Journal of Reliability and Statistical Studies

Evaluation of Machine Learning Algorithms for Air Quality Index (AQI) Prediction

Alka Pant, Sanjay Sharma, Kamal Pant — Tue, 26 Dec 2023 00:00:00 +0100

The Air Quality Index (AQI) has been deteriorated due to the growth of industry and automobiles in many regions of India. Artificial intelligence and machine learning have greatly benefited the ability to predict air quality. This paper aims to know the status of air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) monitored in different cities of Uttarakhand State (India) and the Air Quality Index (AQI) using the Python language (Jupyter Notebook). The air quality index dataset has used six machine-learning algorithms (Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Decision Tree). These machine-learning algorithms have been evaluated based on precision, recall, accuracy, etc. The result shows that Random Forest and Decision Tree algorithms outperformed each other and achieved the highest accuracy, i.e., 99.0%. Further, the air quality index (AQI) values have also been predicted and compared to actual values using the random forest algorithm.

Interval Estimation of the Stress-Strength Reliability in Lehmann Family of Distributions

Sanju Scaria, Sibil Jose, Seemon Thomas — Tue, 26 Dec 2023 00:00:00 +0100

This paper presents a unified approach for computing confidence limits for stress–strength reliability when strength and stress are independent random variables following a distribution in Lehmann family. The generalized confidence interval and the bootstrap confidence intervals are obtained. Simulation studies are conducted to assess the performance of the proposed methods in terms of the estimated coverage probabilities and the length of the confidence intervals. An example is also provided for illustration.

Reliability Analysis with New Sine Inverse Rayleigh Distribution

Ikwuoche John David, Mathew Stephen, Eghwerido Joseph Thomas — Tue, 26 Dec 2023 00:00:00 +0100

This article examined some of the characteristics of the New Sine Inverse Rayleigh Distribution. There is just one scale parameter in the New Sine Inverse Rayleigh distribution. The raw moments, reliability analysis, and other aspects of the New Sine Inverse Rayleigh Distribution have been derived. The maximum likelihood approach was used to estimate the New Sine Inverse Rayleigh Distribution’s parameters. Utilizing simulation, the distribution’s stability was examined, and the applicability of the distribution was demonstrated using three data sets. The analysis’s findings demonstrated that the New Sine Inverse Rayleigh Distribution behaves well and fits the data more closely than other probability distributions.

Generalized Estimator of Population Mean Using Auxiliary Information in Presence of Measurement Errors

Peeyush Misra — Tue, 23 Jan 2024 00:00:00 +0100

It is assumed in survey research that the respondent’s reported response is precise. More often, due to prestige bias, the data provided by respondents frequently include estimates that are significantly different from the genuine values. As a consequence, measurement error is present in the sample estimates that may affect the results. Therefore, this study illustrates an improved generalized estimator that utilizes auxiliary data under measurement error. A numerical study to establish its effectiveness is also conducted.

An Analysis of the Mean Chart Under OC Function for Correlated Data

Manzoor A. Khanday, Shiv Shankar Pandey, Akansha Rawat, Chukka Sowjanya — Mon, 12 Feb 2024 00:00:00 +0100

In this paper, we determine and illustrate the effects of correlation between the observations on the operating characteristics curve, Type-I error and Average Run length. In addition, for different correlated coefficient the control limits have been developed. To study the effect of correlated observations the OC curves, Type-I error, ARL and factor A have been worked out using various equation and values are given in Tables 1 to 4. To give a visual comparison of OC function and ARL, curves have been drawn in Figures 1 to 6. It is found that correlation between observations seriously affected the OC, Type-I error, ARL and factor A for the mean chart when standards are known. When the center line and control limits are based on the large value. Thus, it will be healthy contribution in manufacturing process which tracks important product characteristics in industry.

Reliability Analysis and Life Cycle Cost Optimization of Hydraulic Excavator

Sandeep Kumar Mishra, Neeraj Kumar Goyal, Arup Mukherjee — Sun, 17 Mar 2024 00:00:00 +0100

This study focuses on conducting a reliability analysis of an excavator from its field failure data and improve its reliability cost effectively. The aim of the research is to perform reliability estimation of systems and identify the critical subsystem with significant contributions to system unreliability. The reliability analysis was performed using repairable system data analysis approaches. Life cycle cost (LCC) was estimated for critical subsystems, and it was optimized to select cost effective reliability improvement strategy. The results of the study provide valuable insights into the performance and cost-effectiveness of the excavator and its subsystems, which can assist manufacturers and operators in optimizing their equipment’s reliability, availability while considering the cost implications over the life cycle of the equipment. The results show that the undercarriage has critical contributions to system unreliability. This study attempts deep down reliability analysis of critical undercarriage components for optimal selection of improvement method among feasible alternatives. LCC analysis and its optimization performed on the critical sub-system is expected to help OEM save approx. 15% of LCC. The empirical data used in the paper is based on field data gathered during the operational life of the hydraulic excavator over approx. six years in its Indian operations.

An Improvement in Regression Estimator Through Exponential Estimator Using Two Auxiliary Variables

Sachin Malik, Kanika, Atul — Sun, 17 Mar 2024 00:00:00 +0100

For the case of simple random sampling, we are introducing a new regression estimator for the population mean with the supporting values of two auxiliary variables. The results for the mean square error (MSE) of the new form of regression estimator is fined. The mean square error’s results have also been checked through numerical illustration. It is observed that our introduced estimator is having less mean square error than the traditional ratio and regression estimator for two auxiliary variables.

Effect of Load on Sequential Imperfect Preventive Maintenance and Replacement Schedules of Mechanically Repairable Machines

Nse Udoh, Iniobong Uko, Kufre Bassey — Wed, 27 Mar 2024 00:00:00 +0100

This paper examines the impact of load on the operational time and maintenance cost of mechanically repairable machines. Three different levels of load with multiplicative impact on the hazard rate of the failure distribution were applied to the working of a cassava grinding machine using a two-parameter Weibull distribution with respective hazard and cumulative hazard functions. Their effect on the preventive maintenance (PM) and replacement schedules revealed that at above maximum load level, the length of the machine’s operational time decreased drastically compared to the decrease at maximum load level and relative decrease at the below maximum load level when compared to the machine’s operational time at the minimum load level. The application of load also results in frequent preventive maintenance actions and an increase in machine downtime for a given cost ratio. This implies that the influence of load on the PM and replacement maintenance schedule of mechanically repairable machines is essential to the design and operation of such machines. The results also provide maintenance engineers with an operational guide for PM and replacement maintenance actions in order to prevent failure maintenance and increase the machine’s availability for enhanced productivity.