RAM & Production
Most RAM studies treat reliability as a function of time—but in real operations, it is driven by stress. When production varies, failure behavior changes, and MTBF can become misleading. This article introduces a stress-aware RAM approach, linking reliability to operating conditions through life-stress relationships and cumulative damage. If production strategy influences degradation, shouldn’t our models reflect it?
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Load sharing improves availability — but it can also accelerate asset degradation. Traditional RAM models often ignore the effects of higher operating stress when units share load, leading to optimistic life estimates and distorted LCCA results. This article outlines the hidden cost of load sharing, demonstrates why production-aware, stress-dependent reliability modelling is required, and shows how it alters maintenance and capital decisions.
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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.
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This article illustrates how failure data can be extracted from maintenance records for Life Data Analysis, specifically for components with hidden failure modes.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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In this case study we present an analysis for designing a chemical plant process, part of an extension project and during the FEED stage. The method and approach presented (as well as the software used – AeROS) is equally applicable in other industries where buffer sizing is necessary (such as Oil & Gas, manufacturing, etc.).
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When a manufacturer has the luxury to make a large profit from its products, there is less incentive for the manufacturer to analyze the warranty cost. As the manufacturing cost increases over time, and selling price decreases due to competition, the impact of warranty cost on the profit margin become more pronounced.
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In Printed Circuit Board Assemblies, solder joint failures can occur due to fatigue caused by temperature cycling. Norris-Landzberg model has been used to model fatigue failure in solder joints due to repeated temperature cycling as the device is switched on and off.
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Consider the following scenarios: An iron ore receiving port takes in shipments and keep it in a holding area. Meanwhile, the ore is consumed by nearby processing plant. In this article, I will discuss about how to analyze production impact due to supply (or input) variation. In Operation Research, this type of problem is categorized under Queueing Theory.
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This presentation demonstrates how Life Data Analysis is used to compare the reliability of a component from two suppliers (the concept can also apply to comparing different designs). It also include a cost-benefit analysis that illustrates the financial impact due to warranty cost associated to the choice of supplier.
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Manufactured products are tested at much higher constant usage rate so that reliability test can be completed in shorter time. However, the failure distribution obtained from the lab test cannot be used for warranty return analysis because the products experience very much different usage stress in the fields.
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The requirements for higher reliability have increased the need for more up-front testing of materials, components, and systems. Accelerated test have become increasingly important because of rapidly changing technologies, more complicated products with more components, higher customer expectations for better reliability, and the need for rapid product development.
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A semiconductor manufacturer conducted a reliability test to determine the effect of thermal shock cycle on solder joint life (I.e., one thermal cycle is equivalent to how much operation time). This presentation describes the scenario and shows how Life Data Analysis is used to solve this problem.
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Recurring Data Analysis is suited for repairable items that undergo multiple failures throughout their operational lifespan. These items are typically intricate, exhibiting numerous failure modes.
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This illustration showcases projecting future failures within repairable inventory. The field data undergoes conversion into a suitable reliability data type tailored for Recurring Data Analysis, enabling the creation of an NHPP (Non-Homogeneous Poisson Process) model. This model, in turn, facilitates the estimation of the expected number of future events.
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A reliable failure events forecast allows managers to:
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A company warehouse maintains and supplies an item for a large scale, round the clock production.
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When the spare inventory reaches a minimum level, a reordering is initiated. Since there is a lead time for the items to be replenished, the current quantity should meet the demand until ordered items arrive. This article presents a simulation approach for assessing the production downtime impact due to spare restock -triggering policy, and lead-time constraint.
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In the last presentation (Equipment Production Loss Contribution), production loss due to equipment is estimated using its associated downtime. Production loss of a standby sub-system in a network is more complicated as a failure may or may not cause any loss. This presentation shows how to rank standby sub-system performance in terms of production loss.
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One of the objectives of RAM analysis is to measure the Production Loss due to equipment unreliability and maintenance downtime. This information is used to identify the gaps for improvement, and also a mean to track improvement programs and evaluate its effectiveness.
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This article provides a brief description of how availability is calculated, explains its limitations as a reliability metric, and introduces a useful alternative.
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Using WO as reliability data source to calculate equipment MTBF is a common practice in many big organizations. Meanwhile there are also many software vendors exploiting this demand by providing applications that extract WO data to generate MTBF values for the corresponding equipment.
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