15-09-2017, 03:39 PM
Often, models are constructed in the context of computer and business systems to better understand existing or future systems. However, a model never completely corresponds to reality. Modeling always means emphasizing and omitting: emphasizing essential details and omitting those that are not relevant. But what is essential and what is irrelevant? There is no universal answer to this question. Rather, the answer depends on what the objectives of the model are and who is watching or reading.
Due to their nature, low-volume, customized or special equipment has few statistically significant data to predict system availability during the life of the equipment. Their unique constructions are often expensive to buy and install, and are equally expensive to maintain. This article presents a practical method for estimating the availability of custom equipment, using a custom NVH 4wd dynamometer system as an example. The proposed method models the availability of an existing custom-built system that uses information from anecdotal components based on interviews with field service personnel. The interview data is used to create estimated probability density functions for the main components of the system. The probability density functions of components are assembled into a system model based on a function derived from system reliability. This technique provides a fast and fast system availability model that can be used to assess the risk and profitability of system maintenance strategies.
Due to their nature, low-volume, customized or special equipment has few statistically significant data to predict system availability during the life of the equipment. Their unique constructions are often expensive to buy and install, and are equally expensive to maintain. This article presents a practical method for estimating the availability of custom equipment, using a custom NVH 4wd dynamometer system as an example. The proposed method models the availability of an existing custom-built system that uses information from anecdotal components based on interviews with field service personnel. The interview data is used to create estimated probability density functions for the main components of the system. The probability density functions of components are assembled into a system model based on a function derived from system reliability. This technique provides a fast and fast system availability model that can be used to assess the risk and profitability of system maintenance strategies.