Energy and Power Industry

Energy and Power Industry

In the energy and power sector, existing equipment management and human error prevention measures are relatively well-developed, yet unplanned shutdowns still occur occasionally. AIMS's advanced industrial intelligent maintenance solution helps power generation companies achieve core functions such as online equipment monitoring, intelligent condition assessment, automatic fault diagnosis, and autonomous maintenance decision-making. This effectively enhances the operational safety, reliability, and economic efficiency of power enterprises, facilitating their digital transformation and intelligent upgrade.

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Pain Points of Business

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Inspection Challenges

Diverse equipment, scattered facilities, and harsh conditions like high temperatures and radiation make inspections difficult and labor-intensive.

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Data Silos Exist

Power plants have multiple information systems that don't communicate with each other, creating severe data silos and preventing the full realization of data value.

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High Inventory Pressure for Spare Parts

Regular maintenance and timely repair are required. The long procurement cycle for spare parts leads to significant inventory pressure.

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Substantial Economic Losses from Equipment Failures

High equipment value and maintenance costs lead to significant economic losses and social impacts when failures cause unplanned downtime.

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Equipment Failure Dagnosis Relies Heavily on Expert Experience

Knowledge of failure cases and diagnostic experience is not effectively accumulated or passed on.

Solution

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Upgrade Automated Data Collection

Deploy intelligent sensors using wired and wireless industrial networks to monitor equipment status online, in combination with existing system data, reducing the need for manual inspections.

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Interconnected Multi-Source Data

Leverage the unified data standards and multi-protocol connectivity of the intelligent equipment maintenance platform to integrate data from various systems. This enables correlation and in-depth analysis of multi-source data, addressing data silos between systems.

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Condition-Based Maintenance and Spare Parts Optimization

Transition from scheduled maintenance to condition-based maintenance by monitoring equipment status and predicting failures. This optimizes maintenance planning and spare parts management.

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Multi-Dimensional Parameter Estimation for Early Warning

Implement early anomaly detection and equipment degradation trend tracking through multi-dimensional parameter estimation models. Proactively prevent equipment failures and avoid unplanned downtime to ensure continuous production.

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Scalable Expert Diagnostic System

Develop a scalable expert diagnostic system to structure and digitize expert experience and case histories, facilitating effective knowledge accumulation and transfer.

Customer Value

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Reduced Manual Inspection Workload

Implement non-intrusive intelligent sensors for online equipment condition monitoring, reducing manual inspection workload by over 50%.

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Decreased Unplanned Downtime

Upgrade from traditional fixed-threshold monitoring to multi-dimensional dynamic threshold monitoring for early anomaly detection, reducing unplanned downtime probability by 30%.

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Shortened Fault Diagnosis Time

Establish an expert diagnostic system that automatically extracts abnormal symptoms and infers fault conclusions, cutting fault diagnosis time by 50%.

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Automated Status Reporting

Customize status report templates for regular or on-demand automatic generation, reducing engineers' report writing time by over 80%.

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Optimized Spare Parts Management

Implement condition-based maintenance to reduce spare parts inventory by 20%.

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Accumulation of Maintenance Knowledge

A flexible and scalable fault case and rule library effectively accumulates and transfers maintenance knowledge and experience, reducing reliance on individual expertise.