The system is driven by digital twin technology, integrating intelligent sensing devices to perform online condition monitoring of industrial equipment 24/7, providing users with services such as intelligent fault alarm, intelligent diagnosis and analysis of faults, health assessment, fault prediction analysis, and hidden danger alarm to ensure reliable and stable operation of equipment, achieve enterprise safe production, improve production efficiency, and reduce maintenance costs.
With cutting-edge technologies, it realizes intelligent operation and maintenance, improves safety performance, and demonstrates the foresight as an industry leader.
Equipment Records
The equipment archives are incomplete, with insufficient maintenance records, history of replaced parts, performance parameters and other information, resulting in an inability to accurately assess the current state of the equipment.
Operating Status
Lack of tracking and professional guidance and analysis during the operation process of equipment, low application degree of digital twin technology, most equipment faults are discovered by people, unable to timely discover hidden dangers in equipment.
Experience accumulation
The best practices for equipment operation and maintenance are not systematically recorded, resulting in increased training costs for new employees and extended downtime due to the lack of accumulated historical fault data and solutions.
Data Mining
Lack of basic data analysis on equipment operation, unable to guide precise equipment maintenance and care, resulting in excessive maintenance and additional cost increase.
Value Empowerment
Realize intelligent operation and maintenance of mining equipment, empowering enterprises for efficient and safe production
Unplanned downtime reduces20%~40%
Online monitoring and fault diagnosis can detect abnormal conditions of equipment in real time, provide early warnings, help reduce equipment failure downtime, and improve production efficiency.
Maintenance Cost reduces20%~30%
By monitoring equipment in real-time, predictive maintenance can be performed to avoid repair costs due to equipment failure, thereby reducing maintenance costs.
By analyzing equipment operation data, accurate information on the status of equipment operations is provided to decision-makers, which helps improve decision-making efficiency and reduce decision-making risks.
Core Features
Comprehensively empower enterprises, improve operation and maintenance efficiency, ensure production safety, and achieve a new leap in smart mining.
Intelligent Sensing Devices
Utilizing advanced sensing technology, these devices monitor equipment status parameters in real-time, such as vibration, temperature, speed, etc., to provide accurate data support for production sites, facilitating intelligent monitoring and predictive maintenance of equipment.
Mining Equipment Online Monitoring
Driven by digital twin technology, establish a one-map management for equipment, and realize online monitoring of industrial equipment 7x24 hours a day.
Mining Equipment Inspection Management
The intelligent point inspection system for equipment is based on the "five-fixed principles" of fixed points, methods, standards, periods, and personnel for equipment point inspection work. It intelligently manages personnel, machines, and point inspection operations to ensure long-term safe and stable operation of equipment; avoid insufficient maintenance or excessive maintenance, reduce repair costs; and extend the service life of corporate equipment assets.
Mining Equipment Failure Diagnosis
Based on the mechanism model, fault prediction is carried out through the parameter information of each component, triggering a massive diagnostic model library. When equipment alarms occur, professional intelligent diagnosis reports are issued, providing suggestions for equipment performance optimization to avoid expensive emergency repair costs and unplanned downtime losses.
Mining Equipment Intelligent Alerting
A massive number of alert models are embedded in the equipment model, combining AI algorithms and mechanism models to achieve equipment fault prediction, early exposure of faults, and prolongation of equipment life.
Mining Enterprise Fault Case Library
Build a corporate equipment fault library, including but not limited to documents, graphics, videos, three-dimensional animations and other formats. It supports structured storage by product, model, fault category and other dimensions, and can be searched according to keywords to realize online management of experience knowledge, thereby lowering the technical threshold.
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