Parameter Weibull Distribution Research Articles

Synergetic engineering of oxidizable, redox, and inert metal decorated copper oxide for non-volatile memory and neuromorphic computing applications

Abstract In the quest for efficient resistive switching (RS) materials for both non-volatile memory and neuromorphic computing applications, a variety of functional materials have been researched in the last few years. Herein, we systematically synthesized Ni, Ag, and Au decorated copper oxide (CuxO) by using an electrochemical approach and investigated their RS performance for both non-volatile memory storage and neuromorphic computing applications. By tuning various electrochemical parameters, we optimized Ni, Ag, and Au decoration over the CuxO switching layers to understand the effect of oxidizable, redox, and inert metal decoration, respectively. Fabricated Ni-CuxO/FTO, Ag-CuxO/FTO, and Au-CuxO/FTO devices show forming-free bipolar and analog properties of RS behavior. The electrical measurements asserted that the electrodeposited Ni-CuxO/FTO RS device shows excellent RS, non-volatile memory, and synaptic learning properties compared to the Ag-CuxO/FTO, and Au-CuxO/FTO devices. Moreover, the statistical and Weibull distribution parameters suggested that the Ni-CuxO/FTO RS device has lower switching variation than the other two devices. The conduction mechanisms of all devices are investigated by fitting the appropriate physics-oriented models. It was found that Ohmic and Child's square law were dominated during the charge transport and the RS process occurred due to the filamentary switching effect. Results suggested the electro-decorated/-deposited Ni-CuxO is a suitable switching layer for memory as well as neuromorphic computing applications.

Particle crushing mechanism of recycled aggregate with different component categories: applied to pervious concrete base

This paper focuses on the particle crushing mechanism of construction solid waste recycled aggregate (CWRA), particularly recycled concrete (RC) and recycled bricks (RB), using natural gravel (NG) as a control group. Compression tests, combined with the Weibull distribution, fractal dimension theory, and relative crushing rate, were conducted to investigate the crushing behavior of these materials under different loads. Results indicate that, under the same load, the crushing degree ranks from highest to lowest as RB, RC, and NG. The Weibull distribution effectively describes the crushing state, with “fragmentation” being the primary mode for most materials, while RC also exhibits “grinding.” As the load increases, the relative crushing rate rises for all materials but slows beyond a certain load threshold, indicating greater difficulty in further crushing. The study identifies a load threshold of 400 kN, beyond which changes in void fraction and relative crushing rates become less significant, particularly in stronger aggregates. Highlights Identified that mortar attached to the RC surface is the primary cause of the increased particle crushing below 0.6 mm. Introduced Weibull distribution parameters a and b to accurately characterize the crushing state of RA particles. Revealed the errors between fractal dimension-based evaluations and the actual crushing state of RC particles. Proposed a two-stage crushing mode for RC. Determined the critical threshold value for the variation of crushing degree with load level.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

A Comparison of Parameters of Weibull Distribution on Nigeria Stock Prices

This paper compared 2-parameter and 3-parameter Weibull Distribution using monthly average stock price returns of five different bottling companies enlisted in the Nigerian Stock market comprising Nigerian Breweries PLC, Coca-Cola Bottling Company, Guinness Nigeria PLC, Seven Up Bottling Company and International Breweries from 2012 to 2016 obtained from www.market.ft.com/data/equities/tearsheet/historical. The parameters were obtained using the Method of Moments (MOM) and were compared by the means of minimum Mean Squared Error; the result shows that Coca-Cola bottling company stock price return performed better under 2-parameter Weibull estimate while International Breweries was a choice under 3-parameter Weibull estimate. Also, the goodness-of-fit test was conducted using Kolmogorov-Smirnov Test and the result reviewed that the price of the stock returns does not follow a Weibull Distribution (P-value<0.05).

Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

This study assesses the efficacy of particle swarm optimization (PSO) in estimating scale ( c) and shape ( k) parameters of the Weibull distribution model for wind energy forecasting at two key wind farm sites in Morocco— Tarfaya in the south and Tangier in the north, utilizing real wind data from 2022. Employing a novel square frequency error objective function to enhance parameter accuracy, the study adopts a two-stage training approach involving recursive least-square estimation and PSO fine-tuning. Validation with artificial data underscores PSO’s effectiveness under diverse wind conditions. Parameter sensitivity analysis identifies four optimal PSO configurations, with the PSO-4 model exhibiting superior performance. Comparative analysis against traditional and heuristic optimization methods consistently demonstrates PSO-4’s lowest root-mean-square error (RMSE) and mean absolute error (MAE), high coefficients of determination ( R2), and shortest computation time. The research highlights PSO-4 model as a precise and efficient tool for Weibull distribution parameter estimation in wind energy forecasting, showcasing robust convergence across both wind farm sites.

Optimal power density estimation in Sub-Saharan Africa regions based on Weibull distribution parameters

The production of wind energy requires knowledge of certain wind speeds and directions. Several tools are used for this purpose to characterize the wind power, including the Weibull distribution function with two parameters, shape factor k, and the scale factor c. In this paper six methods Graphical Method (GP), Empirical Method of Justus (EMJ), Empirical Method of Lysen (EML), Energy Pattern Factor (EPF), Maximum Likelihood (ML), and Moroccan Method (MMa) are used to estimate Weibull distribution parameters to evaluate the wind potential an its power density. Twelve sites in West African sub-region such as Abuja, Accra Kotoka, Bamako Senou, Conakry Gbessia, Cotonou Cadjehoun, Kano Mallam Aminu, Lagos Ikeja, Lome Tokoin, Niamey, Niamtougou, Ouagadougou, Tambacounda were selected as case studies. For each selected site, hourly wind speed data collected at 10 m height for the twelve years from January 2011 to December 2023 are used. The evaluations of each method were carried out every month and statistical criteria to provide a more complete analysis. The results show that the EPF, EMJ, EML, and ML provide highly desirable better performance while the GP and MMa showed poor performance for all stations. For all sites, the EPF was recognized as the most appropriate method except for the Lagos site where the EMJ ranks first on the others. The methods that ranked second after EPF varied among the sites.

Disproportionality analysis of cardiac adverse events associated with lenvatinib using the Japanese Adverse Drug Event Report database.

This study was conducted to examine disproportionality, times to onset, incidence rates and outcomes of lenvatinib-associated cardiac adverse events (AEs) using the Japanese Adverse Drug Event Report database. We analysed data for the period between April 2004 and May 2023. Data on cardiac AEs were extracted and the relative disproportionality of AEs was estimated using reporting odds ratios (RORs). Furthermore, Weibull distribution parameters were calculated. Of the 2 230 863 reports analysed, we identified 7684 reports of AEs associated with lenvatinib, including 317 cardiac AEs. Signals were detected for eight cardiac AEs: hypertension, cardiac failure, myocarditis, myocardial infarction, immune-mediated myocarditis, cardiomyopathy, angina unstable and cardiotoxicity. Among these, fatal outcomes were observed for cardiac failure, myocarditis and myocardial infarction. Histograms of median times to onset for the eight detected cardiac AE signals showed that AEs occurred at a median of 3.5-134.5days after lenvatinib administration. The Weibull distributions showed that cardiac failure occurred early after administration (early failure type), myocarditis occurred in a dose-dependent manner (wearout failure type), and myocardial infarction occurred constantly throughout the exposure period (random failure type). We focused on cardiac AEs associated with lenvatinib as post-marketing AEs. Serious outcomes can arise after lenvatinib administration. Patients should be monitored for signs of onset of these AEs not only at the start of administration, but also over an extended period.

