Intelligent Operation and Maintenance Solution

· Process and analyze large amounts of historical and real-time data to identify potential failure modes and trends for early warning and preventive maintenance.

business pain points
· Complex system architecture:
Banking systems are usually composed of multiple types of servers, each of which has its specific failure mode and performance indicators, and the complexity of the system increases the difficulty of failure prediction.
• Diversity of failure modes:
Server failures can be caused by hardware failures, software defects, network problems, power problems, and many other factors. It is difficult to establish a comprehensive fault prediction model.
• Data collection and analysis:
It is a challenge to collect enough fault precursory data and analyze it effectively. Insufficient or poor quality data can affect the accuracy of the predictive model.

Business Objectives
Introduce advanced predictive analytics tools and techniques
Establish a comprehensive data collection and monitoring system
No need to understand the characteristics of the equipment and the principle of the equipment, only from the perspective of historical data for fault warning
From fault repair to planned repair, to provide more efficient and intelligent fault warning solutions

Core Functions
• Access to current monitoring data: for analysis
• Access to historical monitoring data: for learning
• Analysis to produce predicted results: for decision making
Select a response: for maintenance