Asset modelling is the end to end process for using asset data to make decisions. It is the combination of what once was considered separate processes like Failure Mode, Effects and Criticality Analysis (FMECA), condition assessments, probabilistic analyses, optimisation methods and more. Asset modelling combines components of asset analytics, reliability engineering and mathematical optimisation into a single integrated approach to capture current thinking and aid decision making.
Modla's general approach is using a data-interpretation lens, and causal inference.
Asset modelling aims to capture Subject Matter Expert (SME) knowledge and the current thinking of industry in a framework that can answer questions, such as:
Moreover, asset modelling must concurrently consider operational requirements, environmental conditions, risk, business costs and strategic objectives.
Asset models can be structured to accept various types of inputs. Each input must impact the asset in some way and usually falls into one of the following categories.
Specific asset characteristics impact how an asset can fail. These include configuration attributes, size and type, ratings and build standards. The information typically relates to the modes of failure that are applicable and their initial failure parameters.
Condition data determines the current state of the asset. Most analytical approaches assume a new condition. However, up to date condition data facilitates better strategy recommendations reflective of the asset's current condition and age. The conditional inputs are typically measured or observed and can be as simple as age, a visual inspection, or use of more complicated condition monitoring inputs and predictive technologies.
The types of inputs considered under this category, typically reduce or extend the lives of various modes. Operational data includes loading, duty and rate information. It describes how the asset is operated and how its operation affects its performance. Operating attributes can have a direct impact on the component's expected lives.
Environmental conditions include attributes such as process mediums, corrosion rates, or any other factor that can influence asset life, that is external to the asset itself.
Business risk and cost data apply the business context to the model. Risk and cost are incorporated using a value framework approach. The approach monetises risk, allowing analytical solutions to maximise value according to the businesses priorities and strategic objectives.
The considered costs also include the usual suspects such as spares and materials, labour, equipment hire etc.
Businesses can derive insights from asset models and in-turn inform decisions. The process is described below:
The elements connect like this:
The asset model itself may contain any or all of the following elements.
A condition assessment involves processing conditional data and in turn, making inferences on the condition or health of the asset. The data can be observational e.g. visual inspections or measured e.g. sensor data. Sometimes condition information is used to derive a score or scale that describes the overall health of the asset. However, in the context of asset modelling, condition assessments, age or any other applicable input is used to set the initial failure distributions of the assets. That means the conditional information modifies the statistical likelihoods of failure to account for the current health of the asset.
Sensor data can be analysed by Artificial Intelligence (AI) and Machine Learning (ML) algorithms, but the output is the same i.e. derived conditional information. Predictive Maintenance (PdM) also overlaps with this element. The goal of this analysis in the asset modelling value chain is to establish the links between condition data -> condition assessment -> failure modes.
Stemming from reliability engineering, Failure Mode, and Effects Analyses (FMEA and FMECA variants) form the backbone of our asset modelling approach. By understanding the specific modes of failure that affect asset performance allows us to draw links between how an asset fails and the subsequent events. The asset model structure uses a Reliability Centered Maintenance (RCM) form, comprising of:
Our probabilistic analysis considers two main aspects:
Both of these aspects can be affected by environmental, operational, asset-specific, and conditional data. The approach allows us to draw the links between failures, events and the monetary impact on your business. Our platform uses Weibull distributions to represent the probability functions.
Events are changes of state. Failures, interventions and inspections are all considered events when modelling. Each event can have a cost, risk, resources and tasks. The cost of an event is a combination of the sustained effects and response to that event.
Tasks detail what has to be done and when. Tasks are categorised as fault finding or corrective tasks, where fault-finding does not affect the asset in any way, and corrective tasks directly impact the asset's condition and/or performance.
Once the asset is understood and modelled, then analyses can be performed to answer one or more business questions. The goal of each of the analysis elements is unique, however, they draw from the same common asset model framework. Some examples of questions that analysis can answer are.
Maintenance strategy optimisation works by varying the applicable tasks and timings to determine the combination that delivers the most value, for the lowest (monetised risk) cost. Some subsets of maintenance strategy optimisation include Risk Based Inspections (RBI) as well as adaptive and online strategies. The optimised maintenance strategy for an asset can include elements of predictive, run-to-failure, time based, condition-based, or other approaches at a failure mode level. The strategy can also and is often optimal to vary over time.
Operational strategy optimisation is less common than maintenance strategy optimisation, however it still has it's place in increasing value. It works by altering the operational requirements of the asset, and measuring the trade-off between output and reduction of asset life. Overloading or under-utilisation can have a dramatic impact on asset performance as well as life. This optimisation approach aims to determine the operating parameters that will deliver the most value over the period.
Determining the appropriate intervention decision around replacements and refurbishments is another area that can be improved. Each option may be beneficial at various stages throughout an asset's life, and modelling this can help determine the best way forward given the asset's current condition and context.
When modelling the complete value chain from condition to tasks, to costs and risks, an asset model can be used to generate profiles for budget setting and reporting. The trade-off between cost and risk is often one that is overlooked and/or poorly understood. Incremental additions to the maintenance strategy often result in over maintained assets. Understanding these relationships allows for traceable, and transparent decisions to be made, enabling effective management of risk and cost.
Equipment selection is an important decision making point for replacement equipment as well as new or greenfield projects. Optimum equipment selection is determined by varying the asset characteristics to select the best combination for the environment, operational requirements, and risks.
The results of the analysis are then converted into a usable format. Some examples are, but not limited to:
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