An Optimal Strategy for Estimating Weibull distribution Parameters by Using Maximum Likelihood Method

Several methods have been used to estimate the Weibull parameters such as least square method (LSM), weighted least square method (WLSM), method of moments (MOM), and maximum likelihood (MLE). The maximum likelihood method is the most popular method (MLE). Newton-Raphson method has been applied to solve the normal equations of MLE’s in order to estimate the Weibull parameters. The method was used to find the optimal values of the Weibull distribution parameters for which the log-likelihood function is maximized. We tried to find the approximation solution to the normal equations of the MLE’s because there is no close form for get analytical solution. In this work, we tried to carry out a study that show the difference between two strategies to solve the MLE equations using Newton-Raphson algorithm. Both two strategies are provided an optimal solution to estimate the Weibull distribution parameters but which one more easer and which one converges faster. Therefore, we applied both strategies to estimate the Weibull’s shape and scale parameters using two different types of data (Real and simulation). We compared between the results that we got by applying the two strategies. Two studies have been done for comparing and selecting the optimal strategy to estimate Weibull distribution parameters using maximum likelihood method. We used some measurements to compare between the results such as number of steps for convergence (convergence condition), the estimated values for AIC, BIC and the RMSE value. The results show the numerical solution that we got by applying first strategy convergence faster than the solution that we got by applying second strategy. Moreover, the MRSE estimated by applying the first strategy is lower than the MRSE estimated by applying second strategy for the simulation study with different noise levels and different samples size.

Fitting COVID-19 datasets to a new statistical model

This paper discussed gull alpha power Weibull distribution with a three-parameter. Different statistical inference methods of Gull Alpha Power Weibull distribution parameters have been obtained, estimated, and evaluated. Then, the results are compared to find a suitable model. The unknown parameters of the published Gull Alpha Power Weibull distribution are analyzed. Seven estimation methods are maximum likelihood, Anderson–Darling, right-tail Anderson–Darling, Cramér–von Mises, ordinary least-squares, weighted least-squares, and maximum product of spacing. In addition, the performance of this distribution is computed using the Monte Carlo method, and the limited sample features of parameter estimates for the proposed distribution are analyzed. In light of the importance of heavy-tailed distributions, actuarial approaches are employed. Applying actuarial criteria such as value at risk and tail value at risk to the suggested distribution shows that the model under study has a larger tail than the Weibull distribution. Two real-world COVID-19 infection datasets are used to evaluate the distribution. We analyze the existence and uniqueness of the log-probability roots to establish that they represent the global maximum. We conclude by summarizing the outcomes reported in this study.

Experimental study on the effect of reaming on the fatigue life of split-sleeve cold-expanded Ti–6Al–4V alloy components

The split-sleeve cold expansion technique has been widely used in the aerospace industry to improve the fatigue life of metal components with holes. Due to the morphology change after the expansion, a reaming process is necessary to improve the machining accuracy of the hole surface. This final reaming process might change the residual stress distribution around the hole significantly but the effect of the reaming ratio has not been fully explored. This paper investigated the effect of reaming depth on the fatigue life of split-sleeve cold-expanded Ti–6Al–4V alloy components using a series of experiments, including the residual stress measurements, uniaxial fatigue tests, and fracture morphology observations. The expectations of the fatigue cycles were estimated using the 2-parameter Weibull distribution and the S-N curves of different components were acquired. Residual stress measuring results and the fracture surface observations explained the effect of cold expansion and reaming on the components. The experimental results showed that the fatigue life of the components decreased with the increase of the reaming ratio, demonstrating that excessive reaming depth is harmful to the fatigue performance of the cold-expanded components.

A Bayesian approach to estimate the capacity factor of offshore wind farms on the Brazilian coast

This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The calculation of the capacity factor depends on the power curve model and the probabilistic wind speed model, which in this work follows a Weibull distribution, whose scale and shape parameters are estimated using a Bayesian approach where the priors are independent gamma distributions, the likelihood is the Weibull distribution, and the posteriors are obtained by Hamiltonian Monte Carlo. The application of the described methodology is illustrated through a case study of an offshore wind power project located on the Northern coast of the Rio de Janeiro State, Brazil.

