statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.

Production Downtime and OEE: Why Machine Data is Valuable

Smart Manufacturing is the use of one or more technologies including IoT, automation and robots, big data, artificial intelligence, and modeling to optimize manufacturing. An efficient manufacturing operation means, among other things:

  • Product flows through the plant seamlessly;
  • Machines have the highest asset utilization possible to drive as much production and revenue as possible; and
  • The plant’s machines, operators, and systems are providing data that is shared across the organization to enable visibility into the factory floor for making valuable decisions throughout the organization.

Optimizing of Manufacturing

Companies can optimize manufacturing in product concept generation, flexible operations for changing customer needs, manufacturing efficiencies, machines becoming self-optimizing and self-diagnosing, and creating a dynamic manufacturing supply chain.

Manufacturing Efficiencies

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better asset utilization, production capacity, and lower overall costs of production. The higher production capacity and asset utilization, and flexible operations enable them to handle more dynamic customer needs and a dynamic supply chain. They can then dominate their market as a more flexible and consistent low-cost producer with solid quality and delivery to dominate their markets.

Need Data to Improve Company

To reach these levels of better production capacity and low-cost production the key is people must have access to data about machines and how they’re operating… uptime and downtime, asset utilization ratios, the productivity of the machine, quality of the products it creates, it’s running condition, how the operator is running the machine, etc. With this data, companies can then monitor production lines and pieces of equipment, find issues, and make improvements.

Production Downtime

Unplanned production downtime is the time a machine is down due to circumstances that weren’t planned ahead of time. This includes times when:

  • The machine is available but not running: the machine could be used and there is a product to produce but no operator is available, or there is an operator but no raw materials to produce a product.
  • The machine is not available: The machine is down because it had a failure and is under repair.

Production downtime is one of the biggest “killers” in production efficiency. It leads to material loss, resource loss, uptime or asset utilization loss, and capacity loss. Additionally, it reduces a company’s ability to deliver products to quality standards which leads to missing promised delivery dates and lost trust with a customer. In the end, it all results in higher costs and less revenue.

Benefits of Tracking Downtime

Tracking downtime gives you the data you need to both find root causes of downtime and make improvements to users, machines, and processes. When downtime improves then production capacity and asset utilization increase, costs of a product are reduced, revenue increases, and margins increase.

Downtime Study

In 2017, GE ServiceMax commissioned a study that surveyed 450 decision-makers across manufacturing and other industrial verticals. They found companies had issues as a result of unplanned downtime including:

      • 46% of companies couldn’t deliver services to customers
      • 37% lost production time on a critical asset
      • 29% were completely unable to service specific equipment

They also found that production downtime in a factory affects 62% of its productivity with scheduled resources and equipment. These numbers indicate unplanned downtime is common and has a big impact on companies.

statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.
Downtime Fallacy

Companies who don’t track production downtime of their machines may believe they know what the equipment downtime is. However, they are often off by as much as 50%. Without the actual data to measure and analyze, many companies are not seeing the whole picture, and taking all information into account when measuring downtime – and this can greatly impact their business.

This article is part of a series, if you found this information insightful, check out our next article on downtime and how to use the data.


Why Should I Calculate OEE Manually?

OEE (Overall Equipment Effectiveness) is a valuable KPI for how well machines on the plant floor are being utilized. (More information on what OEE is can be found in the Intro to IoT blog post) It is often thought to be a complicated value to calculate, and sometimes it can be. However, it can be greatly simplified. Given the value and the potential simplicity, it is recommended that operations staff including their supervisors and managers should know how the calculation works, what OEE is, and how to use it effectively.

Why is OEE Valuable?

OEE has a direct correlation to the revenue the machine provides the manufacturer. If the machine isn’t utilized well, i.e., if it’s down or if it’s producing poor quality parts that cannot be sent to a customer, then the machine isn’t generating as much revenue as it otherwise would.

