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Reliability Engineering - Science topic

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I am finding difficulties while publishing one of my manuscript related to Maintenance Engineering. I worked very hard through out entire Year on this, and I believe that this manuscript will be very helpful and informative to any readers belong to Maintenance Engineering domain. I have submitted this in to renowned Journals but it's not been accepted. I have also updated the information as per reviewer's, However I am not satisfied with their comments.
What should I do? Please advise me.
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You don't say the reasons why reviewers rejected your article. Even if you are not satisfied with their comments, it is advisable to correct your article according to the recommendations of the reviewers because you do not hold the truth or part of the truth.
If you give the exact reasons for the rejection, the readers of this platform can help you improve the quality of your article and give you the right advice.
Regards
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In Reliability Engineering we use Mathematical Modelling to conclude something. We Apply the Mathematical Concepts on some Machines to analyze the lifetime or failure of the same. So, can we say Reliability Engineering is the part of Mathematical Research only?
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Mathematical modeling was the only way for me to find the C.T performance of inrush in power transformers for 132&220 systems. I developed the mathematical formula for life consuming cost and to find remaining life of power transformers sam could be used with needed modifications for other equipment in power system depending on loading for on line systems monitoring or I could say mathematical modeling the only way to find solutions for H.V, E.H.V, and H.V.D.C system.
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Can I find a book or research that involves this topic?
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Best advice is to ask the technician and lab manager of the accredited test authority you use. Text books are needed too, but there are limitations on what can be expected from standard tests.
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The definition of maintenance is based on reliability, and some times we have difficult to define the best periodic and definition to maintenance, so the some software can help to define this. So I would like to know if exist any software to help with this.
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Compare the Top Equipment Maintenance Software of 2021
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Reliability and Maintainability(R&M) are important parameters to decide a product's life and its standard operations.
So, What are the latest trends that are being followed in Reliability and Maintainability Tools development?
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Thank you@Hamzeh Soltanali for your valuable comments.
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Hi everyone, I am performing Sobol's sensitivity analysis and wondering if there is a way to set a threshold on sensitivity index so that parameters with a sensitivity index greater than the threshold is sensitive.
Many thanks!
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Usually + or - 25%
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Has anyone implemented the new Morris Method by Campolongo and Braddock in 1999 (Reliability Engineering and Systems vol 64, pg 1-12)? This is a different approach to the conventional Morris Method and it allows first order interaction to be determined.
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Dear Dr. Sourangshu Ghosh ,
this topic is rather from my research themes, but I try to help you with the following papers:
-The new Morris method: an efficient second-order screening method
RA Cropp, RD Braddock
Reliability Engineering & System Safety, 2002
- The application of the Morris method for determination of ranges of parameters variation, having assumed influence of calculation result
Sczygiol, N., Dyja, R.
Computer Methods in Materials Science, Vol. 8, No. 4 196-202 (2008)
- An efficient Morris method-based framework for simulation factor screening
W Shi, X Chen, J Shang
INFORMS Journal on Computing, Vol. 31, No. 4, pp. 745–770 (2019)
-Using the Morris sensitivity analysis method to assess the importance of input variables on time-reversal imaging of seismic sources
Anna Franczyk
Acta Geophysica volume 67, pages1525–1533 (2019)
Best regards, Pierluigi Traverso.
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Does anyone know; is there any special formula for inspection or preventive maintenance intervals or scheduling, when we are applying the artificial neural network models instead of conventional methods such as weibull?
Thanks in advance
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I agree with Mohammad Asjad and Günter Becker . ANN is a prediction tool. If you have one or more equipment failure data, you can use Weibull distribution to analyze the failure.
Due to the Weibull distribution, you will be able to comment and create schedules about the behavior of the equipment and even the system.
Cost factor is the most important criterion in these scheduling. Thanks to the Weibull distribution, I suggest you take advantage of the relevant study below in order to determine maintenance periods about costs and failure.
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What is the relation between the product reliability that is calculated in lab using ALT (Accelerated Lab Tests) and the reliability in field ?
Is it always in most cases the Lab test reliability is more than field? Can we generalise it like this?
or
In most cases the field reliability is higher compared the ALT reliability?
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Hello,
As field reliability of a product varies highly with the usage conditions/profiles we can not generalize either is higher than that of the other.
Both cases are possible where the field reliability is higher than the reliability calculated based on ALT/lab test data or vice versa. Mr. Haim Livni gives some reasons above very well that the lab test reliability results might be lower than in the field.
Usage profile and the test profile relation is crucial and either usage profile or test profile may stay in the better position.
Lastly, ALT mostly focuses on the dominant failure modes that the designer figures out, there will be more types of failures than that are focused during ALT, so the contributions by those surprise/unforseen/underestimated failure modes might be much bigger than expected. This reason is for the opposite side where in this case the field reliability is affected negatively.
Regards,
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I just completed my Ph.D. in the Department of Mechanical Engineering Department, a specialization in Machine Design. I am interested to work as a post-doctoral fellow.
