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A Bayesian Approach for Parameter Estimation of Ball Bearing Failure Data
Published in Harish Garg, Mangey Ram, Reliability Management and Engineering, 2020
Jitendra Kumar, Srikant Gupta, Sachin Chaudhary
Rolling-element bearings are widely used in machinery and play a significant role in reliability engineering. Over the last decades, rolling-element bearings were referred to as anti-friction bearings, since they have much lower friction in comparison to sliding bearings. Many types of rolling-element bearings are available in a large variety of designs, which can be applied for most arrangements in machinery for supporting radial and thrust loads. It is well known that the rolling motion has lower friction when compared with that of sliding. In addition to friction, the rolling action causes much less wear in comparison to sliding. In most cases, rolling-element bearings require less maintenance than hydrodynamic bearings. The service life of a ball-bearing is the period that it works under actual operating conditions before it is replaced or fails. It depends on the friction between the outer and inner surfaces of the bearing and their operating environments. Their operational reliability is the basis for devising optimization and improvement strategies and implementing failure factor analysis, which directly relates to the operational safety of the product during service time. Failure analysis involves the inspection of the quality and investigation of the causes for the failure of the engineering equipment.
Failure Analysis
Published in Xiaolin Chen, Yijun Liu, Finite Element Modeling and Simulation with ANSYS Workbench, 2018
Structures such as bridges, aircraft, and machine components can fail in many different ways. An overloaded structure may experience permanent deformation, which can lead to compromised function or failure of the entire structure. When subjected to millions of small repeated loads, a structure may have a slow growth of surface cracks that can cause material strength degradation and a sudden failure. When a slender structure is loaded in compression, it may undergo an unexpected large deformation and lose its ability to carry loads. Failure analysis plays an important role in improving the safety and reliability of an engineering structure. In this chapter, we will discuss some of the topics related to structural failure analysis. The concepts of static, fatigue, and buckling failures will be introduced, along with examples using ANSYS® Workbench.
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.
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.
Time-dependent reliability method for service life prediction of reinforced concrete shield metro tunnels
Published in Structure and Infrastructure Engineering, 2018
Wei Yang, Hassan Baji, Chun-Qing Li
Two factors affecting deterioration of tunnel structure, namely the external loads and environmental effects, are highly uncertain. Therefore, a reliability-based approach would be the most appropriate way for predicting the service life of these structures. Since deterioration in structures is not only random but also time-variant, failure assessment of ageing infrastructure should be dealt with using the so-called time-dependent reliability method. In the reliability analysis, by defining a limit state, the performance of structures can be assessed. The limit state or the performance function differentiate the safe and unsafe regions and can be used to quantify the safety in terms of either the probability of failure or the reliability index (Melchers, 1999). Results of such failure analysis can be used as key information in predicting the service life and maintenance of tunnel structures. In the light of considerable research on time-dependent deterioration and failure of ‘aboveground’ infrastructure, e.g. bridges, (Barone & Frangopol, 2014; Enright & Frangopol, 1998, 1999; Mori & Ellingwood, 1993; Saydam, Frangopol, & Dong, 2012; Stewart, 2001; Stewart, Rosowsky, & Val, 2001) fewer studies on failure of tunnels and other underground infrastructure have been undertaken (Abbas, 2014; Ai, Yuan, Mahadevan, & Jiang, 2015; Bazán & Beck, 2013; Sharifzadeh, Tarifard, & Moridi, 2013; Shimamoto, Yashiro, Kojima, & Asakura, 2009; Wirahadikusumah, Abraham, & Iseley, 2001). This may be attributed to the complex nature of underground structures and difficulties in identifying and mathematically modelling failure modes in these structures.
A Method for Backward Failure Propagation in Conceptual System Design
Published in Nuclear Science and Engineering, 2023
Ali Mansoor, Xiaoxu Diao, Carol Smidts
The safe operation of systems with higher failure consequences is vital for the safety of the public and the environment. However, as modern systems have become more complicated and time to market has decreased, safety verification and validation have become more difficult. One approach to address this situation is integration of safety at the design stage. System failure analysis early in the design process effectively reduces risk, maintains constancy between safety and system design, and provides economic advantages due to fewer design changes, fewer project delays, and lower validation time.1–7