Reliability modeling of three-state systems with multiple types of components

In this paper, a system consisting of three states: perfect functioning, partial functioning, and down is considered. The system is assumed to be composed of several non-identical groups of binary components. The reliability of the system states under various assumptions on the component lifetimes is investigated. For this purpose, first, a new concept of bivariate survival signature (BSS) is introduced. Then, under the assumption that the component lifetimes of each type are exchangeable dependent, representations for the joint reliability function of the state lifetimes are obtained based on the notion of BSS. In the particular case, three-state systems composed of two types of different modules such as general-series (parallel) systems and systems with component-wise redundancy are investigated. Several examples are presented to illustrate the theoretical results.

Performance reliability analysis of multi-state degraded system with improved Lz transform

Universal Generating Functions and Lz transformations have been widely used in the reliability modeling of multi-state systems. In order to solve the problem of complex calculations due to the dense random combination of multi-state performance parameters in the Lz transformation, a screening function is defined before the Lz transformation, and the screening function is combined with the performance threshold to screening the state performance parameters in advance, and the process is simplified through the screen matrix and the screen block diagram, effectively reduce the combined dimensions and quantity, improve the efficiency of reliability analysis, and combine with specific examples for application verification.

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

This paper presents a general testing coverage software reliability modeling framework that covers imperfect debugging and considers not only fault detection processes (FDP) but also fault correction processes (FCP). Numerous software reliability growth models have evaluated the reliability of software over the last few decades, but most of them attached importance to modeling the fault detection process rather than modeling the fault correction process. Previous studies analyzed the time dependency between the fault detection and correction processes and modeled the fault correction process as a delayed detection process with a random or deterministic time delay. We study the quantitative dependency between dual processes from the viewpoint of fault amount dependency instead of time dependency, then propose a generalized modeling framework along with imperfect debugging and testing coverage. New models are derived by adopting different testing coverage functions. We compared the performance of these proposed models with existing models under the context of two kinds of failure data, one of which only includes observations of faults detected, and the other includes not only fault detection but also fault correction data. Different parameter estimation methods and performance comparison criteria are presented according to the characteristics of different kinds of datasets. No matter what kind of data, the comparison results reveal that the proposed models generally give improved descriptive and predictive performance than existing models.

Improving quality of maintenance task for milk powder manufacturing unit through TOPSIS

PurposeThe purpose of this paper is to explore the possibilities of improving the quality of existing maintenance task of the atomizer of milk powder manufacturing unit of a dairy plant. Looking to the past business volume and expected growth, the milk powder manufacturing unit forms a noticeable sector of processing plant. The lack of quality in maintenance standards leads to reliability losses of about 20–25% with low productivity and profit. Such facts and challenges of keeping the system in ready-state motivate a definite maintenance plan to be modeled based on a live failure analysis to be executed during shutdown or scheduled period.Design/methodology/approachThe deliverables are achieved by collecting the historical failure data i.e. downtime and failure frequencies; from January 2020 to July 2020 at Dudhsagar dairy, Gujarat, India. Reliability modeling is done in a view to understand the failure pattern behavior of the milk powder manufacturing unit. The atomizer is discriminated as a critical component based on these data and their functional failures, failure causes, effects and repercussions of failures with existing control and maintenance practices has been modeled based on live shop-floor study. Scores are assigned on 1 to 10 levels by analyzing attributes effects from lowest to highest concern respectively for every modes of failure through realistic brain-storming among maintenance team by incorporating some advanced attributes like maintainability, economic safety, economic cost and spares with basic criteria in this study. The maintainability criticality index (MCI) is narrated by these score values through multi-attribute decision-making (MADM) based failure analysis models like Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).FindingsThe primary findings of this research work are to propose improvements in the quality of the maintenance plan of critical component like; atomizer of a milk powder manufacturing unit which is commonly representing critical component in a major range of industrial processes. The case study recommended four silent maintenance strategies i.e. scheduled maintenance scheduled discard, scheduled failure finding and redesign as a qualified maintenance plan for the atomizer based on MCI and rankings of its potential failure causes. The results are helpful in upgrading quality standards for the maintenance activities of a process industry of alike or of dissimilar kinds in accordance with the failure analysis.Originality/valueOriginality mainly consists of investigating the scope of enhancing the existing maintenance practices through actual failure analysis with the help of TOPSIS. The criteria employed in this study are probability of chances of failure, degree of detectability and degree of severity as basic criteria along with some advanced criteria like; maintainability, spare parts, economic cost, economic safety are selected based on the outcome of shop-floor study and reliability modeling. The notable past failure statistics (downtime, frequency of failures) of a milk powder manufacturing unit were recorded and these data are analyzed based on reliability to extract an explicative component i.e. atomizer.