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Component and Subsystem Reliability Testing
Published in Ali J Jamnia, Khaled Atua, Executing Design for Reliability within the Product Life Cycle, 2019
In RDT, we are interested in answering two questions. First, does the design meet its design life, and, second, what is the failure rate within this period. By knowing these two parameters, we can in fact calculate other reliability metrics. We are not sure if there is a commonly accepted definition of design life; however, a general definition may be the period of time during which an acceptable percentage of the units fail. We need to keep in mind that in general, we deal with either a constant failure rate, which is indicative of random failures, or with increasing rates, which indicate that some components are wearing out. Mechanical components usually exhibit wear. Bearings, valves, linkages, and the like are usually the first examples that we think about when asked about mechanical components. However, corrosion and wear on electrical contacts, cracking and crazing of encapsulants (of integrated circuits), or even cracking of solder joints due to thermal cycling are also examples of the wear-out failure mechanism. As we learned in Chapter 6, we can use the Weibull distribution to model both a constant as well as an increasing (wear-out) failure rate.
Introduction
Published in Ron Burch, Resilient Space Systems Design: An Introduction, 2019
Reliability engineering provides designers with the mathematical models to make quantitative predictions of the system reliability based on component failure rate data and the system's design and operation. These reliability estimates are derived from failure rate models based on measured and historical component test and operational data which then become a part of the system's operational availability. The reliability metric is a probability of system (or mission) success (or failure) over some defined time period of interest such as satellite design life. In Chapter 5, a methodology is provided using system design information coupled with a description of external threats to yield an expected value of the residual system capability that can be calculated for a specific threat or threat scenario, similar to a reliability prediction.
Thermal Management and Reliability
Published in Dorin O. Neacsu, Switching Power Converters, 2017
The failure rate of the entire system is calculated from the failure rates for each individual component. In most systems, like it is the case of power electronics circuits, all components are considered as being important. From reliability point of view, this means that the components are considered as being “in series” within the system. In a series system that includes n components, the overall failure rate λSYSTEM is given by λSYSTEM=∑i=1Nλi
Reliability Analysis of Microgrids: Evaluation of Centralized and Decentralized Control Approaches
Published in Electric Power Components and Systems, 2023
Selahattin Garip, Melih Bilgen, Necmi Altin, Saban Ozdemir, Ibrahim Sefa
Reliability refers to the ability of a system or product to perform its intended function without failure, interruption, or error over a predetermined duration, or within a specific timeframe and under specified operating conditions. Reliability can be quantified by various metrics, such as mean time between failures (MTBF), failure rate, and availability. MTBF represents the average time between two consecutive failures of a system or product, while failure rate represents the probability of a failure occurring within a given time period. Availability represents the percentage of time that a system or product is operational and ready to perform its intended function. In engineering and industry, reliability is a critical factor in the design, testing, and production of various systems and products, including electrical systems, mechanical systems, software, and complex machinery. High reliability is often a requirement for mission-critical systems, such as those used in aerospace, defense, transportation, and medical applications, where failure can have severe consequences.
Predictive maintenance: assessment of potentials for residential heating systems
Published in International Journal of Computer Integrated Manufacturing, 2023
The ‘bathtub curve‘ (Figure 3) indicates that a new system shows a high failure rate due to design, material, manufacturing, and installation errors during the first phase of operation. The decrease of the so-called early failures causes a decrease of the failure rate. This phase is followed by an operating time interval mainly characterized by random failures (Figure 3: Phase II). During this phase with a relatively constant failure rate, failures are mainly caused by external influences on the system. These include environmental stress, inadmissible loads, operating errors, but also inadequately executed maintenance measures. In the subsequent phase (Figure 3: Phase III), wear-related failures occur in addition to the randomly caused ones already described. These have their origin primarily in wear processes, e.g. due to deterioration, fatigue, or corrosion. The consequences of these wear processes are expressed in an increase of the failure rate (Schenk 2010).
Reliability Prediction Methods for Electronic Devices: A State-of-the-art Review
Published in IETE Technical Review, 2022
Vinay Kumar, Lalit Kumar Singh, Anil Kumar Tripathi
The failure rate represents the limit of the probability that a failure occurs per unit time interval, provided that no failure has taken place before time, . In other words, the failure rate is the frequency with which a system or component failure occurs. It is represented in failures per unit of time. Figure 1 demonstrates a failure rate curve as a function of time, also called the bathtub curve. Bathtub curve [6] is broken into three different regions in the reference of time, they are: The first region, where the failure rate decreases called infant mortality or early failure.The second region, where failure rate relatively constant called useful life, andThird and a final region, where failure rate increases called wear out or aging.