Reliability analysis: assessment of hardware and human reliability
- Authors: Mafu, Masakheke
- Date: 2017
- Subjects: Bayesian statistical decision theory , Reliability (Engineering) , Human machine systems , Probabilities , Markov processes
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/6280 , vital:21077
- Description: Most reliability analyses involve the analysis of binary data. Practitioners in the field of reliability place great emphasis on analysing the time periods over which items or systems function (failure time analyses), which make use of different statistical models. This study intends to introduce, review and investigate four statistical models for modeling failure times of non-repairable items, and to utilise a Bayesian methodology to achieve this. The exponential, Rayleigh, gamma and Weibull distributions will be considered. The performance of the two non-informative priors will be investigated. An application of two failure time distributions will be carried out. To meet these objectives, the failure rate and the reliability functions of failure time distributions are calculated. Two non-informative priors, the Jeffreys prior and the general divergence prior, and the corresponding posteriors are derived for each distribution. Simulation studies for each distribution are carried out, where the coverage rates and credible intervals lengths are calculated and the results of these are discussed. The gamma distribution and the Weibull distribution are applied to failure time data.The Jeffreys prior is found to have better coverage rate than the general divergence prior. The general divergence shows undercoverage when used with the Rayleigh distribution. The Jeffreys prior produces coverage rates that are conservative when used with the exponential distribution. These priors give, on average, the same average interval lengths and increase as the value of the parameter increases. Both priors perform similar when used with the gamma distribution and the Weibull distribution. A thorough discussion and review of human reliability analysis (HRA) techniques will be considered. Twenty human reliability analysis (HRA) techniques are discussed; providing a background, description and advantages and disadvantages for each. Case studies in the nuclear industry, railway industry, and aviation industry are presented to show the importance and applications of HRA. Human error has been shown to be the major contributor to system failure.
- Full Text:
- Date Issued: 2017
- Authors: Mafu, Masakheke
- Date: 2017
- Subjects: Bayesian statistical decision theory , Reliability (Engineering) , Human machine systems , Probabilities , Markov processes
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/6280 , vital:21077
- Description: Most reliability analyses involve the analysis of binary data. Practitioners in the field of reliability place great emphasis on analysing the time periods over which items or systems function (failure time analyses), which make use of different statistical models. This study intends to introduce, review and investigate four statistical models for modeling failure times of non-repairable items, and to utilise a Bayesian methodology to achieve this. The exponential, Rayleigh, gamma and Weibull distributions will be considered. The performance of the two non-informative priors will be investigated. An application of two failure time distributions will be carried out. To meet these objectives, the failure rate and the reliability functions of failure time distributions are calculated. Two non-informative priors, the Jeffreys prior and the general divergence prior, and the corresponding posteriors are derived for each distribution. Simulation studies for each distribution are carried out, where the coverage rates and credible intervals lengths are calculated and the results of these are discussed. The gamma distribution and the Weibull distribution are applied to failure time data.The Jeffreys prior is found to have better coverage rate than the general divergence prior. The general divergence shows undercoverage when used with the Rayleigh distribution. The Jeffreys prior produces coverage rates that are conservative when used with the exponential distribution. These priors give, on average, the same average interval lengths and increase as the value of the parameter increases. Both priors perform similar when used with the gamma distribution and the Weibull distribution. A thorough discussion and review of human reliability analysis (HRA) techniques will be considered. Twenty human reliability analysis (HRA) techniques are discussed; providing a background, description and advantages and disadvantages for each. Case studies in the nuclear industry, railway industry, and aviation industry are presented to show the importance and applications of HRA. Human error has been shown to be the major contributor to system failure.
