Reliability engineering is engineering that emphasizes dependability in the lifecycle management of a product. Dependability, or reliability, describes the ability of a system or component to function under stated conditions for a specified period of time. Reliability engineering is a sub-discipline within systems engineering. Reliability is theoretically defined as the probability of failure, the frequency of failures, or in terms of availability, a probability derived from reliability and maintainability. Maintainability and maintenance may be defined as a part of reliability engineering. Reliability plays a key role in cost-effectiveness of systems.
Although Reliability is defined and affected by stochastic parameters, according to some acknowledged specialists, quality, reliability and safety are NOT achieved by mathematics and statistics. Nearly all teaching and literature on the subject emphasizes these aspects, and ignores the reality that the ranges of uncertainty involved largely invalidate quantitative methods for prediction and measurement.
In the context of software engineering, software quality refers to two related but distinct notions that exist wherever quality is defined in a business context:
Structural quality is evaluated through the analysis of the software inner structure, its source code, at the unit level, the technology level and the system level, which is in effect how its architecture adheres to sound principles of software architecture outlined in a paper on the topic by OMG. In contrast, functional quality is typically enforced and measured through software testing.
Survival analysis is a branch of statistics which deals with analysis of time to events, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, and duration analysis or duration modeling in economics or event history analysis in sociology. Survival analysis attempts to answer questions such as: what is the proportion of a population which will survive past a certain time? Of those that survive, at what rate will they die or fail? Can multiple causes of death or failure be taken into account? How do particular circumstances or characteristics increase or decrease the probability of survival?
To answer such questions, it is necessary to define "lifetime". In the case of biological survival, death is unambiguous, but for mechanical reliability, failure may not be well-defined, for there may well be mechanical systems in which failure is partial, a matter of degree, or not otherwise localized in time. Even in biological problems, some events (for example, heart attack or other organ failure) may have the same ambiguity. The theory outlined below assumes well-defined events at specific times; other cases may be better treated by models which explicitly account for ambiguous events.
Systems engineering is an interdisciplinary field of engineering that focuses on how to design and manage complex engineering projects over their life cycles. Issues such as reliability, logistics, coordination of different teams (requirements management), evaluation measurements, and other disciplines become more difficult when dealing with large, complex projects. Systems engineering deals with work-processes, optimization methods, and risk management tools in such projects. It overlaps technical and human-centered disciplines such as control engineering, industrial engineering, organizational studies, and project management. Systems Engineering ensures that all likely aspects of a project or system are considered, and integrated into a whole.
The term systems engineering can be traced back to Bell Telephone Laboratories in the 1940s. The need to identify and manipulate the properties of a system as a whole, which in complex engineering projects may greatly differ from the sum of the parts' properties, motivated the Department of Defense, NASA, and other industries to apply the discipline.
Systems science is an interdisciplinary field that studies the nature of complex systems in nature, society, and science itself. It aims to develop interdisciplinary foundations that are applicable in a variety of areas, such as engineering, biology, medicine, and social sciences.
Systems science covers formal sciences such as complex systems, cybernetics, dynamical systems theory, and systems theory, and applications in the field of the natural and social sciences and engineering, such as control theory, operations research, social systems theory, systems biology, systems dynamics, systems ecology, systems engineering and systems psychology.
Under the label Design for X, a wide collection of specific design guidelines are summarized. Each design guideline addresses a particular issue that is caused by, or affects the characteristics of a product. The design guidelines themselves usually propose an approach and corresponding methods that may help to generate and apply technical knowledge in order to control, improve, or even to invent particular characteristics of a product. From a knowledge-based view, the design guideline represents an explicit form of knowledge, that contains information about "knowing-how-to" (see Procedural knowledge). However, two problems are prevalent. First, this explicit knowledge (i.e. the design guidelines) were transformed from a tacit form of knowledge (i.e. by experienced engineers, or other specialists). Thus, it is not granted that a freshman or someone who is outside of the subject area will comprehend this generated explicit knowledge. This is because it still contains embedded fractions of knowledge or respectively include non-obvious assumptions, also called context-dependency (see e.g. Doz and Santos, 1997:16-18). Second, the characteristics of a product are likely to exceed the knowledge base of a single human. There exists a wide range of specialized fields of engineering, and considering the whole life cycle of a product will require non-engineering expertise. For this purpose examples of design guidelines are listed in the following.
DFX means design for excellence, and also "design for X", where X is a variable with many values.
Materials science, also commonly known as materials engineering, is an interdisciplinary field applying the properties of matter to various areas of science and engineering. This relatively new scientific field investigates the relationship between the structure of materials at atomic or molecular scales and their macroscopic properties. It incorporates elements of applied physics and chemistry. With significant media attention focused on nanoscience and nanotechnology in recent years, materials science is becoming more widely known as a specific field of science and engineering. It is an important part of forensic engineering (Forensic engineering is the investigation of materials, products, structures or components that fail or do not operate or function as intended, causing personal injury or damage to property.) and failure analysis, the latter being the key to understanding, for example, the cause of various aviation accidents. Many of the most pressing scientific problems that are currently faced today are due to the limitations of the materials that are currently available and, as a result, breakthroughs in this field are likely to have a significant impact on the future of human technology.