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Hi,
I think you might be aware of the fact that there are so many kind of post-doc opportunities. To make the explanation simple and effective, I would like to give my opinion. According to me, the post-doc could be categorized in 5 different types as given below;
1. Individual fellowship: it mostly happens through your or your guide's personal contact with the a faculty who is going to hire you from his own project funding. For this, if you don't have any prior contact, you need to check each and every individual faculty's research interests. Once you find some faculty who is working in almost same domain like you, please email him with your CV, publications, research plan and ask him about the possibilities in his group. The hosting faculty could be national or international. Here, fellowship varies case to case, one funding agency to another or even under same funding agency, it might vary from one project to another. However, here the recruiting faculty is a sole authority to take decision, there is no involvement of institute.
2. Institute post-doc fellowship: you need to write a research proposal and a host faculty who will host you in his lab. So, at the verge of completion of your PhD, contact a faculty whim whom you are willing to work, discuss about the work can be carried out in his lab then make a research plan with mutual interest and apply formally to the institute. Institute will form a committee to review your application then upon an interview call, you need to attend the interview. Each institute is having their own norms and funding varies from one institute to another. However, it is fixed for a particular institute. Here, institute committee is sole authority to take decision, your guide might not have influence.
3. NPDF: For this, you need to contact a hosting faculty before applying. You should discuss with him about the work you want to carry out in his lab or what kind of work he is suggesting you, that will be decided on mutual interest basis. Now you will write the research proposal in that manner. Apply to npdf portal. One national committee will be formed and you might be called for a interview in Delhi or selection might be done completely based on your proposal and academic profile. This fellowship amount will be fixed for all awardees and for all institute in India.
4. Indian PDF fellowship for abroad: same like npdf but in this case, you need to contact a foreign faculty as a host. The fellowship page will clearly declare that the particular fellowship will be for a particular country. So you need to proceed accordingly.
5. External funding agency fellowship: If you can manage to convince some external funding agencies like DRDO, ISRO, DST, NASA etc. For funding your PDF research plan, they will review your proposal and further process could be in line. Once you get the fellowship, you might carry out the research in their lab, in any national lab, or in any educational institutions where the funding agency will suggest you to do. Here, again you need to appear for an interview and fellowship will vary like individual fellowship.
So, I would suggest you to apply for all of these 6. For further details, please don't hesitate to contact me.
Thanks!
--Arnab.
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I'm currently making an investigation of how to apply RCM with Preventative Maintenance to a truck fleet of fuel transport. In my company there's not a list of functional failure or failure modes, but according to some books' authors like Dhillon, Pistalleri or Dixon, said that there are generic database failures. I was searching this kind of data in google and google scholar but didn't find anything. So, this is why I ask for you to help if you know any public generic failure database that can give my team a base of information to improve our investigation.
PD: Sorry if there's any spelling or semantic error. I'm not native English, and almost any kind of material are in this language.
Thanks,
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Dear Dr. Prince Capa,
have you considered Google's new service "Dataset Search" (https://toolbox.google.com/datasetsearch) as it proves to be a useful starting point for spotting appropriate datasets for your individual research demands?
You can find some data set sources at the following link:
and I suggest you to have a look at the following documents, in pdf format:
-Optimising pro-active maintenance decisions for a multi-component system By J.A.J. Hoeks (2012)
-Reliability modelling for maintenancescheduling of mobile mining equipment by Raymond Summit and David Halomoan (2015)
-On Data-Driven Predictive Maintenance of Heavy Vehicles by Andreas Falkoven (2017)
-Maintainability analysis of mining trucks with dataanalytics by Abdulgani Kahraman (2018)
Enjoy reading and best regards, Pierluigi Traverso
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A correct interpretation of reliability analysis is determinative for researchers and industrial developers to refine their design and also prepare proper maintenance scheduling and safety analysis. However, I still see that many designers prefer to use classical safety factors instead of reliability analysis techniques. what's your sense about this.
For example, imagine that you are going to buy a bearing and I say you this bearing's reliability is 94% for the expected life of 5 years. it means that if you test 100 bearings under normal performance, almost 6 of them should fail after 5 years. does this analysis makes sense for you in your research and development?
and if the answer is yes, how do you use the outcome of reliability analysis in your research area. the answer is important for me because I am going to start developing commercial software for reliability analysis and it is important to see what are the expectations of experts from the reliability analysis methods.
Thanks,
Sajad
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Hamzeh Soltanali thanks, Hamzeh. Classical safety factors, I mean over-design instead of taking into account the reliability analysis.
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I am not sure if there exists a novel method to relate the state estimation method to the prediction of a survival function or reliability distribution function, which is more like a conventional method, in prognostics. Can anyone answer the question please?
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Thank you for your time, Dr. Jensen.
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Hello. I want to start PhD in industrial engineering this year. I have a master in industrial Eng. with the grade of 3.64/4.00.
What should I do?
Which universities can I apply for?
you can see my research background and CV in my profile page.
please help me by answer and recommend this question.