A Theoretical Analysis of Meteorological Data as a Road towards Optimizing Wind Energy Generation

The development of wind energy has been observed for many years. Both construction firms and the scientific world are analyzing new design solutions, atmospheric conditions and the technical performance achieved. The main goal of this research is to evaluate the requirements that have to be met to design wind power stations that would be an optimal fit for the climatic conditions in Poland. This study combines the results of empirical studies on wind velocity distributions with the physical fundamentals of wind power station design. This paper presents modelling of the relationships between wind velocity distributions observed in Poland and technical requirements for wind power stations design. The wind velocities distributions for various locations in Poland are determined and expressed in Weibull distribution parameters. Theoretical computations concerning the dependence of wind power stations as function of wind speed and air’s physical properties are presented. Conclusions important for the design of power stations fitted to the atmospheric conditions in Poland are given. LabVIEW 2021 was used for computer modeling.

Mathematical Differential Analysis of Atlantic Ocean Wind to Electrical Energy Generation in Lekki Peninsular Lagos Nigeria

This article is a pragmatic approach on the evaluation of wind energy production and it’s estimation in the coastal area of Lagos Nigeria. The work focuses on the accessibility of wind energy production in Lagos Nigeria through analyzing wind data in the look up tables using proficient probability function. Here, three approaches are itemized; Analysis of sets of actual time series data, theoretical Weibull probability function using seven numerical methods and the comparison of theory and the analysis. Two important parameters are used in this analysis are the Weibull shape factor “k” and Weibull scale factor “c”. Theory involves the calculation using seven popular methods of moments (MM), standard deviation method (STDM) or empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM) or least mean square method (LSM), energy pattern factor method (EPFM). The performance of the numerical methods has been tested by five methods; RMSE, X2, IA, MAPE and RRMSE. The results expatiate on Actual and theoretical technique being used to find out wind energy conversion per 1 km2. In this paper, a differential performance method for accuracy check has been proposed as an error indicator between the wind energy calculated by theoretical Weibull function and the one by actual time-series data. The wind speed data was measured from January 2018 to December 2021 in the Lekki peninsular area of Lagos State. The suitability values for these parametric; shape and scale parameters of Weibull distribution are determined in selecting the best location for the installation the wind turbine generators. The measured annual mean wind speed and mean wind power are 10.11 ms-1 and 10.4 KWm-2, respectively.

Assessment of Wind Energy Potential at Three Prime Locations in Saudi Arabia: Analysis of Sharma, Qurayyat and Sakaka Sites

Purpose: The research aims to assess the wind energy potential at three chosen sites in Saudi Arabia Sharma, Al Qurayyat, and Sakaka over a five-year period from 2019 to 2023. The main goal is to determine the most favorable locations for wind farm development based on wind speed characteristics, Weibull distribution parameters, and wind power density (WPD). Materials and Methods: The wind speed data for the chosen locations was obtained from NASA's wind website and analyzed using the Weibull distribution to determine the shape (k) and scale (c) parameters, which are indicative of wind speed distribution. Critical metrics such as mean wind speed, WPD, and the most probable wind speed were computed to evaluate the wind energy potential. The Vestas V150-4.2 MW turbine was utilized to estimate potential energy output, considering the variability and reliability of wind resources at the specified locations. The study utilized tables, figures, and wind rose diagrams to highlight important wind parameters like mean wind speed, Weibull parameters, WPD, and Vmax for each location. Flowcharts were also used to summarize the methodology and conclusions, ensuring a clear and comprehensive presentation of the data. Findings: The analysis showed that Qurayyat consistently had the highest mean wind speed and WPD, indicating excellent wind energy potential. Sharma and Sakaka also displayed significant wind resources, with high mean wind speeds and substantial WPD values. The Weibull parameters indicated that all three sites have favorable wind characteristics for energy production. The estimated energy output using the Vestas V150-4.2 MW turbine ranged from 12,809 to 17,807 MWh/yr at Site 18, 14,095 to 15,170 MWh/yr at Site 24, and 14,013 to 15,832 MWh/yr at Site 31. Implications to Theory, Practice and Policy: The study presents a thorough evaluation of the wind energy potential in the Al-Jawf and Tabuk regions of Saudi Arabia. It contributes to a deeper understanding of wind resource distribution and its significance for the development of renewable energy. The study provides valuable insights for wind farm developers and policymakers by identifying the most promising locations for investment in wind energy infrastructure. Furthermore, it emphasizes the need for detailed site-specific analyses to optimize wind energy deployment, aligning with Saudi Arabia's broader renewable energy objectives.

