Electronic System Reliability: Collating Prediction Models

Abstract

This paper summarizes research done in the area of electronic system reliability and assesses the approaches used in the calculation of electronic system failure rates. A detailed literature survey is conducted to investigate the various available reliability prediction models.The paper starts with a definition of reliability, briefly discusses various regions of system failure rate in time, justifies the role of reliability prediction methods, provides a historical overview, classifies the traditional models into easy to understand categories and discusses the advantages and disadvantage, reviews the key models that are currently in use, and compares the first and most widely used model (i.e., MIL-HDBK-217) with the most recently introduced model (i.e., PRISM).Index Terms-Electronic component reliability, empiricalbased models, failure rate, MIL-HDBK-217, physics-of-failure, prediction models.

Reliability of different levels of cascaded H-Bridge inverter: An investigation and comparison

“…Hence, lifetime prediction does not account for stochastic processes. It is comparable to the physics-of-failure model [8].…”

Forecasting Failure Rates of Electronic Goods by Using Decomposition and Fuzzy Clustering of Empirical Failure Rate Curves

“…Goel and Graves (2006) classify the techniques into two groups: empirical-based models and physics-of-failure models.…”

“…In addition to that, considering the shortening product life-cycles, the curves of failure rate functions of consumer electronics are bathtubshaped with all the three characteristic phases of the traditional bathtub curve: the decreasing first phase, called infancy period, the quasi constant second phase, which is also referenced as the period of normal operation or useful life, and the third, increasing one representing the wear-out period. The failure rate functions, that are also called hazard functions, represent the characteristics of product life (Goel and Graves, 2006;Economou, 2004;Campbell et al, 1992). Similarly to product-or business related life-cycles (Gelei and Dobos, 2014), the failure rate functions can be considered as characteristics that reflect the product reliability over the product life-cycle.…”

Electro-thermal modeling for junction temperature cycling-based lifetime prediction of a press-pack IGBT 3L-NPC-VSC applied to large wind turbines

“…Following the model simplifications, the junction temperatures are represented by (8), (10), (11) for TA1, where the thermal time constant τ th is R th C th . The case and cooling manifold temperatures are given in (12)- (15).…”

“…For converter reliability predictions, the two main approaches being based on empirical-based models and physics-offailure models can be applied [10]. The first approach uses statistical failure rates of each individual system component in failure in time of 10 9 -hour (FIT) regardless of any failure causes and predicts the system's failure rate with respect to the component count and connection type [11].…”