Before01
Company overview
- Failure halts the line
- No room for a stoppage
- Need longer asset life
AI
Predictive maintenance
A factory that never stops — predicting the future of your equipment through data
OVERVIEW
Not after the failure —maintenance before it happens
Reactive and preventive maintenance run on a clock; predictive maintenance runs on the condition of the machine.
Reactive maintenance
- Core concept
- Repair after failure
- Downtime timing
- Right after a failure occurs
- Cost efficiency
- Low
- Data usage
- None
Preventive maintenance
- Core concept
- Periodic inspection/replacement
- Downtime timing
- Scheduled regular inspection day
- Cost efficiency
- Medium
- Data usage
- Experience/manuals (fixed cycle)
Predictive maintenance
- Core concept
- Condition-based real-time prediction
- Downtime timing
- When failure signs are caught
- Cost efficiency
- High
- Data usage
- Sensors, AI models, historical data
KEY FEATURES
Four capabilities that read equipment condition
From real-time collection through root-cause diagnosis and anomaly detection to remaining-life prediction.
Capability Index04
Maintenance Capability
01 / 04
01
Real-time data collection / monitoring
Failure-cause diagnosis / failure-type classification
Anomaly detection / real-time alerts
Remaining useful life prediction
High-sensitivity sensors attached to equipment precisely collect key indicators such as vibration, noise and current around the clock. The large-scale time-series data collected is transmitted immediately to a cloud-based platform, building an integrated monitoring environment.
Management Scope
- Multi-channel sensor integrated measurement
- Real-time monitoring
- Fine-signal capture
- Data preprocessing
- Data-loss prevention
- Standard communication protocol support
Real-time data collection
IoT sensors collect equipment condition data such as vibration, temperature and current in real time
WORKFLOW
The workflow behind each capability
For each capability, the order it runs in and the check it passes before moving on.
Equipment signals under varying loads, read in context
- 01
Sensor setup
vibration · current · temperature
- 02
Operation linking
speed · load · state
- 03
Signal cleansing
time · samples · quality
- 04
Baseline build-up
normal/abnormal operation history
Recorded alongside
01
Sensor/
02
Signal/
03
Sampling interval · gaps
Operating check
Is there a comparison baseline for each operating condition?
YES
Same condition → pattern analysis
NO
Condition differs → split baseline by state
An example workflow · the detailed links and criteria are set to suit your floor.
SYSTEM ARCHITECTURE
What it connects,and the work it leads to
From the data that goes in to the results on the floor.
01 / INPUT
01Condition signalsVibration · current · temperature
02Operating contextSpeed · load · run state
03Maintenance historyFailures · inspections · replacement · recovery
CONNECTED OPERATIONS
AI Maintenance
Normal pattern · deviation
Failure type · trend judgment
Inspection and repair priority
03 / OUTPUT
✓Equipment conditionAbnormal intervals · trends
✓Maintenance basisRisk type · related signals
✓Maintenance executionWork orders · result history
01 / INPUT
01Condition signalsVibration · current · temperature
02Operating contextSpeed · load · run state
03Maintenance historyFailures · inspections · replacement · recovery
CONNECTED OPERATIONS
AI Maintenance
Normal pattern · deviation
Failure type · trend judgment
Inspection and repair priority
CONNECTED OPERATIONS
AI Maintenance
Normal pattern · deviation
Failure type · trend judgment
Inspection and repair priority
03 / OUTPUT
✓Equipment conditionAbnormal intervals · trends
✓Maintenance basisRisk type · related signals
✓Maintenance executionWork orders · result history
Linked by common identifiers, master data and historyResults checked → fed into the next run
Who uses itMaintenance teamProduction managersEquipment engineers
METHOD
How it is built
Equipment data collected and learned, then returned as the moment to service.
Collection01 →
Data Ingestion
PLC / CNC
Modbus / OPC-UA
IoT SENSOR
Edge device link
SCADA link
Live process state
Vibration / noiseTemperature / pressureCurrent / powerParticle contamination
Signal processingSTFT / FFT
Frequency analysis · denoising · normalization
Time syncNTP/PTP
Maps timestamps to equipment history
Target System
Time-series DBTimescale
Data lakeRaw waveform
MetadataMaintenance history
Modeling & analysis02 →
AI/ML Modeling
Predictive maintenance engine
Anomaly detection
XGBoost · Isolation Forest
Failure classification
CNN, YOLO
RUL prediction
LSTM AutoEncoder
Compound cause analysis
Multimodal
Feedback & refinement
Automatic prioritization
Digital twin
MLOps retraining
Service03
Operational visibility
Dashboard
KPI report
Continuous learning
Reinforcement learning (RLHF)
Intelligent maintenance
Failure prediction
Instant alerts past thresholds / automatic equipment stop
Detect equipment warning signs in advance and guarantee operational continuity.
Manufacturing and machine tools
Rotating equipment
Energy/utilities
Robotics and automation lines
Transportation and mobility
Buildings and infrastructure facilities
Manufacturing and machine tools
Rotating equipment
Energy/utilities
Robotics and automation lines
Transportation and mobility
Buildings and infrastructure facilities
Equipment is caught before it stops, not repaired after.
- Vibration
- Temperature and current
- Operating history
- Maintenance history
- Part life
AI Maintenance
- Anomaly detection
- Remaining life prediction
- Maintenance timing recommendation
- Unplanned downtime
- Maintenance cost
- Spare part stock
- Equipment utilization
EFFECT
Business impact
What it leaves behind — in unplanned stops, maintenance cost, spare stock and uptime.
- 01
Fewer unplanned stops
Anomalies are caught before they stop the line, and the best moment to service is known, so equipment only comes down when it was meant to.
Unplanned stopsMTBFservice timing - 02
Lower maintenance cost
Time-based maintenance gives way to condition-based, the premium that follows a breakdown is avoided, and cascading failures are headed off.
Maintenance costpremiumscascading failure - 03
Smaller spare stock
Knowing when a part will be replaced removes the rush order and the safety stock held against it — only what is needed arrives, and on time.
Safety stockrush ordersturnover - 04
Higher uptime
The cause is analyzed the moment something is off, so repairs are shorter, and the equipment is watched continuously against its design performance.
UptimeMTTRdesign performance
CASE STUDY
Case study
Reinforcement learning found the optimal operating policy, and abnormal vibration or noise was caught the moment it appeared.
Customer industry
High-layer-count PCB (MLB) manufacturing
After02
Implementation
- Machine sensor data
- Policy learned by RL
- Live anomaly checks
Business impact
Predictive maintenance where the equipment diagnoses itself