Yours sincerely,
Ali
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Dear Ali,
Congratulations on completing your masters program.
To answer your question: PhD requires a lot of drive, self motivation, isolated and collaborative creativity, please think this through, be sure you're completely ready and committed so as to get the best out of this.
Next is to identify what aspect of industrial engineering interests you, what you see yourself doing, enjoying and contributing to.
Then, identify school's that you will be interested in (effectively utilise google, connect with people on LinkedIn, research scholarly articles in your field, check scholarly forums).
While doing this, you would need to make a list of requirements by these schools, identify skills required to be successful, add to your skills if you feel you're not comfortable with certain requirements, keep growing.
You're also most likely to take the GRE with TOEFL/IELTS depending on the identified school's requirements. In addition, some will ask for abstract of your previous paper(s), Letter of Recommendation and Statement of Purpose. Do not forget, reach out to your prospective advisors, keep your initial email concise and meaningful, some will respond, some won't.
Keep researching, keep making inquiries.
It will all fit together.
Goodluck.
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Dear all,
Does anyone know how can I estimate the NHPP reliability function through Non-parametric method?!
I know the Kernel density estimation is widely used in this area, but seems it has very complicated theory.
I was wondering if you could suggest an example or statistical software directly.
Also, I am just attaching the needed formulas of Kernel model.
Thanks for your attention.
Best/Hamzeh
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Dear Marcello Fera
I know maybe it is difullcut :) but generally its possible.
Thanks for your answer anyway.
Best.
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Dear researchers,
I want to write an article about novel innovations in maintenance. this would be a great state of art paper which can help us in future to write about novel approaches which introduced here.
I work on some part of this paper but I need a researcher who helps me to complete the article.
Also, Professor Lorenzo Fedele help us to organize this paper.
Best Regards
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Hi Ali,
I'm also not quite certain what kind of innovation you refer to, but if you're also going for data-driven maintenance innovations, you might be interested in the following three papers we've published recently:
The first one is a data science study that demonstrates how implicit empirical knowledge of machine operators, which is only latently available in recorded data assets, can be made tangible for better decision support via machine learning methods:
The second one demonstrates how methods from the field of process mining can be used to support maintenance-related objectives based on a variety of data attributes spread over multiple source systems in manufacturing environments:
The third one provides a review of data-driven approaches and offers a taxonomy to structure recurring data analysis problems based on different dimensions and characteristics.
Let me know if you find this helpful or if I should provide you with more information.
Kind regards,
Patrick
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Hi.
I am currently completing the final steps of my school work which is evaluation of performance reliability of Zlín aircraft on our school. The aim of my work is to prove that our fleet is safe. I have already wrote about every single failure which appeared in the last 6 years and discussed about our aircraft maintenance. The last part is calculation of reliability. I have already calculated mean time between failures (easy one) but that is not enough in my opinion. I would like to add other numbers to make the prove better. Do you know any other formulas to calculate things connected to reliability of aircraft? The data i have are types of failures, number of failures, total time flown during each year for every aircraft and calculated mean time between failures. I have found some formulas on the internet (picture added) but the description and way how to calculate it is too much for me (as i am not a mathematician student). Therefore i am looking for a simpler formulas or a program which would help me. I am really grateful for every answer.
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is it possible reliability? If i multiply it by 100 to get % reliability it will be too low percentage if i understand it right
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I have problems with Polynomial Chaos Expansion Regression Method. I have some questions related with these topic and Matlab code.
If anyone help me I will be very appriciated.
If the problem could be solved i can give you gift or money since the problem is the main handikap of my thesis and money is not problem at all from now.
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Thanks a lot. However, these tryings are that i was considered and tried to do. Unfortunately, i had no solution at all
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I am very confused about the formula for availability because it is given by uptime/uptime+downtime.
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Dear Parag,
The formula Availability=Uptime/(Uptime+Downtime) is the most general, and therfore will ALWAYS be correct.
The expression MTBF/(MTBF+MTTR) holds only if ALL MTBF & MTTR assumptions are in effect, and these assumptions are another, extensive discussion which is beyond our scope.
From the prctical point of view, Uptime is the interval during which a system is available WHEN WE NEED IT, and Downtime is the complementary period of time.
Downtime can result from faulty system, maintenance (both preventive and corrective), inspection/calibration, and sometimes also power-up and set-up etc.
A major contributor to Downtime is logistic times, during which a system is unavailable due to a missing part, an unavailable technician etc.
Best,
Alon Sneor
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If f(t) represents the probability density of failure rate, then how it it possible that f(t) will follow exponential distribution whereas the failure rate is constant?
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Dear Parag Sen,
your problem concerns the relationship between Poisson distribution and exponential distribution - namely:
If the random variable X represents the number of errors (system failures) in a given time period and has the Poisson distribution, then the intervals between every two consecutive errors have the exponential distribution.
