
Industrial Equipment Inspection Pain Points
Long training time for experienced employees
On-site equipment inspection relies on experienced employees to combine production processes and equipment historical status, and it is difficult for new employees to get started quickly.
The equipment is complex and large in number
Industrial sites usually have many types of equipment in large quantities. Manual inspections have a narrow coverage, low frequency, and cannot be carried out in a detailed manner.
Single threshold is prone to false positives
Relying on a single threshold alarm is prone to false alarms, and when production specifications are updated, the status of the equipment often changes nonlinearly.
Fault types vary
Equipment failures are changeable, and the same failure can show different data patterns. It is impossible to monitor new failures based on historical failures.
Machine Woodpecker
Intelligent Snapshot Inspection
Through advanced genetic programming and deep learning technology, the twin saves the status of the equipment during normal working time. The twin status is then used to regularly compare the real-time working status of the equipment, promptly discover minor degradation trends, and assist maintenance personnel in carrying out preventive maintenance.

Advantages of Machine Woodpecker Intelligent Snapshot Inspection
Quick Start Deployment
The machine woodpecker only needs to use the normal status data of the equipment to create the virtualized normal baseline state of the equipment, effectively shortening the initial data collection time and avoiding the collection of small sample fault data.
Full coverage of equipment
By comparing the virtualized normal device status with the real-time device status, all inspection devices can be covered by only the computing server.
7/24 hour inspection
Virtualized status comparison can be run 24/7, regardless of the number of inspectors or staff capacity.
Avoid underreporting
Based on the normal baseline state of virtualization, snapshot inspections can capture various types of faults in a timely manner as long as they deviate from the normal state, avoiding reliance on specific fault data patterns.
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