Before01
Company overview
- Failure halts the line
- No room for a stoppage
- Need longer asset life
AX Solution
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
SYSTEM ARCHITECTURE
How it is built
Equipment data collected and learned, then returned as the moment to service.
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
Manufacturing AI
- Anomaly detection
- Remaining life prediction
- Maintenance timing recommendation
- Unplanned downtime
- Maintenance cost
- Spare part stock
- Equipment utilisation
EFFECT
Business impact
What it leaves behind — in unplanned stops, maintenance cost, spare stock and uptime.
BUSINESS OUTCOME
Equipment condition,turned into measurable results
- STOP
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 stops · MTBF · service timing
- COST
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 cost · premiums · cascading failure
- PART
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 stock · rush orders · turnover
- RATE
Higher uptime
The cause is analysed the moment something is off, so repairs are shorter, and the equipment is watched continuously against its design performance.
Uptime · MTTR · design 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
Catch the machine before it stops, not after.
We look at your sensors and failure history together, and start where prediction already pays.
Talk to us