For example, see Wikipedia:
If for every t > 0 the number of arrivals in the time interval [0, t] follows the Poisson distribution with mean value λt, then the sequence of inter-arrival times are independent and identically distributed exponential random variables having mean 1/λ.
In general, the exponential distribution describes the distribution of time intervals between every two subsequent Poisson events.
The answer to your question can be found at the following addresses:
Best regards
Anatol Badach
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Related to the concepts of LOLP or LOLE.
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You can find your answer in the following book:
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Dear all,
I really appreciate it if someone could reply my question.
The parameters based on NHPP are Shape= 0.46, Scale= 20.54.
and operation time
0, 50, 100, 200, 300, 400,..., 1000
for more detail, please find the attached file.
I do not understand why the reliability values are unreasonable!!
Thanks in advance,
Hamzeh
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Dear Prof. Naderpour.
Thank you very much. I appreciate your reply.
Best wishes,
Hamzeh
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I need code in any programming language, preferably in matlab or R. I just dont want to code it from scratch. So please let me know if any one can help.
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Dear Dr. Srete,
Thanks for your kind and quick response.
Best Regards,
Aamir Nawaz
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I have searched the definition of Mean time to failure over Google, and there is still some confusion. Some say that MTTF can also be MTBF for irreparable products. If it is also MTBF, then MTTF is 1/(Failure Rate), right? However MTTF is also the area under the Reliability function graph in some definitions.
Can someone verify these definitions?
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Dear Ying Lim.
I attached a word file. it can help you and other researchers. if is it acceptable, please confirm it by recommendation.
Thanks
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I am interested if any of the reliability researcher has used a methodology for optimizing the time of burn-in at a technological training of electronic components and complex systems. The methodology should take into account the magnitude of thermal overload and the level of electrical overload. It would be better to allow through it can be normalized level of reliability of the products leaving burn-in procedure.
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You may wish to give some clarifications.
For this purpose I use mathematical system of two equations.
The idea is not complex and is easy to apply in practice. The time for increased load bound by givoto reliability. Unfortunately, this approach to Burn-in not report the degree of electrical load timing Byrne in.
I hope that colleagues who are dealing with problems of reliability have used a comprehensive methodology that will share.
Thank you in advance for your help.
Have a nice day!
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Both systems are capable of independently catering to  the full load.The online system system drives the output and on its failure the switchover logic system switched to the hot standby system which are fed identical input and should produce identical outputs. The system encounters a downtime when one of the following happens:
a)  Online system fails AND switch over logic Fails so cannot switch to the healthy system
b) Both system fails simulatneously due to common cause failures.
i Have attached a preliminary markov chain diagram,
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If you are trying to use the processors to validate the response, your use of two systems has an inherent logic flaw because the systems cannot determine which system provided the incorrect answer (failure) in case of  different responses. A minimum of 3 systems is required to determine 'odd man out'. This hardware logic is used by NASA for space missions and a similar concept in Stratus computers for business critical applications.
If you are simply trying to maintain five 9's availability to maintain the environment, both systems do not have to be running at the same time. But they do have to 'checkpoint' critical application data (context) between systems and maintain a 'heart beat' signal in order to determine if the other is still alive. In case of one system failure, the other system would be able to start the applications and use the check point data to pick up at the failure point. This type of fault tolerance and high availability was pioneered by Tandem computers, but there was a similar HA environment for IBM RS6000, Sun, HP and DEC systems.
By going to geographically diverse locations or simply locating the systems on different power grids and network connectivity, you dramatically reduce single points of failure. This can be a simple as different ends of the data center.
This basic concept can be scaled to any n+1 architecture, including MPP, SMP or single processor systems. Telecommunications, Banking, high volume transaction processing have used this type of systems architecture for years.
While this is a gross over-simplification of the 'odd man out' computing and N+1 systems architecture, these are both well established systems architectures with proven software designs to drive reliability to less than 10 seconds of down time in a year.
To speed up your analysis and development of a solution, you might look at some of the Linux 'hot standby' projects that exist. Unless you are doing process control work that has the potential of harming humans or other life, you might look at some of the off the shelf solutions or aligning yourself with one of the open source HA projects.
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The criteria weight calculation is crucial and plays an important role in the decision making process. But how to reduce the subjectivity in the decision making especially for those qualitative criteria? There exists some methods to reduce the subjective preference such as multi-criteria decision making, group technique, Delphi method, and fuzzy-based techniques. Do you have any idea on about "How to reduce the subjectivity in the decision making? " and welcome to provide some suggestions. Much obliged~
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formulate an optimization problem correction to decision matrix and hence find their corresponding weights...
Regards
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The electrified railway infrastructure set comprises of telecommunications, signalling, traction supply and the track. I intend to perform a reliability analysis on the railway infrastructure system which comprises infrastructure sets highlighted above as components. For me to complete this study I have to establish whether indeed an interdependence exists between all or some of the infrastructure sets (components) stated for the reliability analysis to be successful.
Does an interdependence exists? If so between which infrastructure sets?
A suggestion on articles that performed similar studies within the railway industry will also be of great use.
