Failure tracking
Every fault and alarm is logged with timestamp, equipment, and process step. Track MTBF, recurrence rates, and failure patterns.
Predictive Maintenance Software for Industrial Equipment
IoTAutoGen solution
Early warnings that come with the context to act on them.
Predictive maintenance only works when maintenance history, alarms, and sensor data are connected. TCAIGV links them to each asset, so a warning arrives with the history, manuals, and SOPs a technician needs.
Maintenance intelligenceMonitor. Diagnose. Predict.Each step builds on connected equipment and maintenance data.
The Challenge
Prediction starts with connected data
Maintenance records sit in logbooks, tickets, and spreadsheets. Alarms live in equipment systems. Manuals and SOPs live somewhere else. When something goes wrong, engineers piece the story together by hand.
Models built on incomplete or inconsistent data give weak warnings. Before prediction helps, the equipment, alarm, and maintenance data need to be connected and trustworthy.
How It Works
From connected data to maintenance intelligence
IoTAutoGen collects faults, alarms, sensor events, and maintenance actions from connected equipment, builds a baseline for each asset from its own operating history, and flags risks with recommended actions. People review recommendations and confirm actions on equipment.
Faults and alarms logged with time, equipment, and process step
A baseline for each asset built from its own history
Anomaly detection on signals such as vibration, temperature, and pressure
Alerts routed to the right technician with the related manual and SOP
Step by Step
Monitoring, diagnostics, and prediction are different things
We keep them separate so you know what each one gives you.
- 01Monitoring
See live equipment state, KPIs, alarms, and current runs, so you know what is happening now.
- 02Diagnostics
Investigate failures with fault logs, MTBF and recurrence rates, linked SOPs, similar issues, and RCA records.
- 03Prediction
Detect anomalies against each asset's own baseline and raise early warnings with a recommended action.
Capabilities
What is included
Maintenance logbook
A digital logbook per equipment with work orders, technician notes, parts replaced, and sign-off.
AI maintenance insights
Patterns across maintenance history, and a suggested maintenance window that fits the production schedule.
Predictive alerts
Anomaly detection with early warning thresholds from each asset's own baseline, routed to the right technician.
Document library
OEM manuals, SOPs, and wiring diagrams attached to each equipment profile and shown during a maintenance event.
History and reporting
A timeline per asset from installation, audit-ready reports, and cross-site maintenance analytics.
Deployment
Integration and deployment considerations
Equipment data. Comes through the IoTAutoGen gateway, or from your existing MES, SCADA, or historian.
Maintenance systems. Use the built-in CMMS module, or connect the CMMS you already run.
Where models run. Anomaly detection and predictive models can run on the edge device, so real-time inference does not need data to leave the shop floor.
Human approval. Operators and technicians confirm every action on equipment. AI surfaces data and recommendations.
Data readiness. Each baseline is built from the asset's own operating history, so results depend on the amount and quality of the data available.
What Teams Get
Practical outcomes
One record per asset. History, alarms, manuals, and SOPs sit together instead of in separate tools.
Earlier visibility. Unusual behavior is flagged with context, so technicians do not start from a blank page.
Better timing. Maintenance windows can take the production schedule into account.
A traceable path. Follow an issue from alarm to action to closure.
Use Cases
Where it fits
Repeated failures on the same equipmentPlanning maintenance windows around productionGiving technicians the manual, SOP, and history at the moment of an alarmComparing equipment performance across plantsSemiconductor and solar manufacturing equipment
Questions
Frequently asked questions
What is the difference between monitoring, diagnostics, and prediction?
Monitoring shows what is happening now. Diagnostics helps you investigate why a failure happened. Prediction flags unusual behavior early against each asset's own baseline and suggests an action.
Does predictive maintenance need a lot of history?
Each baseline comes from the asset's own operating history, so more consistent data generally gives better early warnings. We assess your data during setup and do not promise a set accuracy.
Can I use my existing CMMS?
Yes. IoTAutoGen includes a CMMS module, and it can also connect to the CMMS you already run.
Does the AI act on equipment by itself?
No. People review recommendations and confirm every action on equipment.
Where do the models run?
Anomaly detection and predictive models can run on the edge device, close to the equipment, so real-time inference does not need data to leave the site.
Related
Keep exploring
Industrial IoT Gateway
Connect mixed equipment once, and use the data everywhere.
View solutionSECS/GEM Integration
One host layer for fab tools from many makers.
View solutionIoTAutoGen
The product behind this solution.
Explore IoTAutoGenSee the AI Maintenance Command Center
For evidence-backed RCA, recommendations, and DSR support.
Learn moreRead: From SCADA to LLMs, practical AI in operations
From the TCAIGV blog.
Read the articleTalk to us about your setup.
Tell us what you run today. We will show how this fits and what it would take.