- Full Text:
- Date Issued: 2017
Intelligent design and biology
- Authors: Ramsden, Sean
- Date: 2003
- Subjects: Hume, David, 1711-1776 , Darwin, Charles, 1809-1882 , Paley, William, 1743-1805 , Dembski, William A., 1960- , Behe, Michael J., 1952- , Evolution (Biology) , Probabilities , Naturalism , Intelligent design (Teleology)
- Language: English
- Type: Thesis , Masters , MA
- Identifier: vital:2739 , http://hdl.handle.net/10962/d1007561 , Hume, David, 1711-1776 , Darwin, Charles, 1809-1882 , Paley, William, 1743-1805 , Dembski, William A., 1960- , Behe, Michael J., 1952- , Evolution (Biology) , Probabilities , Naturalism , Intelligent design (Teleology)
- Description: The thesis is that contrary to the received popular wisdom, the combination of David Hume's sceptical enquiry and Charles Darwin's provision of an alternative theoretical framework to the then current paradigm of natural theology did not succeed in defeating the design argument. I argue that William Paley's work best represented the status quo in the philosophy of biology circa 1800 and that with the logical mechanisms provided us by William Dembski in his seminal work on probability, there is a strong argument for thr work of Michael Behe to stand in a similar position today to that of Paley two centuries ago. The argument runs as follows: In Sections 1 and 2 of Chapter 1 I introduce the issues. In Section 3 I argue that William Paley's exposition of the design argument was archetypical of the natural theology school and that given Hume's already published criticism of the argument, Paley for one did not feel the design argument to be done for. I further argue in Section 4 that Hume in fact did no such thing and that neither did he see himself as having done so, but that the design argument was weak rather than fallacious. In Section 5 I outline the demise of natural theology as the dominant school of thought in the philosophy of biology, ascribing this to the rise of Darwinism and subsequently neo-Darwinism. I argue that design arguments were again not defeated but went into abeyance with the rise of a new paradigm associated with Darwinism, namely methodological naturalism. In Chapter 2 I advance the project by a discussion of William Dembski's formulation of design inferences, demonstrating their value in both everyday and technical usage. This is stated in Section 1. In Sections 2 and 3 I discuss Dembski's treatment of probability, whilst in Section 4 I examine Dembski's tying of different levels of probability to different mechanisms of explanation used in explicating the world. Section 5 is my analysis of the logic of the formal statement of the design argument according to Dembski. In Section 6 I encapsulate objections to Dembski. I conclude the chapter (with Section 7) by claiming that Dembski forwards a coherent model of design inferences that can be used in demonstrating that there is little difference between the way that Paley came to his conclusions two centuries ago and how modem philosophers of biology (such as I take Michael Behe to be, albeit that by profession he is a scientist) come to theirs when offering design explanations. Inference to the best explanation is demonstrated as lying at the crux of design arguments. In Chapter 3 I draw together the work of Michael Behe and Paley, showing through the mechanism of Dembski's work that they are closely related in many respects and that neither position is to be lightly dismissed. Section 1 introduces this. In Section 2 I introduce Behe's concept of irreducible complexity in the light of (functional) explanation. Section 3 is a detailed analysis of irreducible complexity. Section 4 raises and covers objections to Behe with the general theme being that (neo-) Darwinians beg the question against him. In Section 4 I apply the Dembskian mechanic directly to Behe's work. I argue that Behe does not quite meet the Dembskian criteria he needs to in order for his argument to stand as anything other than defeasible. However, in Section 5 I conclude by arguing that this is exactly what we are to expect from Behe's and similar theories, even within competing paradigms, in the philosophy of biology, given that inference to the best explanation is the logical lever therein at work. , KMBT_363 , Adobe Acrobat 9.54 Paper Capture Plug-in
- Full Text:
- Date Issued: 2003
- Authors: Ramsden, Sean
- Date: 2003
- Subjects: Hume, David, 1711-1776 , Darwin, Charles, 1809-1882 , Paley, William, 1743-1805 , Dembski, William A., 1960- , Behe, Michael J., 1952- , Evolution (Biology) , Probabilities , Naturalism , Intelligent design (Teleology)
- Language: English
- Type: Thesis , Masters , MA
- Identifier: vital:2739 , http://hdl.handle.net/10962/d1007561 , Hume, David, 1711-1776 , Darwin, Charles, 1809-1882 , Paley, William, 1743-1805 , Dembski, William A., 1960- , Behe, Michael J., 1952- , Evolution (Biology) , Probabilities , Naturalism , Intelligent design (Teleology)