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Sure Nigel, there are several interdepencendeces among the different insfrastructure elements. You can find some examples:
Level crossing, incidences, weather and GIS analisys, my own work https://www.researchgate.net/profile/Jose_Gomez_Castano/contributions
I hope it helps you
kind regards
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How big is the Guttman Lambda 2 big enough for us to call a reliable test?  Can anyone give me a reference?
Thanks!
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I am facing the same problem. is there any cut-off values for cronbach alpha. I have a scale with alpha=0.6, whether is it acceptable?
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 I've been flipping through some literature on deriving the instantaneous state probabilities of a semi Markov stochastic process. However, I haven't been able to lay hands on a worked example involving non-exponential transitions. Could you please help with links to relevant references, published or unpublished? Thanks! 
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A basic reference is RA Howard (1971) Dynamic probabilistic systems Vol II.J Wiley
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Situation
The mechanical valves are subjected to 1000 beats per minute instead of original 100 beats per minute to measure wear.
So acceleration factor of dP/dt=accelerated frequency/working frequency=1000/100=10.
Query
1. How dP/dt will affect the wear?.
2. How dP/dt will affect life/durability?
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Thanks
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How to set CPDs which are time-variant in a dynamic Bayesian network when using FullBNT toolbox?
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Any standard reference books in this field?
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"which means the 10 year TTF at accelerated cycle test became 150 years at real usage conditions (acceleration factor=15)"
This is true, but usually you want to simulate 10 years of operation of valve in normal conditions thus you build the test stand to shorten the time needed for tests. You need to verify if you need to test the valve for 10, 22, 25, 30 etc years or you need to simulate the valve to operate in normal conditions for such a period of time in Time Accelerated Tests. You will find tests that must be performed on valve protheses in standards. You may refer to ISO 5840:2009 Cardiovascular implants. Cardiac valve prostheses in example.
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Reliability analysis of railway infrastructure has looked at the different infrastructure sets individually for example the reliability of the track ,signalling and power supplies.
Is it possible to look  at the whole infrastructure set as a system and perform a reliability analysis on that ?
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The infrastructure mentioned appears to be a system composed of several components, like rails, switches, signals and more. I assume, that reliability information of the components is given in terms of failure rates, repair rates, test intervals or alike.
Such systems are usually modelled by a boolean model, which nowadays is fault tree analysis (FTA) in most cases. Alternatives are: use of multistate stochastic processes, perhabs Markov processes, or Monte Carlo simulation. In practice, FTA is most frequently used, especially in aviation and nuclear industry, in some countries also in process industry.
The basic problem of any such analysis is to precisely define what events are undesired.
Not being a specialist on railway, I see a lot of possible questions:
  • One might ask: What is the probability that  any component in the infrastructure is defect?
  • Or, differently: What is the probability, that ttraffic from a given points A to another given point  B is not posible due to failed equipment
  • Or: What is the probability, that more than five trains are late in the infrastructure?
Finding the right question for a given problem is half the answer. The first question is a simple or-Gate of all the infrastructure. If you know the unavailabilities of the components, you can find the probability of the event.
The second one could be a more complicated problem. If there is a defective component, there might be a possibility to circumvent this problem using another rail, if the network topology permits this. So, the fault tree would become non-trivial.
As for the third one: It may turn out to be difficult to define a fault tree for this one. Simulation may be a feasable approach.
Reliability engineering can do a lot for you. You must only find the right question for your problem.
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Hi all,
I would like to know if someone know the data from a white paper/report/research paper about
-> SoftErrors: Failure in Time (FIT) rate (neutron/meoun/proton etc.) for some Flip-Flops which are based on FinFET and/or FDSOI technologies. As there are few papers available on SRAM FinFET FIT rate. Furthermore, there are few papers on CMOS FIT rate for flip flops are also available.
Thanks.
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Hi,
I don't have direct access to any white papers on flop SEU and FIT rates but I can say that in typical designs, the FIT rate of sequential logic, at least in terms of retention cross-section (without accounting for logic or timing derating - if you want to include this, and assuming a flop has the same fail rate as SRAM, derating will reduced the actual flop error rate to 5-10% of the measured retention value in most logic designs), is about the same as that of the SRAM - in other words if you put and array of flops in a neutron beam/alpha source and an SRAM array with the same number of elements as the flop array, the failure rate will be similar. It is true that flops with higher drive transistors (wider gates) can have a reduction in failure rate but we usually see all flops from a technology library from the weakest to the strongest are about 2x higher than SRAM (in the same technology) to about 15x lower failure rate, In a FIINFET technology I would expect the same trend to be followed since the sensitive element in the SRAM and the flop is the same or similar FinFET transistor.
Sorry if this is to general for you needs, but if you are trying to design experiments, the rule of thumb above has worked for many generations of bulk and SOI devices and I assume will work for FinFET as well.  
One note, I am assuming you are referring to standard flops and not DICE or some other redundant flops which, of course, would have a much, much lower failure rate.  