It’s also valuable because breaking the value down into is parts can help hone in on the cause of issues with a machine. It is made up of three separate percentage values: Availability, Performance, and Quality. One can look at the component values and determine where to start their analysis to find issues with the machine.

Why Calculate Manually

Why should operators and related staff calculate OEE manually? Two reasons:

  1. To understand what OEE really means and what it’s made up of, and
  2. To get a solution calculating OEE in place when an automation solution isn’t available.

Understanding OEE: One can only understand another person’s perspective when they’ve walked a mile in their shoes. In the same way, a person can only understand a concept once they’ve recreated it themselves and worked with it. The nice thing is that OEE, or at least some parts of it, can be calculated easily on paper.

Automation isn’t available: In many manufacturing companies, OEE will likely be calculated automatically by various systems such as IoT software platforms (these are the solutions we often implement). These solutions are becoming more predominant as companies look for more ways to pull valuable data from their machines to complete…all under the moniker of Smart Manufacturing. However, some companies may want to start utilizing OEE before budgets can be made available for these solutions. So, why not start when pen and paper!?

How to Calculate Manually

A deeper discussion of the calculation of OEE can be found in this How OEE is Calculated blog post. So we won’t cover the details here.

To calculate OEE all you need is a pen, paper, maybe a clipboard, and diligent effort to record and calculate the data regularly. It is also helpful to have Excel available. Don’t need anything fancier than that.

You can start with Availability, one of the components of OEE. Put the paper, pen, and clipboard close to the machine to measure. Then every house has users mark what happened with that computer regarding its availability: If it was up and running at normal speed mark a “G” for Green (can include planned downtime if appropriate). If the machine experienced some setup or adjustment time during that hour, mark a “Y” for Yellow. If the machine was down, mark “R” for Red. You could also use color pens/markers and/or a whiteboard.

Then count the total number of G’s, Y’s, R’s. Subtract the total number of Y’s and R’s from the total number of G’s to get the number of hours the machine was available to run. Count the total number of hours overall on the paper no matter the condition for the scheduled operating time. Then divide available time by the scheduled operating time and you get the Availability Rate % value.

Do this for each day, record the results in Excel, maybe even create a simple chart, and you’ll start to see a trend for where the OA (Overall Availability) for the machine is. If you do this for multiple machines you can then get some solid, objective data on the status of the various machines, and which might need some attention.

You can also repeat this process recording similar data for Performance and Quality.

How is OEE Calculated?

OEE (Overall Equipment Effectiveness) provides an indication of how effectively machines are utilized on a manufacturing plant floor. It’s valuable for identifying and removing production constraints and for driving up revenue earned by every machine.

The calculation has some mysticism surrounding the complexity of its calculation. We’ll simplify the calculation to a level where the reader can immediately start calculating it by hand.

How to Calculate OEE

OEE for any machine is made up of 3 parts multiplied together: Availability (up-time) x Performance (production speed) x Quality (widgets produced correctly the first time). Below is a brief explanation of each part and how it’s calculated.

Availability Rate

Machine’s up-time, the percentage it is ready to produce products and is working properly, excluding changeover and setup time.

Formula: (Scheduled Operating Time – Downtime) / Scheduled Operating Time

Note that Scheduled Operating Time is not “total time”. Availability refers to when the machine could be running based on when it is needed or planned to run. There could be reasons it won’t run when it is needed (i.e., setup, breakdown) and this calculation must account for those downtime reasons.

Performance Rate

The rate a machine actually produces products relative to it’s best known or standard production rate.

Formula: Actual Output / Standard Output

Note it’s critical to come to terms at your plant with what the standard production rate is for your equipment. It’s best not to use the specified or design production rate by the vendor of the product. In this case, if the machine were running faster than the design production rate and you were using the designed production rate this could mask quality or availability issues in the OEE calculation.

It’s also worth noting that the production rate will highlight losses due to idling, slowdowns, and minor stoppages.

Quality Rate

The rate the machine outputs good parts.

