April 26, 2019, 7:39 pm

Asset Health and Condition Monitoring: How to Establish a Successful Program - Part 2

Welcome back. In Part 1 of this series of 3 articles, we talked about the elements of a Condition Monitoring system and their relationships. We stressed the importance of thinking about it as a continuous cycle that drives bottom line improvements to your overall Asset Management strategy.

Five steps to establish a successful Asset Health Condition Monitoring program.

The five steps involved in implementing an Asset Health Condition Monitoring Program are as follows:

  1. Identify the Assets and their Models
  2. Define and understand the Variables
  3. Select Monitoring Equipment and establish a Data Collection and Storage System
  4. Define and follow a consistent Condition Assessment and Health Ranking System
  5. Integrate the Condition Monitoring System into your existing Maintenance Strategy

In this article I will look at the first two of these steps and we will examine the last three in the final instalment of this series.

 1.   Identify the Assets and their Models.

The first step is going to set the tone of the rest of your program. It will help you identify what variables you need to measure, what data you need to collect and how to transform this into valuable decision-making information. Depending on the reliability and availability goals of your organisation, knowing the health condition of a particular asset will help you decide the type and timing of the maintenance that needs to be performed on it.

First you will have to select the assets that will be monitored. A good place to look to get a sense of which assets are worthy to be included in this program is your equipment criticality matrix. Typically, those assets with higher associated risks and higher financial or safety consequences of failure will make a good case to be included in the Condition Monitoring program. Validate your selection criteria with the appropriate stakeholders within your organization including Finance, Operations and Maintenance. Achieving early buy-in from the team will make it easier to get the resources you’ll need for implementation in the future.

Once you know which assets need to be monitored, you have to decide which condition and health assessment models you are going to use. I would venture to state that these models are one of the most important components of a Condition Monitoring system. The models are the “translators” that will help you transform the data being collected into a useful diagnosis that will indicate the current “health” of the asset, whatever health means (more on this later). Without them your system is that, just data.

These models might take various forms and I would encourage you not to think about a specific embodiment of the model but rather the output that it should produce. A model is a conceptual representation that helps us visualise this “black box” that takes data as an input and produces a condition assessment as an output.

The model you choose will depend on the type or class of equipment for which you are trying to assess the condition of.

Over the years since the industrial revolution, the various engineering disciplines have reached varying degrees of understanding on different classes and types of equipment. This means that for each class of asset there will be more or less advanced condition assessment models available to you. The best way to approach this selection process is to consult subject matter experts in the various classes of equipment that you plan to include in your program.

These models might take the shape of a simple “if” rule, a software tool, or even be an organisation or expert that will interpret the data that you give them produce a judgment about the current condition and health of the equipment.

The output of this step is a list of assets and associated condition and health assessment models.

A simple example would be that of a chute. The internal walls of the chute are fitted with wear plates which have the function to protect the walls of the chute and maintain its mechanical integrity. As the plates wear, their thickness decreases until they are too thin to perform their function reliably and have to be replaced.

In this case, the condition of the chute liners is determined by its thickness. Each plate will wear at a varying rate depending on factors such as: ore abrasiveness, position of the plate within the chute, ore speed, average lump size, etc.



100% LIFE (NEW)

150 mm

75% LIFE

130 mm

50% LIFE

110 mm

25% LIFE

90 mm


70 mm


A simple way of describing this is: CONDITION = f (WEAR PLATE THICKNESS).

The model should be capable to describe the condition of the asset when the factors of the model are known. In our example it would be something like the following table.


What we need as an output of the model is an assessment of the condition, which in our case is directly related to the wear plate thickness. Often the condition of the asset will have to be described by more than one factor with more complex relationships. My advice would be to make the model only as complex as necessary to achieve an acceptable degree of confidence in our assessment of the condition. Thickness is a sufficient descriptor of a wear plate’s condition and that’s where we stop. We could add more parameters and make the model more complicated but we won’t because it’s not necessary.

One more thing, once you select a model for a particular class of asset, stick with it. Or at least make sure that any changes are properly documented. Five years from now, when you have moved on, the person managing the system has to be able to obtain consistent health assessments as additional data is collected and understand what “tweaks” or changes have been done to the system over time. By doing this, you are building robustness into the system.

2.   Define and understand the Variables.

After having identified what assets and models you will be using, the next logical step is to properly understand what parameters you are trying to measure and why. In this sense the models themselves will be a great guide in terms of what inputs are required to make them work.

One of the common pitfalls you have to avoid is going “measuring happy” or as one colleague used to put it, turn your plant into an “engineer’s playground”. We engineers love to play with data and make nice trend lines and charts in Excel™, even if at the end we don’t really know what to do with them.

In order to establish an adequate Condition Monitoring system, it is essential that the parameters to be measured are carefully selected and defined. If a variable is to be worthy of capture and storage, it has to be a significant indicator of the asset’s condition or play an important role in the overall health model for a particular piece of equipment.

The chosen variables will have to be clearly mapped to a model, even if it’s a simple one. The simple rue is: if there is not a clear relationship between the variable being measured and the model, don’t include it.

Continuing with our chute example, we saw in the previous step that the condition of the chute is a function of its thickness. The wear plate thickness would be then the obvious variable to monitor. However, it might also be valuable to monitor other variables which might enrich our knowledge of the system. In our case we might want to monitor the qualities of the ore (such as its abrasiveness)or the amount of ore moving through the chute.

This acquired knowledge, which will be very specific to your plant and operational circumstances, will give you the additional capability to not only assess the current condition but also predict the wear rate and therefore future states of the asset condition over time. This would start your journey into Condition Based Maintenance, but this is a topic for another article.

An important point here is not to confuse the variables that make a condition monitoring system with the measurements that you need as part of the automation, protection and control functions of your plant. The parameters measured for automation, protection and control purposes can be incorporated into a Condition Monitoring program but not all of them have to be used. Don’t feel compelled to collect and store a piece of data just because it’s there, available. Some measurements and variables have a specific function within the context of a particular system, but that doesn’t necessarily mean that they have to be used as, or even that they are, good condition monitoring inputs.

This step will add the next layer of detail to your system, which will incorporate all of the associated input variables of the models. With this in hand you are ready to jump on to the next part, selecting monitoring equipment and establishing a collection and storage system.

In Part Three of this series, we’ll we will discuss the next three steps in setting up a Condition Monitoring program.

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Carlos Gamez

Formerly Assetivity, Principal Consultant

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