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can any one suggest best voting algorithm to implement quadruple modular redudancy for the analog signals?
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A reasonable approach is to leave out the values which deviates most from the average and use the avarage of the remaining units.
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Is there any data available for potential exposure (#unexpected exposure) in the occupation of radiation facilities( non-nuclear facilities) like industrial radiography, food irradiation facility, radiotherapy etc. 
I want to compare my data with some of the study resulting in the probability of potential exposure in above facilities. OR  please give reference for the data for probability of accident in above facilities.
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It is very difficult to give a general number, even by categories of application. The potential exposure will always depends on the radiological protection measures and on how the procedures are respected.
Public exposure should be totally excluded, except for exceptional accidents like the pne in Goiania, for instance (if you Google it you will find the IAEA report on it). Personnel exposure will depend on the safety culture of the organisation. One example of accident in the industrial radiography (it reproduces a real instance): the warning signals that indicate that the source is not in the shielded position malfunction and the source remains out in an unshielded position, yet undetectable to the naked eye. The workers enter to inspect the pipe and to do other work, but they do not wear the compulsory PADs so they have no idea the source is still in exposed position. As a result, a number of workers were exposed to doses between a few 10ths of mGy to a 1-2 Gy. The accident would have been easily avoid if the workers would have worn the PADs or the RSO would have inspected the site prior to allowing the workers in.
In theory the procedures were there, and the accident had a close to 0 probability but, because nobody followed the rules, it happened. 
To conclude, it is hard to pin a number on situations that depend heavily on human behaviour,
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I am doing research on my Masters thesis on how i can improve maintenance management of the rail track using reliability analysis.I intend to perform a reliability analysis on the components that are found in the track and with that hope to establish components that can be prioritized during maintenance planning. Is this feasible ? ,if so what material would you recommend for me to read.I do not have any background in reliability engineering,but i have a solid background in Civil and Structural engineering.
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Another more basic option is to run Monte Carlo simulations on a Reliability Block Diagram of the overall system.
You can use the time to failure of items as the randomised variable within whatever level of confidence you wish. It may not be as fancy as Markov but with computers it is pretty easy to do.
You can then insert your maintenance strategies into the simulation to determine the impact on availability with the second randomised variable of downtime due to maintenance.
For maintenance optimisation I highly recommend the RCM approach.
Cheers,
Shanna
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Hi everyone
I am studying the First Order Reliability Method in Liquefaction Now, I have read many literature related to the calculation of this method. I have difficulty to understand the equivalent normal transformation (Rackwitz-Fiessler (1978)) two-parameters of equivalent normal transformations i.e. μ and σ . Could you help me by giving me the example and how to solve of the example, especially in liquefaction problem when we want to calculate the probability using β as reliability? if yes, please attach me the file or link.
Thank you for your attention,
Best Regard
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haekonisit2015
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Hello
I have few quires about modelling NBTI Trapping/Detrapping Model Based on Predictive Technology Model (PTMs) firstly for CMOS and then FinFETs for smaller tech. nodes such as 16nm/22nm/32nm/45nm.
Q1: What material PTM (CMOS and FinFETs) models used for Gate-Oxide (e.g. Aluminium oxide Al₂O₃ or Hafnium oxide HfO₂ etc...)?
Q2: Based on above material, what is the typical trap density (i.e. number of traps/cm^2) for 16nm/22nm/32nm/45nm PTMs?
Q3: What is the typical Activation Energy (Ea) value for trapped charge emission? and what is its' range?
Thank you.
Usman
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Dear Usman,
This question attracted me to carefully read it. I think the best people who can answer your question are the founder of the Predictive Technology models.
However interface state description and modelling is not a new topic. It started intensively with the advent the MOS and the MIS devices and transistors. The best studies made on the interface state and their effect on the material and device performance were made with the start of the MOS technology. The field effect could  be demonstrated first when the interface states between the silicon and silicon dioxide  are technologically controlled. The story of the interface states and their understanding and control is an appreciable part of the success story of the surface field effect devices.
So, to predict the scaling effect on the interface state in the field effect devices you have to follow the performance of the MOS and MIS capacitor and the their control on the FET transistor among the different MOS generations. Prediction is an extrapolation process based on the tendency of performance with time and the basic physical laws. So, one can discover the properties of the next node before it is realized in experiment.
 The interface states themselves are due to unsaturated dangling bonds at the surface of the material.
They can act as traps of a type of charge and also they can act a scattering centers impeding the motion of mobile charges and also they can act as recombination centers for the minority carriers.
Interface states are reduced by a process called passivization such that their effect is has little or acceptable effect on the device performance.
Yes this is not a direct answer on your question but may be helpful to consider some concepts and principles relating to it.
wish you success.
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The HRA is an approach widely used in the past for the assessment of the human factor in the safety of the nuclear plants. In your opinion, opportunely changed the models to evaluate it, is it possible to focus this method on the assessment of the stress in the production environment, using for example methods such as the system dynamics or others?