- Description: The thesis is that contrary to the received popular wisdom, the combination of David Hume's sceptical enquiry and Charles Darwin's provision of an alternative theoretical framework to the then current paradigm of natural theology did not succeed in defeating the design argument. I argue that William Paley's work best represented the status quo in the philosophy of biology circa 1800 and that with the logical mechanisms provided us by William Dembski in his seminal work on probability, there is a strong argument for thr work of Michael Behe to stand in a similar position today to that of Paley two centuries ago. The argument runs as follows: In Sections 1 and 2 of Chapter 1 I introduce the issues. In Section 3 I argue that William Paley's exposition of the design argument was archetypical of the natural theology school and that given Hume's already published criticism of the argument, Paley for one did not feel the design argument to be done for. I further argue in Section 4 that Hume in fact did no such thing and that neither did he see himself as having done so, but that the design argument was weak rather than fallacious. In Section 5 I outline the demise of natural theology as the dominant school of thought in the philosophy of biology, ascribing this to the rise of Darwinism and subsequently neo-Darwinism. I argue that design arguments were again not defeated but went into abeyance with the rise of a new paradigm associated with Darwinism, namely methodological naturalism. In Chapter 2 I advance the project by a discussion of William Dembski's formulation of design inferences, demonstrating their value in both everyday and technical usage. This is stated in Section 1. In Sections 2 and 3 I discuss Dembski's treatment of probability, whilst in Section 4 I examine Dembski's tying of different levels of probability to different mechanisms of explanation used in explicating the world. Section 5 is my analysis of the logic of the formal statement of the design argument according to Dembski. In Section 6 I encapsulate objections to Dembski. I conclude the chapter (with Section 7) by claiming that Dembski forwards a coherent model of design inferences that can be used in demonstrating that there is little difference between the way that Paley came to his conclusions two centuries ago and how modem philosophers of biology (such as I take Michael Behe to be, albeit that by profession he is a scientist) come to theirs when offering design explanations. Inference to the best explanation is demonstrated as lying at the crux of design arguments. In Chapter 3 I draw together the work of Michael Behe and Paley, showing through the mechanism of Dembski's work that they are closely related in many respects and that neither position is to be lightly dismissed. Section 1 introduces this. In Section 2 I introduce Behe's concept of irreducible complexity in the light of (functional) explanation. Section 3 is a detailed analysis of irreducible complexity. Section 4 raises and covers objections to Behe with the general theme being that (neo-) Darwinians beg the question against him. In Section 4 I apply the Dembskian mechanic directly to Behe's work. I argue that Behe does not quite meet the Dembskian criteria he needs to in order for his argument to stand as anything other than defeasible. However, in Section 5 I conclude by arguing that this is exactly what we are to expect from Behe's and similar theories, even within competing paradigms, in the philosophy of biology, given that inference to the best explanation is the logical lever therein at work. , KMBT_363 , Adobe Acrobat 9.54 Paper Capture Plug-in
- Full Text:
- Date Issued: 2003
A probability operator
- Authors: Sinclair, Allan M
- Date: 1965
- Subjects: Mathematics , Probabilities
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5423 , http://hdl.handle.net/10962/d1007702 , Mathematics , Probabilities
- Description: From Introduction: In probability theory it is often convenient to represent laws by characteristic functions, these being particularly suited to classical analysis. Trotter has suggest ted that probability laws can also be represented by probability operators. These operators are easily handled since they are continuous, and hence bounded, positive linear operators on a normed linear space. This representation arises because distribution functions and their complete convergence correspond to probability operators and their complete convergence. Hence the relations between distribution functions and probability operators will be discussed before the introduction of probability laws.
- Full Text:
- Date Issued: 1965
- Authors: Sinclair, Allan M
- Date: 1965
- Subjects: Mathematics , Probabilities
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5423 , http://hdl.handle.net/10962/d1007702 , Mathematics , Probabilities
- Description: From Introduction: In probability theory it is often convenient to represent laws by characteristic functions, these being particularly suited to classical analysis. Trotter has suggest ted that probability laws can also be represented by probability operators. These operators are easily handled since they are continuous, and hence bounded, positive linear operators on a normed linear space. This representation arises because distribution functions and their complete convergence correspond to probability operators and their complete convergence. Hence the relations between distribution functions and probability operators will be discussed before the introduction of probability laws.
- Full Text:
- Date Issued: 1965
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