Motors, Pumps, And Compressors
Monitor changes in vibration, temperature, pressure, runtime, or power use, and find conditions that may need inspection before performance drops further.
Industrial Predictive Maintenance
Predictive Maintenance In Industry: Find Equipment Risks Earlier
Reduce unplanned downtime by finding signs of equipment trouble before a major failure. Mimasa AI connects machine readings, operating history, maintenance logs, and production data, then helps teams detect unusual conditions, assess risk, and start the right maintenance workflow.
- Earlier Risk Signals
- Planned Downtime
- Audit-Ready Actions
On-premise, private cloud, or cloud · Role-based access · Human approvals · Audit trails
- Use data from current machines and systems
- Keep maintenance teams in control
- Connect alerts with inspection and approval workflows
- Deploy on-premise, in a private cloud, or in the cloud
Move From Unexpected Failure To Planned Action
An unexpected machine failure can stop more than one asset. It can delay production, affect quality, increase overtime, and disrupt customer delivery.
Many plants still depend on two basic approaches: repair the machine after it fails, or service it on a fixed calendar. Both have limits.
Reactive maintenance acts too late. Fixed schedules may service healthy equipment while missing a problem that develops between planned checks. Predictive maintenance uses actual equipment and operating data, so maintenance teams can decide what may need attention and when.
Mimasa AI adds agentic workflow automation to that decision, moving from a risk signal to an alert, inspection task, approval, or planned service action.
What Is Predictive Maintenance In Industry?
Predictive maintenance is a data-based approach to equipment care. It looks for changes that may point to wear, failure, or poor performance. The system may use data such as:
- Temperature
- Vibration
- Pressure
- Power use
- Cycle time
- Runtime
- Error codes
- Breakdown history
- Inspection findings
- Maintenance records
- Production load
A predictive maintenance software system does not guarantee that every failure will be found. It gives maintenance teams earlier signals and better context for action.
Predictive Maintenance Software System
How Mimasa AI Supports Industrial Predictive Maintenance
Six connected steps take plant data from monitoring to an approved maintenance action, with the evidence and decision kept together for review.
- 01
Connect Equipment And Maintenance Data
Bring together machine logs, sensors, historians, MES, ERP, maintenance tools, and shift records. Mimasa AI works as an intelligence layer and does not require a full replacement of plant systems.
- 02
Build An Equipment-Health View
Combine current readings with past events, service history, production load, and machine context, so teams see behaviour over time instead of a single sensor value.
- 03
Detect Unusual Conditions
Use configured limits, business rules, statistical methods, or machine-learning models to find rising temperature, vibration outside range, longer cycle times, or repeat error codes.
- 04
Assess Risk And Impact
Weigh the signal with asset criticality, the current production plan, backup capacity, open orders, spare-part availability, and past failure patterns.
- 05
Recommend The Next Step
Suggest a defined action based on your rules and available evidence: inspect the machine, check a named component, increase monitoring, or plan work during the next stop.
- 06
Start The Approved Workflow
Send the recommendation to an authorised employee. After approval, the workflow can create a task, notify a team, or update a connected system, with every step recorded.
Data Sources We Can Connect
- Machine or PLC logs
- Sensors and edge devices
- SCADA or historian data
- MES and ERP records
- Computerized maintenance systems
- Inspection reports
- Spreadsheets and shift logs
- Manuals and service documents
Plant and enterprise sources are brought together through data extraction and connectivity and prepared with no-code data transformation, so equipment signals sit beside maintenance and production context.
Asset Coverage
Predictive Maintenance Use Cases In Manufacturing
Start with the assets where failure hurts most, then extend across the plant as the workflow proves itself.
CNC Machines And Machining Centres
Track cycle time, alarms, spindle behaviour, load, and maintenance history. Flag changes that may affect uptime or part quality.
Presses And Forming Equipment
Review pressure, cycle patterns, stoppages, and error events, then alert teams when behaviour moves outside the expected range.
Conveyors And Material Handling
Find repeat stoppages, motor strain, unusual speed, or rising energy use, and prioritise the sections that affect production flow.
Furnaces, Boilers, And Thermal Equipment
Monitor temperature, pressure, fuel use, alarms, and operating cycles to find changes that may affect reliability or process control.
Cooling And Utility Systems
Track pumps, chillers, and air systems. A shared utility failure can affect several production areas, so early warning has wider value.