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I think that should be possible. There are probably already good adaptions for mantainance work in nuclear prower plants that has more similarities to "classical production" than the control room team operators work you probably have in mind.
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I Have supported the theory that the reliability of project is related with a technical technological reliability- human reliability and environmental factors
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Dear colleagues,
It is possible mathematical modeling of a project similar to a system and then approached quantitatively by systems theory with feedback.
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What are the main factors we need to account for mathematically predicting (FEM) fatigue life and reliability of Mechanical heart valve?
In addition to the factors like "a)surface finish b)size c)temperature d)load factor e)fatigue strength reduction factor f)Stress avg and max" along with "Goodmann relation equations" what are the other critical factors needed to numerically predict the life in number of cycles.
Also how body fluids like blood effect the fatigue of materials, as the SN curve we generate is at room temperature and at air surroundings. So can we use the SN curve at air for predicting life of an implant socked in blood by using any correction factor?
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Dear scientist,
I guess one should not calculate/test a system brake down (here prosthesis dysfunction) under conditions, which are different to the conditions after implantation. That means
1.)  Test conditions: air surroundings + room temperature.
Real condition: blood/fluid surroundings + at least 37 ° C or even more. The temperature as well as the fluid/blood surroundings will effect the material behaviour.
2.)  The prosthesis´ orientation differ from case to case when implanted into the specific location in the heart, therefore the blood current and of course also the stress to different parts of the occluders is different from case to case.
3.)  The frequency of circles is changing in a heart (let’s say from 60 to 140 b/min), which will have an impact on the material of the occluder and the complete prosthesis. Higher alternation of load increases not only the counts of completed circles but increases also the opening and closure force on the material of the occluder and prosthesis in every single circle.
4.)  Last but not least the real stroke volume through the valve is not only pulsatile, but is changing severely and is often arrhythmic (see: atrial fibrillation, ES etc.), which may also change stress components to the material.
Only conditions, which simulate the real conditions as accurate as possible may give some imagination of the fatigue at most.
At the end several factors, which will be present in implanted prostheses and which are responsible for most of the dysfunctions of “mechanical” prostheses are also important, like rheology of blood (thrombus formation may cause dysfunction), tissue ingrowths into the prosthesis etc. A construction, which reduces these issues will definitely reduce the amount of dysfunctional prostheses, because most dysfunctions occur due to theses issues, not because of a broken part of the prosthesis.
Kind regards
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Reliability Engineering. In development product.
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Dear Rivalino and Charles, thanks for your comments. Nevertheless, I believe that the core of my question concerns to sampling method and subjective probability. Traditionally, it is necessary to have a probabilistic sampling as a condition (statement) to infer about a population. In spite this, based on Bayesian concepts, it is possible to consider expert opinion to do these inferences. So, considering this context, is the generalization acceptable for reliability figures? Of course, when considering expert opinion it is important to show the uncertainties associated to the estimates, but are this uncertainty representation acceptable?
Best regards,
Pauli
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Could you tell me an artificial method to calculate the reliability of the structure?
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What do you mean by an "artificial method" ?
 Anyway,  try the Monte Carlo method based on calculating system signature, see for example,  our book "models and Methods of Network REliability", CRC press.
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I study the reliability of the system and use the RBD (Reliability Block Diagram). I should find the failure rate but I couldn't be sure of the data set size. Is there any rule about size?
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ISSUE: Sample size for Weibul distribution?
The instantaneous failure rate given by: H(t) = (B / eta)(x / eta)B-1
WEIBUL DISTRIBUTION: In failure rate studies, the Weibull distribution is a common tool. The shape of the distribution is a bath-tub shape showing a drop in failure rate when the product is still new. During its useful life, the failure rate is stabilized at the lowest point. After its useful life, the failure starts to increase. This makes a U-shape curve.
SAMPLE SIZE FOR WEIBULL: the sample size of the Weibull distribution may be estimated by chi-square. The chi-square method is given by:
(1)   n = [X2(MTTF)] / 2T
… where X2 = chi-square with 2f + 2 degrees of freedom; f = number of failures; T = test duration; and MTTF = mean time to failure. The mean time to failure (MTTF) is given by:
(2)   MTTF = (-t) / ln(R)
… where t = time to failure; and R = reliability of the system as a function of t, i.e. R(t). System reliability is given by:
(3)   R(t) = e-(t/eta)B
… where B = shape of the parameter equal to the slope of the line in the probability plot. Eta = scale parameter or ea and a = intercept of the QQ plot line.
REFERENCES: For related articles on Weibull distribution, see links below.
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we will find the liner relationship between reliability and risk in the maintenance, Determination index that show weight tow criteria: risk and reliability
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Thank you very much, it was great
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So far I have used 0,10,20,40,50 for DALs E,D,C,B,A respectively.
Any comments on this ?
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Thanks for the answer !
This seems to be close to what I've found so far (considering the differences between the standards), I also posted this question on the ARP4754 group on LinkedIn and they have given some good insight as well.
