone growth in the hip joint leads to friction and pain during movement, who have worker's compensation (WC) or no-fault insurance (NF) versus commercial insurance (CI) at both 2 year and 5 year follow-up. METHODS: This was a single center, single surgeon, retrospective analysis performed between August 2007 and May 2023 of consecutive patients that underwent HA, a minimally invasive surgical procedure used to diagnose and treat problems inside the hip joint through small incisions, for FAIS. Patients were divided into two cohorts-those with WC/NF and those with commercial insurance (CI). Patient reported outcomes (PROs), which included modified Harris Hip Score (mHHS) and Non-Arthritic Hip Score (NAHS), were collected preoperatively, as well as at least 2-year postoperatively. Additionally, other clinically relevant outcomes variables including prevalence of revision surgery and conversion to total hip arthroplasty were recorded. RESULTS: Three hundred and forty three patients met inclusion criteria. There were 32 patients in the WC/NF cohort and 311 patients in the commercial cohort. When controlling for age, sex, and Body Mass Index (BMI), WC/NF status was associated with lower mHHS at both 2 year (ß = - 8.190, p < 0.01, R2 = 0.092) and 5 year follow-up (ß = - 16.60, p < 0.01, R2 = 0.179) and NAHS at 5 year follow up (ß = - 13.462, p = 0.03, R2 = 0.148). The WC/NF cohort had a lower rate of achieving Substantial Clinical Benefit (SCB) for mHHS at 2-years follow-up (66.7% vs. 84.1%, p = 0.02).The rate of revision hip arthroscopy was significantly higher in the worker's compensation/no fault cohort than the commercial insurance cohort (15.6% vs. 3.5%, p < 0.01). The rate of conversion to total hip arthroplasty (THA) in the WC/NF cohort was not significantly different than the rate of conversion to THA in the commercial insurance cohort (0.0% vs. 3.2%, p = 0.30). CONCLUSION: Patients with WC/NF insurance may expect a significant improvement from baseline mHHS and NAHS following HA for FAIS at short-term follow-up. However, this improvement may not be as durable as those experienced by patients with CI. Additionally, WC/NF patients should be counseled that they have a higher risk of undergoing revision hip arthroscopy than similar CI patients. LEVEL OF EVIDENCE: III, Retrospective Comparative Prognostic Investigation.

Unveiling the Stacking Fault-Driven Phase Transition Delaying Cryogenic Fracture in Fe-Co-Cr-Ni-Mo-C-Based Medium-Entropy Alloy.

In this work, the tensile deformation mechanisms of the Fe55Co17.5Cr12.5Ni10Mo5-xCx-based medium-entropy alloy at room temperature (R.T.), 77 K, and 4.2 K are studied. The formation of micro-defects and martensitic transformation to delay the cryogenic fracture are observed. The results show that FeCoCrNiMo5-xCx-based alloys exhibit outstanding mechanical properties under cryogenic conditions. Under an R.T. condition, the primary contributing mechanism of strain hardening is twinning-induced plasticity (TWIP), whereas at 77 K and 4.2 K, the activation of martensitic transformation-induced plasticity (TRIP) becomes the main strengthening mechanism during cryogenic tensile deformation. Additionally, the carbide precipitation along with increased dislocation density can significantly improve yield and tensile strength. Furthermore, the marked reduction in stacking fault energy (SFE) at cryogenic temperatures can promote mechanisms such as twinning and martensitic transformations, which are pivotal for enhancing ductility under extreme conditions. The Mo4C1 alloy obtains the optimal strength-ductility combination at cryogenic-to-room temperatures. The tensile strength and elongation of the Mo4C1 alloy are 776 MPa and 50.5% at R.T., 1418 MPa and 71.2% in liquid nitrogen 77 K, 1670 MPa and 80.0% in liquid helium 4.2 K, respectively.

A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions.

Ensuring the smooth operation of rolling bearings requires a precise fault diagnosis. Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands.

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels.

Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current-Vibration Signals.

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current-vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current-vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.