Performance evaluation of ten numerical methods for Weibull distribution parameter estimation applied to Nigerian wind speed data

Utilizing wind energy necessitates a thorough understanding of wind profiles as well as a precise forecast of wind speed at a study location. In this study, ten Numerical Methods (NEMs), which include the Empirical Method of Lysen (EML), Percentile Method (PCM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), Empirical Method of Justus (EMJ), Alternative Moment Method (AMM), Median and Quartiles Method (MQM), Probability Weighted Moments Based on Power Density Method (PWMBPM), Method of Mabchour (MOMAB) and Energy Variance Method (EVM) were applied to estimate the two- parameter (k and c) Weibull (Wbl) distribution in five locations (Jos, Kano, Maiduguri, Abuja, and Akure) in Nigeria. The performance of these NEMs was assessed using five different metrics and the most effective NEM was determined for each studied location. Daily wind speed data spanning 11 years for the studied locations were sourced from the Meteorological Agency in Nigeria and used in this study. The k and c parameters range from 2.91 to 5.46 and 9.95 to 10.26 (Kano); 2.31 to 4.50 and 5.63 to 6.20 (Maiduguri); 3.19 to 7.61 and 12.16 to 12.99 (Jos); 2.18 to 6.77 and 4.99 to 5.50 (Abuja), and 1.84 to 3.18 and 3.83 to 3.90 (Akure). Findings revealed that the best methods for estimating Wbl parameters for the Kano, Maiduguri, Jos, Abuja, and Akure locations were MMLM, MMLM, MQM, MQM, and EMJ, EML, and AMM, respectively, as MOMAB remained the least performing NEM for all the studied locations. The results also showed that the Vms , Vmps , and V emax varied from 3.47 m/s to 11.63 m/s, 3.40 m/s to 11.90 m/s, and 4.58 m/s to 12.59 m/s, respectively, with the most recorded for Jos. The PWPD augmented from 36.45 W/m2 (Akure) to 1000.06 W/m2 Jos), at a hub height of 10 m.Based on these results Jos was the best location for installing wind turbines while Kano was an excellent place for integrating the grid. Additionally, the Maiduguri location was determined to be suitable for a stand-alone application while Abuja and Akure were considered to be unsuitable for wind energy applications.

A new approach to estimate parameters of the two-parameter Weibull distribution

This study proposes a new approach to estimate the parameters of the two-parameter Weibull distribution in the case of complete (uncensored) or doubly Type II censored samples. The proposed estimators have closed forms and can be adapted to Type II right or Type II left censored samples. The new estimators are strongly consistent and the new shape estimator follows asymptotically a normal and the new scale estimator a lognormal distribution. Bias, mean square and efficiency performance of the new estimators are investigated through simulations and compared to some known estimation methods such as maximum likelihood estimation, method of moments, quantile estimation, maximum goodness of fit estimation, L-moments, U-statistics and bias-corrected maximum likelihood. According to the simulations, the newly proposed estimators seem to have good efficiency performance in comparison to the other methods, particularly in small samples. Furthermore, two real data sets are used to illustrate the new estimators.