Formula: Right First-Time Output / Actual Output

Note that products produced by the machine which require rework or any sort of adjustments, along with scrapped products, are not counted as a quality product.


Now let’s run a quick example calculation for one shift:


Scheduled Operating Time: shift 8 hrs or 480 mins, 20 mins planned downtime, 0 mins breaks; total 460 mins

Downtime: breakdowns 30 mins, setups and adjustments 15 mins, minor stoppages 15 minutes; total 60 mins

Available time = Scheduled time – Downtime = 460 – 60 = 400 mins

Availability rate = (Scheduled Operating Time – Downtime) / Scheduled Operating Time = (460 – 60) / 460 = 87%


Actual Output: 400 parts or 1 part/min for 400 mins of Available time

Standard Output: 800 parts or 1/2 part/min for 400 mins of Available time

Performance rate = Actual Output / Standard Output = 400 / 800 = 50%


Right First-Time Output: 400 parts – 20 defective parts = 380 parts

Actual Output: 400 parts

Right First-Time Output / Actual Output = 380 / 400 = 95%

OEE = Availability x Performance x Quality = 87% x 50% x 95% = 41%

Additional Considerations

One must keep in mind that OEE is not a value that can be used to compare the performance of many different machines, different production lines, or even less so various plants. As you can see above, there is a lot of uniqueness built into the calculation for each machine. Each machine is unique for the schedule required of it, it’s inherent production rate (by its design), and other factors. Therefore, OEE is really only meant to be used as a metric to improve the performance of each individual machine. Monitor the OEE value for the machine, break it down into its component pieces, and hone in on the problem, fix, and then watch OEE to see if it improves after the fix.

Even if the above is true, OEE can be used to compare machines and lines, but only if they are used in a similar fashion and have similar demands.

Comparison of OEE across different lines and plants can be done but should only be done 1) when using an average OEE value, and 2) with the understanding the value will not be highly accurate.

Introduction to OEE (Overall Equipment Effectiveness)

Many companies have jumped on the bandwagon of using OEE (Overall Equipment Effectiveness)

as a KPI for their manufacturing equipment, lines, and plants. This article will introduce what OEE

is and briefly discuss how it should and should not be used.

What is OEE

OEE indicates how effectively machines are utilized on the manufacturing plant floor. Another way to put it is OEE is the percentage value the machine is performing up to its true potential.

OEE is made up of three parts, each of which is calculated as percentages, and the total value is calculated by multiplying all three parts: Availability rate x Performance rate x Quality rate. Ex: 70% Availability x 80% Performance x 90% Quality = 50% OEE

Each of these three parts can be further broken down as follows:

Availability (uptime of the machine)

  • Equipment failure or breakdowns
  • Setup and adjustment

Performance (speed producing widgets)

  • Idling and minor stoppages
  • Reduced speed of operation

Quality (products produced correctly the first time, without rework)

  • Process defects (scrap, repairs)
  • Reduced yield (from startup to stable production)

Why is OEE Valuable?OEE

This is very valuable because the utilization of an asset has a direct correlation to the revenue the machine provides the manufacturer. Of course, you want to make sure that your machine is operating as efficiently as possible. paying attention to OEE will help you to keep your machines running more, with less downtime. OEE can also assist in making sure that your machine is making a good quality product to be sent to the customer. If your machine is down or making poor quality products that cannot be sent to the customer, then the machine is not generating as much revenue as it otherwise would.

Having machines down or just not working at an effective pace can greatly affect the overall production of the plant, and cause you to miss deadlines. That means less happy customers and a higher chance of them taking their business elsewhere. Happy customers mean more business, more money, less stress, and a great strong reputation to retain a good customer base and push your growing business up to the next level.

The bottom line is that your machines across the plant floor will be more productive, making better products. Making sure that your machines are being used to their full potential will lead you to generate higher revenue.

