
Case Duration:
December 2019 - Present (4+ Years)
Client Background:
This enterprise has completed a million ton refining and one million ton ethylene project, which is an important hub for the introduction, storage, transportation, and processing of oil and gas in west China.
Client Requirements & Solutions
Customized services, multi-dimensional enhancement of comprehensive equipment management, and promotion of efficient management of all staff.
Specialized management of 'Three Highs' conditions in key pumps:
Ronds deploys an online monitoring system for key pumps across the client's entire plant, targeting the 'Three Highs——High Vibration, High Load, and High Hazardous Medium' conditions in these critical pumps for specialized management.
Integrate data, build intelligent equipment operation and maintenance platform:
Ronds assists the client in developing an intelligent equipment maintenance platform. This platform provides real-time monitoring of key equipment status, implements health grading and classification management for assets, and enables early detection and proactive intervention to maintain optimal equipment performance. Moreover, Ronds integrates data from existing systems, consolidating disparate datasets into a unified platform for centralized management.
Enhance metrics, elevate the comprehensive equipment management:
Ronds has enhanced the clients' overall equipment management by developing functional management modules tailored to their business needs and refining key performance indicators for the entire lifecycle management of pumps.
Application Value
Realize a comprehensive monitoring of critical pumps by building a platform:
Successfully established an intelligent equipment operation and maintenance platform that enables real-time monitoring of the health status of key pumps throughout the entire plant.
Empower experts, elevate management practices:
Ronds has trained nearly 10 VCAT III and 50 VCAT II diagnostic analysts for the client, assisting in designing diagnostic processes and creating a data-driven predictive maintenance management model involving comprehensive employee participation.

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