Tool Wear And Process Drift
Use cycle data, quality results, and equipment conditions to find patterns linked to possible tool wear or process change.
Fleet And Mobile Equipment
Combine runtime, service history, error codes, and use patterns to plan inspections for forklifts, loaders, and other plant vehicles.
Business Value
Benefits Of Predictive Maintenance In Manufacturing
Reduce Avoidable Downtime
Find warning signs before an issue becomes a major stop, giving teams more time to inspect and plan.
Improve Maintenance Planning
Plan work around equipment condition, production needs, labour, and available shutdown windows.
Focus On Critical Assets
Rank issues by risk and business impact instead of treating every alert as equally urgent.
Use Spare Parts Effectively
Check part availability before planned work to reduce emergency purchases and unnecessary stock.
Protect Production Schedules
Link equipment risk with capacity and open orders, so planners see which issue may affect delivery.
Support Root-Cause Review
Bring machine readings, alerts, repairs, and production events into one timeline for investigation.
Reduce Manual Monitoring
Let the system watch defined signals and notify the right team, so staff focus on inspection and repair.
Create A Clear Maintenance Record
Save the signal, recommendation, approval, and completed action for later review and improvement.
Results depend on data quality, sensor coverage, equipment condition, operating variation, and the failure mode being monitored. Progress is easier to track when maintenance outcomes are reported through insights, analysis and reporting.
More Than A Machine Alert
Many monitoring tools stop after they show a warning. Mimasa AI connects that warning to the wider manufacturing process.
Equipment risk, production impact, and the approved action stay in one governed record, reviewed by people through data governance and collaboration.
- 1A machine shows an unusual vibration trend.
- 2The platform checks recent load and service history.
- 3The asset is marked as important to the current production plan.
- 4The system recommends an inspection during the next planned stop.
- 5Spare-part availability is checked.
- 6A maintenance lead reviews the evidence.
- 7An approved task is sent to the maintenance system.
- 8The result is added to the asset history.
Predictive, Preventive, And Reactive Maintenance
Most plants use a mix of all three approaches. The right choice depends on asset value, failure impact, data availability, and maintenance cost.
| Approach | When Work Begins | Main Strength | Main Limit |
|---|---|---|---|
| Reactive maintenance | After failure | Simple for low-value, non-critical assets | Can cause unplanned downtime and urgent repair costs |
| Preventive maintenance | On a fixed schedule | Easy to plan and standardise | May service healthy equipment or miss problems between checks |
| Predictive maintenance | When data shows rising risk | Supports earlier, condition-based action | Needs useful data, suitable models, and regular review |
A Practical Predictive Maintenance System
An effective system needs more than a model. It should connect four parts, brought into one governed workflow by AI agents and shared dashboards.
Equipment Data
Signals must be available, useful, and linked to the correct asset and time.
Operational Context
The same reading can mean different things under different loads, speeds, products, or conditions.
Maintenance Knowledge
Past failures, inspections, repairs, manuals, and engineer feedback help explain what a signal may mean.
Action Workflows
Alerts need an owner, priority, approval path, and next step, or teams only receive more notifications.
Built For Plant Teams
Built For Maintenance, Operations, And Plant Teams
Maintenance Managers
See asset risk, open alerts, planned work, and completed actions in one view.
Reliability Engineers
Review signal history, operating context, repeat failures, and maintenance outcomes.
Plant Managers
Understand which equipment risks may affect output, quality, cost, or delivery.
Production Planners
See how a possible machine issue could affect capacity and the production plan.
Maintenance Technicians
Receive focused tasks with the asset, issue, history, and supporting evidence attached.
Manufacturing IT And Data Teams
Connect plant and enterprise systems, and control data access, deployment, and integrations.
Shared equipment views and maintenance summaries can be published through visualisation and dashboards so every team works from the same record.
Choose The Right First Use Case
The best first use case is not always the most expensive machine. Look for an asset or failure mode with:
- A clear business impact
- Repeat failures or costly downtime
- Useful historical or sensor data
- A known inspection or maintenance response
- Enough events to test the approach
- Support from maintenance and operations teams
A Focused Pilot Answers Three Questions
- Can the available data show a useful warning?
- Can the team act early enough to create value?
- Can the workflow fit normal plant operations?