International Journal of Reliability, Quality and Safety Engineering

In this paper, we propose a computing algorithm to obtain optimal checking times which minimize the expected cost rate of sequential inspection models. This algorithm is applied to two modified inspection models with finite intervals such that the unit is checked at times $T_k$ ($k=1,2,\dots ,N$) for given $N$ and given $S\equiv T_N$. The modified algorithms are proposed and optimal checking times to minimize the expected cost rates are obtained. Numerical examples of each model are given to show how to use the algorithm. This method would be applied to other sequential inspection models by its suitable modifications.

In general, a system deteriorates due to internal physical degradation and external random shocks. Previous studies mainly focused on establishing dependent competing risk models to evaluate mutual effects of degradation processes and random shocks affecting system health. However, there is a lack of consideration about the magnitude of impacts caused by random shocks on degradation processes. Thus, a shock-loading-based degradation model is proposed to classify the magnitude of impacts from random shocks on degradation processes based on the threshold of the cumulative shock loading. Copula methods are utilized to derive joint reliability function from multiple marginal distributions of degradation processes. Two numerical examples are utilized to demonstrate the reliability prediction performance of the proposed model. First, a simulated example is used. The second example employs the turbofan engine degradation data from the NASA Prognostic Data Repository to show the performance of the proposed shock-loading-based degradation process and its corresponding system reliability model.

When analyzing cybersecurity datasets with machine learning, researchers commonly need to consider whether or not to include Destination Port as an input feature. We assess the impact of Destination Port as a predictive feature by building predictive models with three different input feature sets and four combinations of web attacks from the CSE-CIC-IDS2018 dataset. First, we use Destination Port as the only (single) input feature to our models. Second, all features (from CSE-CIC-IDS2018) are used without Destination Port to build the models. Third, all features plus (including) Destination Port are used to train and test the models. All three of these feature sets obtain respectable classification results in detecting web attacks with LightGBM and CatBoost classifiers in terms of Area Under the Receiver Operating Characteristic Curve (AUC) scores, with AUC scores exceeding 0.90 for all scenarios. We observe the best classification performance scores when Destination Port is combined with all of the other CSE-CIC-IDS2018 features. Although, classification performance is still respectable when only using Destination Port as the only (single) input feature. Additionally, we validate that Botnet attacks also have respectable AUC with Destination Port as the only input feature to our models. This highlights that practitioners must be mindful of whether or not to include Destination Port as an input feature if it experiences lopsided label distributions as we clearly identify in this study. Our brief survey of existing CSE-CIC-IDS2018 literature also discovered that many studies incorrectly treat Destination Port as a numerical input feature with machine learning models. Destination Port should be treated as a categorical input value to machine learning models, as its values do not represent numerical values which can be used in mathematical equations for the models.

This paper explores the impacts of reviewing the time to replace of spare parts under the lenses of reliability- and cost-based optimization with the attempt to reduce the cost of a maintenance plan within a harshly constrained maintenance tasks’ scheduling problem. A hierarchical procedure for a robust reliability prediction and scheduling of maintenance tasks is introduced to this purpose.

This study takes inspiration from real applications of the preventive maintenance (PM) scheduling process on high-throughput packing and manufacturing lines counting hundreds of functional groups and thousands of components subject to failures and replacements. We investigate the role and the criticality of data entry in the not polynomial (NP)-hard cost-based and reliability-based scheduling problem proposed by Manzini et al. (R. Manzini, R. Accorsi, T. Cennerazzo, E. Ferrari and F. Maranesi, The scheduling of maintenance. A resource-constraints mixed-integer linear programming (MILP) model, Comput. Ind. Eng. 87 (2015) 561–568).

The proposed reliability-based analysis aids the setting of proper time to failures for spare parts and combines with an analytical single-component and cost-based model to set which maintenance tasks are the most sensitive to the overall cost reduction of a maintenance plan.

In this paper, it is aimed to propose a notion of the alternating renewal process to mathematically model a multi-failure complex system in order to develop its availability and maintenance measures. In the study, M/E₂/1 queueing model with infinite waiting space during service times of components has been worked out under the FCFS discipline. Inspections are performed at regular intervals to detect breakdowns. The primary objective of the paper is about obtaining the system’s reliability, availability, and the optimal interval period with minimum maintenance cost. To validate the mentioned model a case study has been done on the Power Distribution System. Finally, a comparative study between M/E₂/1 and M/M/1 queuing models is done and the various aspects based on availability and long-run cost are focused in the analytical result. The results were direct toward enhancing the availability of the system and making the decision about an appropriate inspection period.

The Laplace transform order of reversed residual life, introduced by [I. Elbatal, The Laplace order and ordering of reversed residual life, Appl. Math. Sci. 36 (2007) 1773–1788.] is used in the stochastic comparison of two life distributions. In this paper, we aim to study new notions of stochastic comparisons based on bivariate Laplace transform order of reversed residual lives. We investigate relationships the new stochastic order has with other existing bivariate orders. The interpretation of the new orders and their applications in different contexts are also pointed out.

Predictive models for work-related injuries in mines are complicated, with personal, technical, and societal aspects all playing a role. Numerous studies have reported that human behavior is a significant factor in mine injuries. Many researchers have opined that maintaining a high level of safe human behavior is a challenging task. In Indian mines, the risk management team uses hierarchy control charts to avoid incidents, but managing human behavioral factors which lead to injury is more challenging. Owing to these constraints, we attempted to find the significant human behavioral factors that lead to injury in mines. In this work, we try to develop predictive models using behavioral and demographic factors to predict mine workers’ injury status and evaluate the most significant factor. To investigate the relevance of behavioral characteristics in work-related injuries, we employed three machine learning models (neural networks, random forest, and K-NN) as well as two classic statistical approaches (linear and logistic regression) to compare their results and see which method performs better. We have collected data of six factors and injury status of 186 workers through a questionnaire survey. After data analysis, a detailed comparison between machine learning models and traditional methods shows that random forest performs better than other models. This study also found that demographic features and job dissatisfaction attitude are the leading influencing factors to work-related injuries. This work is beneficial to mine management in the present Indian mining scenario to devise appropriate countermeasures.