ReliaSoft Corporation

Tucson, AZ, United States

ReliaSoft Corporation

Tucson, AZ, United States
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Guo H.,ReliaSoft Corporation | Liao H.,University of Tennessee at Knoxville
IEEE Transactions on Reliability | Year: 2012

Reliability Demonstration Testing (RDT) has been widely used in industry to verify whether a product has met a certain reliability requirement with a stated confidence level. To design RDTs, methods have been developed based on either the number of failures or the failure times. However, practitioners often have difficulty in determining which method to use for a specific design problem. In particular, the method based on the number of failures cannot be used when all the units are tested to failure, while the alternative based on failure times falls short in dealing with cases where no failures are expected. This paper elaborates on the two methods, and compares them from both practical and theoretical standpoints. The detailed discussions regarding the relationship between the two methods will help practitioners design RDTs, and understand when the two methods will lead to similar designs. A Weibull distribution is used in the relevant mathematical derivations, but the results can be extended to other widely used failure time distributions. Case studies are provided to demonstrate the use of the two methods in practice, and in developing equivalent RDT designs. © 2006 IEEE.


Pulido J.,ReliaSoft Corporation
Proceedings - Annual Reliability and Maintainability Symposium | Year: 2014

Every company is dependent on some type of asset that keeps the business in business - be it a computer, a centrifuge or a megawatt transformer. In a large enterprise, reducing costs related to asset maintenance, repair and ultimate replacement is at the top of management concerns. Downtime in any network, manufacturing or computer system ultimately results not only in high repair costs, but in customer dissatisfaction and lower potential sales. In response to these concerns, this paper presents a methodology for using Accelerated Life Testing techniques for evaluating and projecting preventive maintenance schedules. It provides valuable guidelines for planning an enterprise system that monitors critical maintenance processes and assets. The paper also presents an application in a turbine where the technique was used in determining the right maintenance interval. © 2014 IEEE.


Guo H.,ReliaSoft Corporation | Paynabar K.,University of Michigan | Jin J.,University of Michigan
IIE Transactions (Institute of Industrial Engineers) | Year: 2012

This article proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA(2, 1) model structure. The Haar wavelet transform is used to obtain effective monitoring statistics by considering the process dynamic characteristics in both the time and frequency domains. Three control charts are developed on three selected levels of Haar wavelet coefficients in order to simultaneously detect the changes in the process mean, process variance, and measurement error variance, respectively. A systematic method for automatically determining the optimal monitoring level of Haar wavelet decomposition is proposed that does not require the estimation of an ARMA model. It is shown that the proposed wavelet-based Cumulative SUM (CUSUM) chart on Haar wavelet detail coefficients is only sensitive to the variance changes and robust to process mean shifts. This property provides the separate monitoring capability between a variance change and a mean shift, which shows its advantage by comparison with the traditional CUSUM monitoring chart. For the purpose of mean shift detection, it is also shown that using the proposed wavelet-based Exponentially Weighted Moving Average (EWMA) chart to monitor Haar wavelet scale coefficients will more successfully detect small mean shifts than direct-EWMA charts. © 2012 Copyright Taylor and Francis Group, LLC.


Mettas A.,ReliaSoft Corporation
International Journal of Performability Engineering | Year: 2010

Design for Reliability (DFR) is not a new concept, but it has begun to receive a great deal of attention in recent years. What is DFR? What are the ingredients for designing for reliability, and what is involved in implementing DFR? Should DFR be part of a Design for Six Sigma (DFSS) program, and is DFR the same as DFSS? In this paper, we will try to answer these questions and, at the same time, we will propose a general DFR process that can be adopted and deployed with a few modifications across different industries in a way that will fit well into the overall Product Development Process. © RAMS Consultants.


Pulido J.,ReliaSoft Corporation
Proceedings - Annual Reliability and Maintainability Symposium | Year: 2015

As demand for highly reliable complex systems increases, engineers are being forced to consider the risk implications of design decisions earlier in the conceptual phase of projects and with greater accuracy. Standard probabilistic risk assessments (PRA) usually employed to verify that a product meets requirements are too resource intensive and too slow to keep up with the speed at which the design is maturing; while classical qualitative methods do not provide the level of detail and granularity required by the designers to make high-quality risk informed decisions. Every company is dependent on some type of asset that keeps the business in business - be it a computer, a centrifuge or a megawatt transformer. In a large enterprise, reducing costs related to asset maintenance, repair and ultimate replacement is at the top of management concerns [1]. Downtime in any network, manufacturing or computer system ultimately results not only in high repair costs, but in customer dissatisfaction and lower potential sales. In response to these concerns, this paper presents a methodology for using Life Data Analysis (LDA) techniques for evaluating new product innovation and projecting product performance due to several failure modes. The paper presents an application for a brake design where the technique was used in determining the right failure mode based on failure mechanisms. © 2015 IEEE.


Trademark
ReliaSoft Corporation | Date: 2014-03-24

Computer software that provides web-based access to applications and services through a web operating system or portal interface.


Trademark
ReliaSoft Corporation | Date: 2014-03-25

Computer Software that provides Application Programming Interface for use in customization of the Synthesis Platform Application and integration with other software systems and products.


Rga

Trademark
ReliaSoft Corporation | Date: 2016-09-02

Software for reliability analysis, namely reliability growth analysis and repairable systems analysis as it applies to the field of Reliability Engineering.


Trademark
ReliaSoft Corporation | Date: 2016-09-02

Software for for product failure analysis, namely quantitative accelerated life testing analysis as it applies to the field of reliability engineering.


Rbi

Trademark
ReliaSoft Corporation | Date: 2014-03-24

Computer software for Risk Based Inspection (RBI) analysis for oil, gas, chemical and power plants.

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