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Power Transmission and Gearing Systems
Published in Wei Tong, Mechanical Design and Manufacturing of Electric Motors, 2022
Failure analysis is the process of collecting and analyzing data to determine the root cause of failure of a system or components and prevent the failure from recurring. Failure causes of gearing systems can be generally attributed to eight classifications: (a) faulty design, (b) material defects, (c) processing and manufacturing deficiencies, (d) assembly or installation defects, (e) off-design or unintended service condition, (f) maintenance deficiencies, (g) abnormal wear as a result of lubricant deficiency, (h) improper operation, and (i) improper load and duty specifications [9.87]. In practice, these failure causes are usually determined by relating them to one or more specific failure modes.
Quality Management
Published in Gary L. Richardson, Brad M. Jackson, Project Management Theory and Practice, 2018
Gary L. Richardson, Brad M. Jackson
This procedure is used with physical products to analyze failure characteristics in the design. The results of these tests classify the impact of the failure and rationalize strategies to improve taking into account the quality goals versus the cost. Failure causes can result from any error or defect in the process, design, or execution (manufacture) of a product. Effect analysis refers to studying the consequences of those failures in regard to the customer experience with the product. This type of activity goes beyond the process and inspection-type components in that it uncovers the limits of the product design. Modern examples of this would be impacting passengers in a car crash test, or tests to evaluate mean time to failure for an item. The performance of a product is checked and tested under increasing stress until it fails to work. This exposes the weakest points of the product as it is vibrated, dropped, heated, or otherwise abused. From these tests, the quality management team can decide whether it is viable to improve the design or process in order to improve the resulting product. Sometimes small changes in a product or process can have significant impact on the resulting overall quality of the product.
Application of failure classification schemes to technology qualification
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
T. Myhrvold, A. Hafver, S. Eldevik, F.B. Pedersen, O.I. Haugen, K. Kvinnesland, D. McGeorge
Rausand and Øien (1996) also described an alternative classification scheme by failure cause as an option, where failure cause is “the circumstance during design, manufacture or use that have led to failure”. The failure cause is a necessary information to avoid failures or re-occurrence of failures. Failure causes may be classified in relation to the life-cycle of an item or a functional block as illustrated in Figure 4. They also described classification of failure by its effect and severity (From MIL-STD 882): catastrophic, critical, marginal, negligible.
Statistical perspectives on reliability of artificial intelligence systems
Published in Quality Engineering, 2023
Yili Hong, Jiayi Lian, Li Xu, Jie Min, Yueyao Wang, Laura J. Freeman, Xinwei Deng
In this section, we introduce the “SMART” framework for AI reliability study, which contains five components. Here, the acronym “SMART” comes from the first letter of the five components below.Structure of the system: Understanding the system structure is a fundamental step in the study of AI reliability.Metrics of reliability: Appropriate metrics need to be defined for AI reliability so that data can be collected accordingly and reliability can be tracked over time.Analysis of failure causes: Conducting failure analysis to understand how the system fails (i.e., failure modes) and what factors affect the reliability.Reliability assessments: Reliability assessments of AI systems include reliability modeling, estimation, and prediction.Test planning: Test planning methods are needed for efficient reliability data collection.
Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge
Published in International Journal of Computer Integrated Manufacturing, 2022
Oscar Serradilla, Ekhi Zugasti, Julian Ramirez de Okariz, Jon Rodriguez, Urko Zurutuza
Diagnosis: once an anomaly has been detected, diagnosis should be performed to analyse which components have been affected, in which way and to what extent. Some possible common factors are measurement errors, changes in EOC and component degradation while keeping correct working mode, failures and conditions that can lead to them. A useful technique to detect failure causes is root cause analysis, which is defined by Andersen and Fagerhaug (2006) as ‘structured investigation that aims to identify the true cause of a problem and the actions necessary to eliminate it’. It also and defines three levels of causes: symptoms, the first-level causes that lead to the problem and the higher-level causes that lead to first-level causes, where root cause is the origin.
Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making
Published in International Journal of Production Research, 2019
Arash Geramian, Ajith Abraham, Mojtaba Ahmadi Nozari
After its introduction in the 1940s (Su et’al. 2014), FMEA is extensively used in the mechanical, chemical, electronic, medical (Liu, Liu, and Liu 2013) and aerospace industries (Bowles and Peláez 1995). It is capable of analysing designs, processes, systems or services systematically, to identify potential failures as well as failure causes and effects. Failure modes are described by Severity (S), Occurrence (O) and Detection (D) factors (hereafter called risk factors). Estimated according to expert knowledge (Braaksma et’al. 2012), the risk factors are converted into a single index called Risk Priority Number (RPN) (Chen 2013). A list of RPNs shows risk priorities of failures.