Machine Condition Monitoring: Are You Fixing Alerts or Ignoring Them?

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Prateek Khare

Praful Dandgawal |

Most teams tune out their alerts. Here's how equipment makers turn every warning into a scheduled fix, lower warranty cost and new service revenue.

Machine condition monitoring means tracking how equipment behaves over time (vibration, temperature, pressure, current draw, oil quality) so that wear usually shows up weeks before a breakdown. The idea itself is decades old. What’s new is that sensors, cheap connectivity and AI now let an OEM do it across every unit it has shipped.

Yet ask most equipment manufacturers what it has actually delivered and you’ll usually hear the same answer. Plenty of data. Lots of screens. Oddly few avoided failures.

More sensors won’t fix that. What tends to fix it is software that reads signals as they land and decides which ones matter. For an OEM, that judgment can become something customers will happily pay for. In our experience, connected condition monitoring earns its keep only when people stop staring at it and the business starts running on it.

"Condition data is cheap. Acting on it at the right moment is what customers actually pay for."
– Prateek Khare, Saviant Consulting, 2026

Condition monitoring reads an asset’s health weeks before it fails

In practice, five techniques cover most industrial assets. Each one tends to catch a different kind of failure, and most mature programs combine two or three:

Common condition monitoring techniques

Drift is usually the earliest warning you’ll get. A worn bearing tends to change its vibration signature long before it locks up. Tired pumps often need extra amps just to hold the same flow. And a motor running hot today is, roughly speaking, a better bet to fail tomorrow than its cooler neighbor. So it’s no accident that AI vibration condition monitoring tools for industrial assets are where most programs start. Rotating equipment announces its problems early. If anyone is listening.

Catching the signal was never the hard part. Doing something useful with it? That’s where things get messy.

Most industry condition monitoring programs stalled at the dashboard

The first wave went roughly like this. Bolt on sensors. Stand up a web portal. Set some thresholds. Within weeks the alarm count had climbed into the hundreds, and within a few months most crews had learned to tune it out, the way you stop hearing a car alarm on a busy street.

The sensors weren’t the problem. People simply couldn’t keep up. Even a modest fleet of connected machines typically throws off data far faster than any reliability engineer can read it. Visualized condition monitoring without an interpretation layer is, in practice, a very expensive screensaver.

Is that just anecdote? Not really. Deloitte’s 2025 Smart Manufacturing and Operations Survey of 600 US manufacturing executives found close to half of respondents (46%) already use industrial IoT. Only 29%, under a third, have AI or machine learning deployed at facility or network level. Collection raced ahead. Interpretation, for most, is still jogging behind.

A condition monitoring system needs an intelligence layer, not more screens

Here’s where we’d put the effort: above the sensors, not beside them. In the industrial IoT programs Saviant builds, Azure brings together three streams that usually live apart: live signals from sensors and control systems, maintenance records from the CMMS (computerized maintenance management system), and warranty and service data from the ERP. The result is one consistent profile per machine. Dull, until you’ve reconciled three spreadsheets mid-outage.

Models then learn from the failure history of that specific equipment family. Not generic degradation curves. Your bearings. Your duty cycles. Your customers’ oddly specific operating environments. What comes out is a short answer per machine: how worried to be, what’s likely causing it, and roughly how much remaining useful life (RUL, the estimated time before intervention is needed) it has left.

The systems that improve reliability with condition-based insights seem to share one habit. They answer “what should we fix this week?” rather than “what happened last night?”

Building the software itself? See our guide to custom machine condition monitoring software on Azure.

Insight only counts when it lands as scheduled work

A prediction parked in a portal changes nothing on the shop floor. It’s just a more confident way of being ignored. Value tends to show up only when a flag becomes a job, so once a model marks a machine as at risk, a workflow layer can:

• Check the ERP for parts before anyone picks up a wrench.
• Slot the repair into a planned stop.
• Open the job in the CMMS or field service tool, assign a technician who’s actually qualified for it, and chase it if nobody picks it up.

Dashboard-era vs decision-era condition monitoring

For equipment manufacturers, the installed base is the real prize

Plant operators watch their own machines. OEMs sit in an odder, more interesting seat. Every unit shipped into a customer’s plant is, potentially, a data source, and that quietly rewrites the economics. Warranty claims usually drop when failures get caught early. Service contracts can be priced on uptime instead of parts. Field engineers turn up with the right part the first time.

The awkward bit? Most of the installed base predates connectivity. So a condition monitoring retrofit typically comes before any AI: clamp-on vibration sensors, an edge gateway, and cellular or plant-network backhaul. Unglamorous work. It’s also, from what we’ve seen, where connected product programs quietly succeed or fail.

Machine condition monitoring cost per asset hinges on five factors

Vendors have long pitched the machine condition monitoring market around hardware, and the condition monitoring equipment market still dominates most sales conversations. We’d argue that’s the wrong place to look. The sensor rarely decides payback; integration and model tuning usually do. Five factors tend to shape the per-asset number:

See it in practice in our condition monitoring data engineering case study.

A proof point from the field

Start with one asset class and one number

Scan most condition monitoring news and you’ll find launches of new sensors and slicker dashboards. But the manufacturers we see pulling ahead are doing something far less photogenic: wiring predictions into work orders, one asset family at a time. The path usually runs in three phases.

1. Data audit. List the signals you already capture, test which are clean enough to model, and rank machines by what a breakdown would cost.
2. Proof of value. Pick one asset family. Lock the baseline before anything else: downtime hours, mean time to repair, warranty cost per unit. Skip this and ROI becomes a story rather than a number. Then show live predictions on real equipment.
3. Scale. Extend coverage. Retrofit the installed base.

Author's Bio

Prateek Khare
Practice Manager - Instrument and equipment manufacturing | Saviant

Prateek specializes in building product and tech roadmaps using tech levers like IIoT, ML/AI, data analytics, and GenAI solutions on Cloud. He leads multiple industrial clients at Saviant and contributes to customer growth & success.

Author's Bio

Praful Dandgawal
Program Manager | Saviant

A marketer by heart, Praful always thrive to drive business growth and build a brand with storytelling. He comes with an extensive B2B technology marketing background.