How to Use it

Here are 3 ways we recommend using OEE if your company is new to this manufacturing KPI:

  1. Find machines that need attention: A good manufacturer will attempt to remove all constraints in its production process whether or not that additional capacity is currently needed. Users can find constraints or issues with machines by looking at the individual OEE values for each machine and determine which are performing effectively and which are not. Lower OEE values will indicate which may need some attention. Addressing issues with machines and thereby improving their operating effectiveness can then increase the revenue the machine produces.
  2. Find the cause: Once a machine with a lower OEE is identified the staff can look at each of the three components of OEE (Availability, Performance, and Quality) and find a leading cause of the OEE difficulties. Additional analysis is then required to find the cause and fix. Then setup improvement projects to address the issues.
  3. Confirm improvement: Once the issues are addressed with the machines continue to monitor the OEE values for those machines. If the OEE numbers go up, especially in the parts where the issues were addressed, then the efforts to fix may have been worthwhile. Otherwise, the machine and its issues may warrant a second look.

Breaking it Down: Building Blocks of Smart Manufacturing

A Smart Manufacturing company is a manufacturer with fully integrated solutions that empower it to be highly flexible and responsive
to changing conditions, with self-healing systems. Those capabilities enable a company to reduce costs, generate more revenue, and

increase their competitiveness in their market.

So how does it all work, and where do these capabilities come from?

The Nine Blocks that Stack Up to Smart

There are 9 technologies or building blocks that stack up to create Smart Manufacturing.

  1. IoT (Internet of Things): Taking data from sensors on machines, moving it over the internet, and converting it to valuable information to help people make decisions and drive action.
  2. Automation and robots: Using robots and software to enable machines and people to work together more efficiently.
  3. Big data: Storing and using massive amounts of machine data for data analysis to gain insights about operations and machines.
  4. AI (Artificial Intelligence): Using software tools to build models and algorithms which can learn about niche areas of your facility to distinguish between normal and critical conditions.
  5. AR (Augmented Reality): Viewing models or images overlaid onto the real world on a mobile device.
  6. Additive Manufacturing: Creating objects from 3D models by joining materials layer by layer.
  7. Modeling: Portraying or defining aspects of the physical world in digital form for gauging equipment status, simulating operations, or controlling live systems.
  8. Cyber Security: Applying old and new ideas of encryption, protecting attack vectors, and combining digital with physical security tactics to protect data in motion and data at rest.
  9. Cloud: Moving away from local, on-site servers towards 3rd parties like Amazon, Google to store, service, and process data.

Stacking the Blocks

Each of these technologies can deliver value on its own. However, companies can realize exponential value by combining technologies.

Here are a few examples:

     Predictive Maintenance

  • Technologies: IoT + AI
  • Method: AI experts create a machine learning model. IoT pulls data from machines and channels it through the model to assess the probability that a part or whole machine may fail within a certain period of time.
  • Benefits: Clear identification of critical maintenance windows, resulting in up to 50% cost reduction by addressing issues only when needed but before failure.

     Robot-Assisted production

  • Technologies: Robots + Automation + IoT
  • Method: A “human assistant” robot is programmed to work alongside people to handle repetitive and mundane tasks or sets of tasks. The robot is pre-programmed to handle multiple task sets. Input from IoT data (such as production data further up or down the line) directs the robot to change its tasking based on real-time production needs.
  • Benefits: Increased production with existing staff and reduced pressure to hire/replace workers.

     Data-driven quality control

  • Technologies: IoT + Automation + Big Data
  • Method: Set up machines to test products immediately after a certain stage of production. Automatically analyze that data relative to product quality standards. Based on results, either trigger alarms for humans to intervene or have the equipment modify its own behavior appropriately to meet the required standards.
  • Benefits: Earlier notification of equipment problems, earlier identification of product quality issues, reduced waste, and rework. Increased production of a product to standards.

Choosing the Right Blocks

Some of the most exciting benefits of Smart Manufacturing come from the myriad and creative ways that new technologies can be combined and implemented. To find the right combination requires effort: thoughtful planning, employee input, and focus. But even small and mid-size companies can increase their competitive position, with access to the same building blocks as larger manufacturers.

