Everyone diagnoses the asset.
We diagnose the plant.
CMMS, vibration routes, predictive maintenance AI, or digital twin: each solves one piece of the problem, asset by asset. Ln2 is the only one that treats the whole plant as what it is — a system of systems — without losing traceability down to the sensor.
A plant isn't one big machine. It's several machines talking to each other.
A mill, a distillation line, or a powertrain isn't a single block: it's several coupled subsystems — drivetrain, lubrication, machine body — that share heat, vibration, and load. Almost every reliability tool today installs on one of those subsystems, or on a single asset, and stops there. If the problem originates elsewhere in the plant and only shows up here, none of them sees it coming from where it actually comes from.
It's like putting a different doctor in every room of a hospital, with none of them talking to each other: each one diagnoses their own patient well, but if the problem in one room spreads to the next, nobody connects the dots.
Ln2 splits the plant into subsystems — like floors of that hospital — and calibrates each one separately. The boundary between them is the real blind spot: if it isn't instrumented, no method sees the contagion coming, and ours declares that instead of guessing. That's why the first deliverable isn't a diagnosis — it's the map: which boundary needs a sensor so that full-plant diagnosis becomes possible.
Six ways to look at an industrial asset. Only one looks at the whole plant.
We group the tools you already know by category, with one real example of each — based on their public documentation, not our opinion.
We don't compete with these tools. We do one thing none of them does, and we let them keep doing what they already do well. We're the reliability engineer's extension, not their replacement.
IBM Maximo
Manages work orders and keeps an asset history with a failure hierarchy that manually links each failure mode to a cause. It's a system of record: it doesn't calculate anything new from the sensor's live signal.
Diagnoses in real time from the sensor and delivers the root-cause data that today gets entered by hand. It feeds Maximo's own work order — it doesn't replace it.
SKF · vibration routes
Vibration, temperature, or current measured per component and compared against a threshold. It alerts once the variable has already crossed it — almost always on the part that suffered the most contagion, not the one that caused it.
The same sensors, but before the threshold is crossed: it separates who actually failed from who merely inherited their neighbor's wear.
Augury
Its public documentation describes asset-by-asset AI diagnosis, with certified analysts validating the result, and detection weeks ahead of a fixed threshold. It doesn't detail how it separates the real cause when several coupled components degrade at once, nor does it offer the auditable traceability that IEC 61508/62443 requires.
The same early anticipation, plus an invertible mathematical operation — not a trained network — that separates cause from contagion and can be audited step by step.
Aspen Mtell · AspenTech
Learns the vibration or process signature of each past failure and recognizes it per asset, with equipment-specific templates. Well documented in continuous processes; it doesn't describe causal attribution across coupled assets of different types.
The same anticipation horizon, derived from an equation with formal proof — not a learned pattern that needs retraining when the asset changes.
Senseye · Siemens
Offers a unified risk view across every asset in the plant — the jump from isolated asset to plant-wide view is already there. Its public documentation doesn't detail whether that view distinguishes cause from contagion between coupled assets, or whether each asset is evaluated separately and aggregated afterward.
The plant-wide view isn't an aggregation of isolated diagnoses: the coupling matrix connects the assets to each other, so each component's number already has what arrived from its neighbors discounted.
AVEVA · Siemens Xcelerator
Maximum fidelity: it replicates the asset's exact physics. The cost is months of modeling per asset, and that computational weight isn't viable to run in shadow across an entire plant with dozens of pieces of equipment.
It trades detail fidelity for diagnosing by instrumentation in days, not months — the same mathematics is calculated across every asset in the plant at once, without the computational cost of a full twin.
In-room facilitation, with a checklist
Rebuilds the cause tree by hand, or with a checklist, in a room, days or weeks after the failure — it depends on who's in the room and what they remember.
The same cause tree, calculated in seconds on live data, before the failure finishes happening. It prepares the case for the RCA committee to review — it doesn't replace them.
IBM Maximo, SKF, Augury, Aspen Mtell (AspenTech), Senseye (Siemens), AVEVA, and Siemens Xcelerator are registered trademarks of their respective owners. Ln2 has no commercial relationship with them. The manual/structured RCA row describes a category of practice widespread across industry, not a named product or methodology. The comparison is based on public documentation as of this page's date and describes differences in category and scope — not an independent benchmark or a precision measurement against these vendors.
An unplanned shutdown almost never stays contained to a single asset.
When the real cause lives in one subsystem and the visible failure shows up in another, diagnosing asset by asset doesn't just take longer: it can point to the wrong equipment across the whole line, not just one machine. Plant scale isn't an extra — it's the difference between stopping a component and stopping an entire line.
is the cost of every hour of unplanned downtime, depending on the asset type — and it multiplies when the shutdown drags down connected lines.
Do you have a plant with several coupled assets, not a single stand-alone machine?
Let's talk about diagnosing it as what it is: a complete system.
Let's talk