Individual Wave Overtopping Volumes On Mound Breakwaters

Mound breakwaters are widely used to protect harbors from wave attack. Wave overtopping is a key parameter on the breakwater design, since it affects the hydraulic stability, the port operativity and also generates risks to the facilities, vehicles and pedestrians. The estimation of the mean wave overtopping rate, q[m3/s/m], has been extensively analyzed in the literature (see EurOtop, 2018 and Van Gent et al., 2007). However, the maximum individual wave overtopping volume, Vmax [m3/m], can be much larger than q[m3/s/m] and it is a better variable to evaluate the direct hazards. The prediction tools of q and Vmax are mainly based on laboratory tests, where q is registered much more easily than individual wave overtopping volumes. However, few of these studies detail the methodology to identify the number of overtopping waves and the associated individual wave overtopping volumes. The estimation of Vmax is usually made in literature (see Molines et al., 2019 and EurOtop, 2018) using a 2-parameter Weibull distribution (shape and scale factor) fitted with utility functions which consider the 10%, 30% or 50% of the highest individual wave overtopping volumes. The shape factor (b) is fitted with the laboratory measurements and the scale factor (A) is obtained by forcing the mean value of the Weibull distribution to be equal to the registered mean individual wave overtopping. In this study, a fully automatic detection methodology of the individual wave overtopping waves and volumes is developed using 2D physical tests. The performance of the 2-parameter Weibull and Exponential distributions to estimate Vmax is analyzed here with four utility functions to weight the data, f(u). The full findings of this study are available in Molines et al. (2019).

Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application

In this study, the unified hybrid censored approach is employed to estimate the parameters of the inverse Weibull distribution, as well as the survival and hazard rate functions. Parameter estimates are obtained using both Bayesian and Maximum Likelihood approaches, with Bayesian estimates acquired through Lindley's approximation method using three distinct balanced loss functions. These encompass both symmetric and asymmetric balanced loss functions, specifically the balanced squared error (BSE) loss function, the balanced linear exponential (BLINEX) loss function, and the balanced general entropy (BGE) loss function. We conduct a simulation study to compare the effectiveness of various estimators, and a real-world data analysis is presented to illustrate practical implementation. Ultimately, our findings indicate that Bayesian parameter estimates consistently outperform their Maximum Likelihood counterparts across all methods.

A solution to multi Objective Stochastic Optimal Power Flow problem using mutualism and elite strategy based Pelican Optimization Algorithm

Wind Energy is rapidly growing into one of the most popular energy options for power generation. However, the unpredictability of wind necessitates the employment of a distribution function when describing the wind environment. In the depiction of wind speeds, the two significant parameters of the Weibull distribution, such as the scale parameter and the shape parameter are often utilized. Therefore, it is essential to choose the most appropriate approach for estimating these parameters. Furthermore, the uncertainty in delegated power from variable wind speeds makes system management challenging. In this paper, the Weibull distribution parameters were determined using the Pelican Optimization Algorithm (POA). POA has gained extensive popularity among the research community and is currently used to address a wide range of optimization problems. However, the exploration and exploitation are not properly balanced in this algorithm. To overcome this problem a novel hybrid algorithm namely Mutualism phase based POA (MPOA) is proposed with the help of mutualism phase of Symbiotic Organisms Search (SOS) algorithm. To demonstrate the efficacy and robustness MPOA is applied on multi-objective Optimal Power Flow (MO-OPF) problems. For this study IEEE 30 bus standard system and modified IEEE 30 bus system consisting of two wind farms are taken. The purpose of this work is to examine the impact of changing the Weibull parameters, reverse and penalty cost coefficients on the overall generation cost while considering all the security aspects.

Reliability Model of Medical Equipment in University of Port Teaching Hospital

In this research, on medical equipment maintainability and reliability we conducted basic statistics analysis using University of Port Harcourt Teaching Hospital as the case study; the data collected covered 18 departments, namely; Anatomical Pathology, Micro Biology, Chemical Pathology Laboratory, Radiography Department, Pediatrics, Hemodialialysis, Hematology and Blood Transfusion, Physiotherapy, Dental Department, MDR-TB unit, Pharmacy, ICU, Assisted Conception Unit, Orthopedic Ward, Care for Elderly Laboratory, Family Planning Unit, Community Medicine and Labour Ward. The results of the parametric Weibull distribution percentile suggested that the reliability of the devices tends to fail every 21 days. The reliability plot of the model indicated that the devices tend to decrease its life span with age, the Weibull model was adequately fitted following the results of Anderson adjust test of goodness of fit and the probability plot. In comparison, the Probability value of goodness of fit P(0.0000) of Weibull distribution model was compared with that of exponential distribution model P(0.034), the outcome showed that Weibull distribution is better to model the data of medical equipment in University of Port Harcourt Teaching Hospital.