What Is Smart Manufacturing and Why Should You Care?

Smart Manufacturing is a term with many definitions that all have one theme in common:

using new technologies and capabilities to gain a competitive advantage.

The Ideal and The Reality

At Ectobox, we say that a company is a “Smart Manufacturing” company when they have fully integrated solutions that empower them to be highly flexible and responsive to changing conditions. A Smart Manufacturer produces a quality product, uses finely-tuned processes, monitors their supply chain and coordinates it with production, and overall, maintains systems that work well together. Theoretically, the best Smart Manufacturers can even fix their own problems automatically.

But what do we typically see the most of? Engineers huddled around a problem trying to solve it. Inventory pilling up in various work areas or cells. Inventory “starvation” in other areas. People and machines not working when they should be. These situations are so common that we see them at almost every manufacturing company we visit. The good news is that these issues are all fixable. The bad news is that manufacturers who don’t make the effort to eliminate these issues and increase their efficiencies are the ones who will struggle to stay competitive.

Why it Pays to Pursue Smart Manufacturing

Besides the appeal of new technology, why does Smart Manufacturing really matter to the average company? It matters because the specific technologies and capabilities under its umbrella can make companies more efficient, enable cost reductions, and potentially, drive new revenue. Some of the capabilities Smart Manufacturing offers are:

  • Better insight into what’s going on at your plant floor
  • New and better products and services to meet your customers’ needs
  • Faster development and launch of new products
  • Reduced costs of production
  • More revenue per employee and per machine
  • New business models (such as selling your products as a service)

Smart Manufacturing is the Future

Manufacturers all over the country are under pressure to be more competitive and overcome new challenges. In reality, factories of the future probably won’t look much different than they do now. The biggest difference is that they will be working much more efficiently. The machines and people and supply chain of the future will be more flexible, better-coordinated, and self-healing. The technology is already here, so companies can start getting “Smart” right away.

In our next blog post, we will talk about the specific technologies within Smart Manufacturing.

IoT Solution Success: Start Small, Work Efficiently

The best way to begin implementing an IoT solution is to start small – but what exactly does that mean, and why is it important?

In this post, you will learn how to plan your first IoT solution by looking for a simple problem that IoT can solve quickly, and why that ensures success.

How to Start Small

When we say to start small, that means don’t try to create a “big bang” solution. It’s natural to want to solve all your problems at once or make a huge impact on the market. But the driving goal in integrating IoT into your business should always be improving how the business runs. Successful businesses tend to grow gradually over time, and implementing solutions to solve business problems the same way allows the process to mature and evolve over time. So, always begin by defining your desired business outcomes and requirements. You can think big as you consider how this will improve your overall productivity and grow over time but then start with something simple as the first step.

Find a Simple Problem to Solve

The best way to figure out where to start is to pick a problem to solve. Perhaps you run a manufacturing plant with pools of cooling water, and your pumps are often down or not functioning properly. This can be the problem you want to solve.

In order to do so, you’ll need to ask yourself some problem-solving questions such as, “Do I know how much uptime/downtime my pumps have?” or, “Can I monitor the motor speeds of my pumps?” You can even ask questions such as, “Can I view my data remotely?” Perhaps you have meters on your pumps to track their performance, but you need an employee on-site at all times to monitor them.

When you focus on one problem, you can define factual questions that will help point to a solution.


A lot of companies get lost at the implementation stage. . While they have identified one problem they wish to solve, they may be unsure of how to begin solving it. Just remember: focus on the simple problem. It’s easy to get confused when, as one problem is related to others, but you will be able to solve those later.

Ensure you have the best people on the job. Find IoT experts to work with and assign employees that have the desire to improve your operations and who are open to change. A great way to ease everyone into this change is to pick an off-the-shelf solution. There’s no need for custom solutions in IoT when you are first starting out. Custom solutions are expensive and can end up evolving into large-scale projects before any basic implementation. PTC’s Thingworx is a great example of an off-the-shelf solution that is designed with manufacturing in mind. It is user-friendly enough that anyone can use it, from the shop floor to the top floor.