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I have two Markov reliability model for fault tolerant mechanism and I want to achieve the sensitivity solutions of these models through perturbation theory.
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Ok, consider a system work with two parallel component (A and B) and system fails if both A & B became fail. In this model lambda (1 and 2) denotes the failure rate of each component. It is supposed that failure rate of each component obey from exponential distribution function. Reliability of the system can be achieved by solve the Markov model. I need a good theory on perturbation to achieve the sensitivity of system functioning (reliability of system) toward failure rate of component A. Bellow figure shows she Markov model of explained system.
Some paper exists about sensitivity analysis of Markov through perturbation like:
"Sensitivity and importance analysis of Markov models using perturbation analysis application in reliability studies"
In above-mentioned paper the Markov model does not have any absorbing state and I cannot use their theory.
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I am currently working on my final year project which entitles " Probabilistic Life Cycle Cost in Pipeline System." I am thinking of using ANFIS to determine the cost.
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Please type "help ANFIS" in matlab. It can help you to develop your model.
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.
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I have attached a lecture from prof. Faber who is a expert in this field.
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Does safety always increase by increasing system reliability? 
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Safety and reliability are different properties which may be supporting or conflicting. Increasing the reliability of a safety function can make an entire system safer. On the other hand, some forms of redundancy will actually increase safety at the expense of reliability, or increase reliability at the expense of safety.
The distinction is clearest if you imagine non-safety ways in which a system must be reliable. For example, an aeroplane needs to be available to fly. It could be quite safe if it never took off, but that wouldn't be considered reliable. Alternatively, it could reliably transport passengers from place to place, but kill one or two every flight. That would be reliable but not safe.
Your sub-question is actually a separate issue. Safety is with respect to absolute intent, not requirements or design. A system can behave exactly as specified or designed, and still be unsafe. You don't necessarily need a component failure to cause a dangerous behaviour. There are also component failures which case the system to fail against its requirements, but not in an unsafe way.
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Some of them are DFT and BIST. What are others?
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For testing of digital vlsi circuits you can use Design for testability tools . it includes BIST-Built in self Test, ATPG-automatic Test pattern generation,Scan Chains etc..
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I find very often that professional and laymen are using the wording “risk” and “reliability” as having the same meaning. As I speak few languages, I have the impression that this perception of the subjects is cross cultural phenomena.
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It might be better to consider this question in the context of two different language types, natural language and formal language. Natural language is used by people to communicate about the world. Natural language evolves naturally.
Formal languages are are focused on specific domains and have specific notation and relationships defined.
Natural language is very ambiguous and needs common context and verbose content to help make the natural language statements clear. Natural languages are full of idiom and metaphor, which makes understanding more difficult.
Formal languages are unambiguous, literal and can be fully analyzed (structure, tokens, symbols) to clearly understand the semantic meaning of the expression in a formal language.
Wayne Wymore in "Model-Based Systems Engineering," 1993, proposed a formal systems design language. In this formal systems engineering language, reliability is defined as " Reliability is the performance figure of merit that assigns to each functional system design the probability the INFOSY will not enter failure mode between the time any user inputs a demand for information and the time the information is delivered."
Natural languages are needed to provide a metalanguage to talk about formal languages. Formal languages are needed to reduce ambiguity and redundancy in communications.
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A lot of various paper discuss the need of uncertainty decomposition into aleatoric and epistemic. In the context of reliability and risk analysis, the uncertainty quantification is commonly refered to be very significant in order to achieve more accurate results.
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It is important to note that the reason Der Kiureghian and Ditlevsen separate the different types of uncertainty is for risk communication, not for risk assessment. The purpose in separating is to make clear which types of uncertainty you choose to reduce or not reduce. My previous answer ignored this purpose. I agree that talking about different types of uncertainty can be helpful. In my public talks on risk, I often make the point by asking people what's the probability that the dice in my hand will roll a "6". When they answer "1 in 6" I point out their faith in my honesty, and ask them to consider how _sure_ they are that the odds are "1 in 6".
I maintain that if you are trying to quantify risk, it isn't important whether your uncertainty comes from "inherent randomness of the universe" or "things we don't know". If you're trying to communicate risk, in particular if you're advocating actions to reduce risk or to better understand risk, the distinction may be helpful.
The Trivedi approach which folds the epistemic uncertainty back into the probability calculations has a different purpose which (personal opinion here) runs contrary to the transparency goal of Der Kiureghian and Ditlevsen. It requires a number of assumptions which _themselves_ are unquantifiable epistemic uncertainties. The risk is that by quantifying some of the epistemic uncertainty, you give a false impression that this is all of the uncertainty.
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I know how to determine the weibull distribution, but I don’t see the usage or the contribution that this analysis can do during the implementation of reliability centered maintenance.
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When shape parameter is 1 or less than 1, RCM decision is no preventive maintenance. If shape parameter is greater than 1, preventive maintenance may be carried out. For preventive or predictive maintenance case, knowledge of distribution may also help you decide PM interval or time available beween P and F points for predictive maintenance.