AssetStudio

A mining company has a policy to replace the lubricating oil of its fleet of machines when the oil TAN (Total Acid Number) value reaches a critical level. The company is currently using Brand A. Another supplier claimed that its brand (Brand B) has a longer operating life (Oil-Life-Before-Drain).

This example presents a Life Data Analysis approach to compare the lubricating oil datasets from the 2 different suppliers. Given the datasets, we determine whether the difference between the 2 populations is statistically significant. If there is a significant difference, how to access the financial benefits for switching supplier?

Many non-repairable systems, subsystem, and components (generally refer to as “units”) have more than one cause of failure. For the purpose of improving reliability, it is essential to identify the cause of failure down to the component level.

This presentation shows the procedure for analysing products with multiple failure modes, and compare with the analysis where failure modes information is ignored. The product reliability model is then obtained by combining the reliability of each failure modes in series.

From reliability perspective, solar systems are generally robust against failures. Yet, from operation perspective, the system is not stable, as energy source depends the weather conditions.

This presentation describes a simulation approach to quantify how the variations of 1. input energy, 2. load demand, and 3. storage usage, affect the availability of output energy.

There is always a motivation to hold a low level of spares especially if the spare is expensive and holding cost is significant. However, it may run a risk of spare unavailability which in turn causes production loss.

This presentation uses AssetStudio’s AeROS reliability analytical tool to optimize the restock policy for a critical spare.

This article briefly explains how the gamma value is solved when you are fitting a dataset to a 3-Parameter Weibull (3P-Weibull), so that you have enough background knowledge to decide whether it is a good idea to proceed with the analysis.

An example that demonstrates an appropriate use case of the 3-Parameter Weibull distribution is also provided.

This article presents the development of a reliability digital twin designed for a data communication system. AeROS utilizes this digital twin to implement Consecutive K-out-of-N logic for conducting RAM (Reliability, Availability, and Maintainability) analysis.

Within maintenance organizations, historical failure data extracted from Computerized Maintenance Management Systems (CMMS) serves as a foundational data source for reliability analysis. This presentation highlights the challenges encountered when employing Life Data Analysis on recurring datasets, presenting an alternative statistical model, Recurring Data Analysis, tailored specifically for these scenarios.

This article illustrates how failure data can be extracted from maintenance records for Life Data Analysis, specifically for components with hidden failure modes.

This presentation illustrates the creation of a reliability digital twin featuring a startup hidden failure, developed using AeROS software. The digital twin enables us to evaluate the production impact of a system encountering a hidden failure mode.