By solving that one simple problem, you’ll have achieved your initial success, and created a model for solving future problems.


Implementing an IoT solution may seem like a huge undertaking, but it doesn’t have to be. If you think big but start small, you can avoid many of the common pitfalls. Focus on how IoT can help the business, and find the right people to help you. Reliable employees who want to grow with the company in combination with experienced experts will ensure you can get your first solution out the door. If you have questions or need advice on how to get started with IoT, feel free to give us a call at 412-923-3002 and one of our experts will be happy to help.

Ectobox celebrates 20 years!

The Past

2018 marks 20 years of Ectobox developing solutions outside the box.

Ectobox began as a software consulting firm driven by a simple idea: we don’t like to see companies suffer needlessly doing work manually that can hold a business back when there are opportunities to automate processes. Our President and founder wanted to help. With a servant leader mentality, Ectobox moved forward with helping other companies improve, get better at what they do, and grow with software and data.

The Present

IIoT, Software Expertise and the Manufacturing Sector

Ectobox has recognized the power and value that IoT can bring to manufacturers in becoming better, more efficient organizations. Therefore, Ectobox has transformed into an IoT and software consulting and solutions company. We are able to help manufacturing companies grow with strategic level consulting as well as the tactical details of technical implementations. We have made this transformation by, among other things, being thought leaders and experts in IoT, making strategic moves to expand our consulting and technical capabilities, and using the latest tools and technology including PTC ThingWorx, AWS IoT, and Azure IoT Hub.

The (near) Future

Continuing to Share Manufacturing IoT and Software Knowledge

As Ectobox sees significant company growth in size and market presence, we hope to continue to build strong partnerships with all of you that last into the next 20 years, and beyond.

We have many exciting trade shows, speaking events, and webinars coming up at the end of this year and into 2019 including:

How to Profit from IoT Webinar – October 3rd
Business Transformation with IoT – October 17th
Manufacturing Day – October 4, 2018
Industrial IoT World Atlanta – October 29-30
Digital Bridge Industry 4.0 Conference – November 1st
Westmoreland Chamber IoT Roundtable Panel talk – November 2nd (registration info TBA)
NWIRC IoT/Industry 4.0 Webinar – November 7th (registration info TBA)
NWIRC IoT/Industry 4.0 Webinar December 11th (registration info TBA)

The Value of Condition-Based Monitoring. Part II: Technology

Last time, we discussed how to prepare to implement an effective condition-based monitoring system for your operations.

In this month’s post, we’ll show you how to choose the right technology.

Choosing the Right Tool

With the operating parameters and failure modes defined, you can now add effective monitoring to your operations. Many companies currently employ humans to monitor the conditions manually. Maintenance rounds are a common part of maintenance staff duties. The employee will walk around with a clipboard, take measurements from the equipment, and then log the data into some log format. Ideally, they compare those data points to required operating conditions, and if they see some operating value out of bounds, identify impending issues.

Technology can help a lot here. A system such as an IoT software platform, can easily pull data from multiple pieces of equipment and automatically deliver warnings of potential issues.

There are two major components of success when implementing a technology-driven solution.

The Right Platform

The first key to success in a technology-driven monitoring solution is choosing an IoT software platform that is flexible enough to handle nearly any piece of equipment for configuring how the equipment operates, as well as the multiple ways the equipment could fail, and what automated notifications to send. Additionally, the software should be able to read the data from nearly any piece of equipment. Some software tools are proprietary to a specific hardware product and won’t work for all equipment, so you will want to avoid those.

A tool like ThingWorx from PTC has the flexibility to handle any piece of equipment and read data from nearly any data source. It can also be set up to provide warnings of any type, and it can integrate with a company’s CMMS. And it’s scalable. When installed, the reliability team can start small, tackling one component of a single piece of equipment for testing purposes. If that works, then they can easily add other components and equipment. The tools for setup and configuration allow for fast setup and long-term solutions.

