High
Failures arrive without enough warning
Impact severity
Initializing Systems...
Predictive Maintenance
Prevent costly downtime before failures occur
LeanQubit helps maintenance and operations teams move beyond calendar-based maintenance and late-stage firefighting. By combining connected asset signals, machine context, and industrial AI, the solution identifies degrading conditions early enough for teams to plan controlled intervention instead of reacting after failure.
24/7Asset Monitoring
1Decision Loop
14dLead Time
MAINTIQ MONITORCondition → Action
LIVE
Asset Health
CNC spindle 03
Normal
92%
Conveyor motor A2
Bearing wear
68%
Hydraulic press 01
Critical seal
35%
Prediction
Lead time
14d
Priority
High
Actions
Trigger alert
Schedule window
Vibration · temperature · current · runtime context
Maintenance alerts tied to plant conditions
Supports planning, spares, and intervention windows
Built on connected IIoT and operational data
Client Problems
Why industrial teams stay trapped in reactive maintenance.
This page should reflect the realities of industrial automation teams, not generic AI messaging.
High
Preventive schedules waste money
Impact severity
High
Asset data is not operationally usable
Impact severity
High
Spare parts and labor are always late
Impact severity
How LeanQubit Solves It
An IIoT and AI maintenance loop built for industrial operations.
The solution is structured around practical maintenance decisions, not abstract prediction scores.
Solution 01 of 04
Capture condition signals continuously
LeanQubit integrates vibration, temperature, current, runtime, and machine-state data from relevant assets and process equipment.
Proven Outcome
Condition monitoring becomes live and usable instead of fragmented or occasional.
Reference Workflow
How predictive maintenance fits into the industrial stack.
This is the system view buyers need to understand the solution end to end.
LeanQubit Stack
The modules that make predictive maintenance operational.
This solution depends on both IIoT and execution context, not just one AI model.
Module 01
IIoT Integration
Brings live machine and sensor data from the plant floor into the predictive maintenance workflow.
Part of LeanQubit Platform
Module 02
FactoLake
Stores asset history, event streams, and contextual plant data required for condition analysis.
Part of LeanQubit Platform
Module 03
FactoIQ
Hosts anomaly detection, maintenance intelligence, and guided intervention logic.
Part of LeanQubit Platform
Module 04
FactoMES
Connects maintenance action back into execution, downtime, and work order workflows when needed.
Part of LeanQubit Platform
Buyer Value
What maintenance and operations teams actually gain.
This is the business layer of the solution page.
Value for Maintenance Manager
"Earlier warning, better prioritization, and more controlled intervention planning on critical assets."Talk to us
Key metrics
- OEE upliftavg across plants0%
- MTTR reductionfaster fault resolution0%
- Shift visibilityreal-time coverage0%
Implementation Model
How to deploy predictive maintenance without over-scoping the program.
The rollout should feel manageable on mobile and desktop alike: select assets, validate signals, prove value, then scale.
Start your journey Typical deployment: 6–8 weeks
FAQ
Questions industrial buyers ask before committing.
Showing 3 of 3 questions
Next Step
Build a maintenance program that acts before failure, not after it.
Book a machine-data assessment to identify which assets should be monitored first, what signals matter, and how LeanQubit can turn those signals into operational maintenance action.