Features
Development of a Mechanical Component Failure Database
In this article, we present a methodology to derive component failure rate and failure mode data for mechanical components used in automation systems based on warranty and field failure data as well as expert opinion. We describe a process for incorporating new component information into the database as it becomes available. The method emphasizes random mechanical component failures of importance in the world of safety analysis as opposed to the wear-out and aging mechanical failures that have dominated mechanical reliability analysis. The method provides a level of accuracy significantly better than warranty failure data analysis alone. The derived database has the same form as that for electrical/electronics databases used in FMEDA analyses used to show compliance with international performance-based safety standards. Thus, the mechanical database can be used in conjunction with existing electrical/electronics databases to perform required probabilistic safety analysis on automation systems comprised of both electrical and mechanical components.
Safety instrumented systems (SIS) are automatic systems designed for the purpose of taking action to avoid danger or to reduce the consequences of a potentially dangerous event. International performance-based standards [1,2] require that designers of these systems use probabilistic analysis for equipment failures classified as “dangerous” to determine if any given design meets risk reduction goals. The analysis must incorporate all equipment needed for the automation system to protect against pre-identified hazards. Typical equipment includes mechanical/electronic sensors, electronic signal conditioning modules, microcomputer controllers, relays, solenoids, pneumatic actuators and valves. The probability of failure analysis requires, at a minimum, the failure rates and failure modes data for all subsystems.
SIS typically consists of one or more safety instrumented functions (SIF). Each SIF has three subsystems. Each subsystem consists of one or more instrumentation products. A sensor is used to detect the potentially dangerous condition. A “logic solver” is used to perform filtering, timing, comparisons and other functions required to generate a trip signal. A “final element” is used to actually execute the required action. An example typical of the petro-chemical industries is shown in Figure 1. This SIF measures pressure in To perform the safety analysis for the SIF, the designer needs to estimate or predict (in the case of a design not yet constructed and/or fielded) the failure rates and failure modes of each of the products used in each subsystem.