Choosing the Right Factors to Monitor

The second key to a successful monitoring solution is understanding what the most impactful components, operating conditions, and failure modes of the component are. That information can be configured into your IoT software platform. Once an abnormal condition or failure mode is noticed by the automated system, it can throw an alarm, send a text message, or put in an automatic request for service from the maintenance team, etc.


Let’s put all of this info into a short example. For a high-pressure air compressor, one of the components is a pump. The pump’s purpose is to pump oil to the compressor lubrication system at a target pressure of 25 psi. The machine can continue operating without affecting the larger equipment even if the pressure is maintained above 20 psi. So if the pressure drops below the optimal 25 psi but hasn’t gone lower than 20 psi ThingWorx could throw a “yellow flag”. Then, if a maintenance person has the time they can look at it while it is still operating effectively. However, when the pressure drops below 20 psi, the system delivers a red flag that warrants immediate action.


Condition-based monitoring is important because 50% of manufacturers become aware of a problem only after a breakdown has occurred, which results in downtime, lost revenue, and high repair bills When an issue is caught and fixed before it breaks down, a company can expect a significant decrease in maintenance and repair costs as the equipment is protected from a more significant or catastrophic failure.

The Value of Condition-Based Monitoring. Part I: Preparation


Diligent monitoring of the condition of equipment at manufacturing plants (condition-based monitoring) can have a big impact on the company. This is an increasingly urgent issue in a market where approximately $65 billion worth of automation systems are at or near the end of their useful life. The oldest equipment still in use dates back to 1938.

Condition-based monitoring can greatly reduce the cost of repairs and keep the company assets up and running for longer periods. At the same time, it allows the company to reduce costs and generate more revenue.

You’ll find below the way to begin condition-based monitoring in your operation. Keep in mind that the work outlined below should be performed within the context of a business case to ensure the work is has a good ROI, is based on an incremental process of Proof of Concept through Production, and that it is performed in a culture that drives and welcomes change for the betterment of the company and all who work there.


To monitor equipment, there are three prerequisites you need to ensure that you can prevent major issues and realize the resulting cost saving and additional revenue. They are:

  • Know the equipment
  • Know the Issues
  • Have a tool

Know the Equipment

Before you start hooking up sensors and software to equipment you need to know your equipment:  how it needs to operate for optimal outcomes, its suggested limits, and how it can fail. The team working to set up condition-based monitoring must understand the technology and the operations of the equipment so they know what is normal and what is abnormal. They can then translate this information to the monitoring system.

You’ll want to record this information for both the primary function of the equipment (its main purpose) such as a high-pressure air compressor that supplies compressed air) and also the functions of its components (e.g., electric motors, pumps, valves, etc.). If possible, this information should be organized into a hierarchical diagram, with primary functions at the top and lower level support functions further down the diagram.  It is important to document the individual component functions because those functions and breakdowns ( loss of function) can negatively impact the whole piece of equipment and ultimately, the performance of the plant.

Next, you must define and agree upon the operating context of the equipment. The operating context means the desired operating parameters of the equipment. For example, what are the capabilities of the equipment and its components, what are the optimal minimum and maximum operating parameters for the equipment (e.g., power, flow, pressure), and suggested shutdowns and maintenance work. You can usually find all this information in operating and maintenance manuals.

Know the Issues

Once you have defined normal operating conditions, you are ready to define the failures or potential failures What are the different ways that a component could fail? What is the potential impact on the equipment and other components if the component in question fails? How can that failure be identified in terms of operating parameters (e.g., pressure rises above 100 psi) or other data (e.g., the temperature of the motor casing rises above 200 degrees). These definitions are referred to as failure modes. Along with defining the failure modes, the team should define proactive tasks that can be performed to mitigate or eliminate the issues.

Next time, we’ll discuss the keys to successfully implementing a condition-based monitoring system.