What is Condition Monitoring (CM)?

Condition Monitoring forms the foundation of what has become known as Industry 4.0. In its basic form, the term is fairly self-explanatory and refers simply to the act of monitoring the condition of an asset.

Within the IIoT ecosystem, an integral part of Condition Monitoring is providing data that can then be used for Predictive Maintenance (PdM) and other smart factory applications such as Digital Twin.


Condition Monitoring Vs. Condition Assessment

When a technician performs a routine visual check of a component in a plant, it’s a pre-scheduled and momentary snapshot of that component’s health, regardless of historical performance data or previous inspections. This type of inspection is known as Condition Assessment.

With Condition Monitoring, we take into account a much broader set of granular data that includes sensor data from the asset, previous inspections, other components of the same type, location and condition of the plant, and historical trends. An analysis is made that not only defines the component’s current status but also predicts future issues and when they’re likely to occur, including when the part will need replacement.

The 6 Main Benefits of IoT Condition Monitoring

The 5 Main Benefits of IoT Condition Monitoring

Condition Monitoring offers multiple business benefits, including secondary advantages earned from the reduction in costs and resources that this technology enables.

The core benefits of condition monitoring can be summarized as:

Reduced maintenance costs

Maintenance becomes proactive and timely, cutting labor and travel costs, and repairs are done before critical damage occurs. Service time is reduced, and customer satisfaction improved.

Maximized production

With accurate and extensive readouts from sensors on production machines, combined with data analytics algorithms to gain visibility into production inefficiencies, new levels of productivity can be reached. This is especially true with condition monitoring in the oil and gas industry.

Optimized inventory of spare parts

Rather than overstocking the inventory of expensive spare parts, which impacts margins, or running low on inventory, which increases downtime, Condition Monitoring enables accurate forecasting of the demand for spare parts.

Accurate and relevant data for driving product development

Asset behavioral data collected over time can be aggregated and analyzed by engineering to identify product design flaws that can be rectified in subsequent product versions.  

Extended machinery lifetime

The health of a machine and all of its components is monitored in detail. Overheating, wear-and-tear, and other threats to the machine’s well-being are taken care of in a timely manner, lengthening the machine’s lifespan.

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Condition Monitoring


Condition Monitoring Techniques

The implementation of condition monitoring differs greatly from one manufacturer to another. This is largely because of how every product or asset has its own unique pattern of “normal” behavior which must be monitored and analyzed.  

Here are some of the more commonly used Condition Monitoring techniques:

  • Vibration analysis
  • Lubricant analysis
  • Infrared thermography
  • Acoustic emission
  • Ultrasound
  • Motor current signature analysis (MCSA)
  • Model-based voltage and current systems (MBVI)

As an example, let’s take a look at this last method using the following illustration:

Condition Monitoring techniques
Condition Monitoring with MBVI

In the above flow diagram, after running a voltage through a motor, the current is measured and compared to that of a mathematical model that is fed with accurate real-time data from the same motor. The two current readings are summed and compared. In cases where no deviations are evident, the motor (or system) can be regarded as healthy. If there are discrepancies between the mathematical model and the actual motor, we move on to the analysis stage to find out exactly what the problem is. Once the problem has been identified, we can classify it and deploy the relevant solution.

In this example, the idea of the permanence of Condition Monitoring becomes clear. It makes sense to constantly be able to monitor and record the motor’s status, instead of only momentarily performing a diagnostics check. This way, historical trends are captured automatically showing us how mechanical, electrical and operational problems and their parameters change over time.


Condition Monitoring Software

With sensors in place to record the various parameters of the machines as they work, it would also be very useful to have an application to concentrate the information and communicate the required action. For this reason, the adoption of condition monitoring software is growing rapidly as manufacturers look for an easy and efficient way to interpret information collected by a CM system, and then take timely action upon it.

Seebo's Condition Monitoring system
Seebo’s Condition Monitoring system

The Seebo remote condition monitoring solution is an example of such software with the exception that it not only consolidates the Condition Monitoring data but also supports the planning and delivery of a complete condition monitoring system from scratch.

Where should the sensors be placed? What should they be measuring? How should they be calibrated? What alerts should they send out, and to where? All these questions and others can be answered using Condition Monitoring software, allowing stakeholders to weigh in on the design of a system at any stage.

Once remote Condition Monitoring has been implemented, the software continues to act as its hub, concentrating all the incoming data being reported by the sensors into a central repository, allowing for deep data analysis that drives corrective action.


Leveraging Condition Monitoring to Create Business Value

Condition Monitoring is really only the first phase in a larger cycle of industrial IoT maintenance. While monitoring the condition of an asset, data is collected and stored. If that data calls for an immediate action such as a repair or preventive maintenance of some sort, then a technician or team is deployed.

Regardless of the action taken, the state of the asset is stored together with its sensor data, in a big data repository. The data repository can be referred to for specific comparisons that require historical data, and can also be used to observe trends and formulate predictions.  

The accuracy and depth of the data collected from Condition Monitoring, and its reach with regards to encompassing an entire factory or plant, provides manufacturers with extremely valuable information that can be leveraged to make informed business decisions to reduce the impact of the Six Big Losses.

Using Big Data analysis, trends can be observed that form the foundation for accurate predictions, helping both day-to-day operations as well as triggering creative and proactive strategies for further growth.


Machine + Human = Best Outcome

Condition Monitoring fits into the overall Industrial IoT framework as a foundation block for continuous improvement. Insights from Condition Monitoring must be acted upon by humans, embedding the new knowledge into customer service processes, aftermarket sales, production planning, and new product development. Through Condition Monitoring and other IoT use cases in manufacturing, we improve our daily operations by containing service and production costs, increasing sales, and boosting customer satisfaction.

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