An overview of the approaches for automotive safety integrity levels allocation

YesISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems, ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges

A degradation-based model for joint optimization of burn-in, quality inspection, and maintenance: a light display device application

This paper presents a degradation-based model to jointly determine the optimal burn-in, inspection, and maintenance decisions, based on degradation analysis and an integrated quality and reliability cost model. Degradation modeling plays an important role in reliability prediction and analysis for many highly reliable components and equipment, when the failures can rarely be observed. Unlike traditional applications, quality and reliability must be considered simultaneously for devices subject to degradation, because quality inspection decisions often impact anticipated reliability and failure-time distributions. This paper presents an integrated model to jointly optimize quality and reliability for devices subject to degradation, with a focus on burn-in, quality inspection, and maintenance policies. Based on the degradation modeling and analysis, the reliability function and the time-to-failure distribution are derived under the condition that the quality inspection is applied following the burn-in period. The optimal burn-in, quality inspection, and preventive maintenance policies are determined by minimizing the expected total cost per usage lifetime. The proposed model is illustrated using the application of light display devices, in which the degradation path follows a negative shifted lognormal distribution with a random failure threshold. A numerical example is provided to illustrate the application of our model to the light display devices

Simultaneous quality and reliability optimization for microengines subject to degradation

Micro-electro-mechanical systems (MEMS) represent an exciting new technology, but to achieve more widespread usage and wider adoption within more industrial applications, they must be highly reliable, and manufactured to stringent quality standards. Many challenging manufacturing issues are of concern during the fabrication of MEMS, such as precise dimensional inspection, reliability modeling, burn-in scheduling, avoiding stiction, and maintenance strategies. However, only limited mathematical tools for improving MEMS reliability, quality, and productivity are currently available. This paper proposes a mathematical model to jointly determine inspection & preventive replacement policies for surface-micromachined microengines subject to wear degradation, which is a major failure mechanism for certain MEMS devices. The optimal specification limits for inspection, and the replacement interval are determined by simultaneously optimizing MEMS quality and reliability. The proposed model can be used as a tool for decision-makers in MEMS manufacturing to make sound economical and operational decisions on reliability, quality, and productivity. While illustrated considering one specific microengine design, the proposed model can be applied to a broader range of MEMS devices that experience wear degradation between rubbing surfaces

Corrections to “Component reliability criticality or importance measures for systems with degrading components” [Mar 12 4-12]

In the original published paper (ibid., vol. 61, no. 1, pp. 4-12, Mar. 2012), there was a computer algorithm coding error (Matlab) in the numerical example of Section 4.2, which impacted all results. This error causes the ranges and shapes of Fig. 9 to 12 to be incorrect. Hence, we provide new figures here with the same parameter setting, as well as the new discussions about the figures accordingly

A review of Pareto pruning methods for multi-objective optimization

Previous researchers have made impressive strides in developing algorithms and solution methodologies to address multi-objective optimization (MOO) problems in industrial engineering and associated fields. One traditional approach is to determine a Pareto optimal set that represents the trade-off between objectives. However, this approach could result in an extremely large set of solutions, making it difficult for the decision maker to identify the most promising solutions from the Pareto front. To deal with this issue, later contributors proposed alternative approaches that can autonomously draw up a shortlist of Pareto optimal solutions so that the results are more comprehensible to the decision maker. These alternative approaches are referred to as the pruning method in this review. The selection of the representative solutions in the pruning method is based on a predefined instruction, and its resolution process is mostly independent of the decision maker. To systematize studies on this aspect, we first provide the definitions of the pruning method and related terms; then, we establish a new classification of MOO methods to distinguish the pruning method from the a priori, a posteriori, and interactive methods. To facilitate readers in identifying a method that suits their interests, we further classify the pruning method by the instruction on how the representative solutions are selected, namely into the preference-based, diversity-based, efficiency-based, and problem specific methods. Ultimately, the comparative analysis of the pruning method and other MOO approaches allows us to provide insights into the current trends in the field and offer recommendations on potential research directions