By Vigneshwar Kannan and Aliesha Aden
With rapid advances in IoT, there is now an overwhelming number of sensors available to support condition monitoring. From wearables to machine-mounted sensors, the technology landscape is packed with tools designed to help maintenance and reliability teams make better decisions.
When it comes to machine health, it is tempting to instrument everything. Real-time data, instant alerts, full visibility.
Why wouldn’t you want eyes on the health of every asset in your plant?
The challenge, of course, is cost.
Fully instrumenting an entire operation often comes with a significant price tag. Too often, a large portion of continuous improvement budgets end up tied to non-critical assets that make up the bulk of the fleet, while truly important equipment remains under-protected.
So how do you get meaningful visibility across your assets without overspending where the return simply isn’t there?
The answer is not more sensors. It is better decisions about where, how, and why you monitor.
This article outlines a practical approach to selecting condition monitoring solutions, one that focuses on impact, not just data.
Different assets need different approaches
At first glance, condition monitoring seems simple: deploy sensors, collect data, and prevent failures. But the moment you try to apply the same monitoring strategy across different machines… reality quickly pushes back.
There are many different failure modes within industrial equipment, which is why it is critical to select monitoring techniques that are suited to the specific issues you are trying to detect. Deploying a single sensor type across an entire machine, or even an entire production line, is rarely the most effective approach.
Even within a single machine, different components often require different monitoring methods.
Take a reduction gearbox as an example.
Bearings within the gearbox are best monitored using vibration-based techniques, which can detect specific fault signatures in the frequency spectrum.
However, when it comes to monitoring wear particles or lubrication degradation, vibration may not provide early enough warning. In these cases, oil condition monitoring is often better suited to identifying issues before they escalate.
In many situations, combining multiple monitoring methods provides the clearest picture, allowing teams to validate findings and narrow down root causes with greater confidence.
The key takeaway is simple:
Condition monitoring decisions should be made at the asset and component level, not at a blanket plant level.
The operating environment matters
Selecting the right sensor is not just about the failure mode. It is also about where that sensor will live.
Will it be exposed to high temperatures, washdowns, dust, chemicals, or continuous vibration?
Environmental and operating conditions can significantly impact both sensor performance and lifespan. In some cases, the environment itself can distort readings or lead to premature sensor failure.
Consider a MEMS vibration sensor. Its performance and accuracy are sufficient for most standard equipment, but results can vary significantly when it is used on machines operating at elevated temperatures, such as kilns. This is because certain properties that MEMS accelerometers rely on for accurate measurement begin to degrade with excess heat, typically above 80°C.
In these environments, piezoelectric vibration sensors are often a better choice. They maintain sensitivity and linearity at higher temperatures and provide more stable data under demanding conditions.
Operational and environmental context is often the difference between reliable insight and wasted spend.
Criticality should drive investment
One of the most common mistakes in deploying condition monitoring is applying the same level of monitoring intensity to every asset. This spreads budgets thin, diverts effort toward low-impact equipment, and leaves critical assets without the depth of insight they require.
Consider a small deployment of 20 vibration sensors as a ‘proof-of-value’ or trial. Once value is demonstrated, it is often easier to secure a budget for a wider rollout. The temptation in these early stages is to maximise coverage by allocating one sensor per machine.
While this approach increases visibility, it often limits diagnostic value.
Take a reduction gearbox that is critical to production. Spares may be expensive, and lead times for replacement could stretch into weeks. A single, well-placed vibration sensor may alert you to a change in condition, and in some cases provide partial diagnosis. But it rarely delivers enough information to support prescriptive maintenance decisions.
By contrast, allocating multiple sensors across the gearbox and motor, for example one per bearing, allows teams to isolate the faulty component, understand the nature of the fault, and act early enough to minimise secondary damage. When every hour of downtime can cost thousands, this level of insight can dramatically reduce risk and disruption.
At the same time, deploying sensors on low-criticality assets with minimal failure history often delivers little value, even as part of a trial. That budget and effort are usually far better spent protecting assets where failure consequences are high.
Asset criticality and failure consequences should directly shape how much you invest, and how you monitor.
The real value is in the right combination
Given all these factors, there is no single answer to the question, “What is the best condition monitoring system?”
The real value comes from using the right combination of technologies to address the right problems. The difficulty is not knowing what to monitor. It is managing multiple sensor types without creating unnecessary complexity for the teams responsible for acting on the data.
Most solution providers specialise in only one type of sensor. It is even rarer to find a provider that can aggregate, interpret, and simplify data from multiple technologies into clear, actionable insight.
Which leads to the real question:
How do you manage multiple sensors without managing multiple platforms?
One platform, many sensors
In practice, the most effective approach is a sensor-agnostic platform that can ingest data from multiple sources and turn it into clear, actionable insight.
If you have found this article, you likely already understand the value of an end-to-end solution, from sensor hardware through to prescriptive maintenance insights.
But the real power lies in flexibility. Being able to integrate third-party sensor data means you are never locked into a single technology or vendor.
Instead, the focus stays where it belongs: selecting the right solution for the problem at hand.
At MOVUS, we support this approach by combining a broad sensor range with platforms that bring those data sources together into a single operational view.
Want to learn more about our solutions range? Head to our Technology page now.
And if you want to sanity-check sensor combinations for your equipment, or still have no idea where to start, our team is always happy to talk it through.