OnePact Predict

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Onepact Predict

Minimizes unplanned downtime by using advanced predictive analytics to assess and optimize production assets. Proactively identifies potential failures, allowing for timely maintenance and improved operational performance.​
This approach optimizes resource allocation, reduces maintenance costs, and ensures consistent reliability across multiple assets.​

Maximize efficiency by minimizing surprises with proactive insights.​

OUR APPROACH TO KEY CHALLENGES

Operational Challenges: Resolved

CHALLENGE
SOLUTION
Unplanned Operational Failures​
Digital Twin Technology​

Creates digital twins to anticipate and prevent operational issues.​

Data Integration Difficulties​
AI-Based Data Integration​

Integrates and analyzes data using AI for accurate predictions.​

Delayed Anomaly Detection​
Advanced Real-time Monitoring​

Enables real-time monitoring and early detection of anomalies.​

Inefficient Resource Allocation​
Optimized Resource Allocation​

Uses AI to optimize maintenance schedules and reduce downtime.​

Scalability Challenges​
Scalable Predictive Models​

Ensures scalable models for efficient application across multiple assets.​

Features

Predictive technology to prevent issues and enhance operational reliability​

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Real-Time Equipment Condition Monitoring​

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Advanced Pattern Recognition​

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Centralized Asset Health Monitoring​

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Data Driven Models

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Early Warning System​

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Advanced Analysis and Diagnostics​

Real-Time Equipment Condition Monitoring​

Real-Time Equipment Condition Monitoring​

Continuously monitors the condition of your equipment in real time, ensuring optimal performance and operational efficiency.

Advanced Pattern Recognition​

Advanced Pattern Recognition​

Utilizes advanced pattern recognition software to detect anomalies and trends that may indicate potential issues, allowing for proactive maintenance.

Centralized Asset Health Monitoring

Centralized Asset Health Monitoring​

Offers a centralized platform to manage and monitor the health of all assets, streamlining operations and improving decision-making.

Data Driven Models

Data Driven Models

Leverages data-driven models that use actual operating conditions to provide accurate insights into equipment performance and maintenance needs.

Early Warning System​

Early Warning System​

Provides early warnings of impending equipment problems or failures, enabling timely interventions and minimizing downtime.

Advanced Analysis and Diagnostics​

Advanced Analysis and Diagnostics​

Offers advanced analysis and diagnostic capabilities for precise problem identification, ensuring issues are resolved quickly and efficiently.

Case Study

All, before the damage was done.

case-study
Predıctıve Maıntenance

Generator Lower Guide Bearing Temperature

Type: Hydorlectric Power Plant
Power Plant: Hacınınoğlu HES
Installed Capacity: 200MW
Generator Count: 64
Owned By: Enerjisa Üretim A.Ş

Case:

While there are still 6 months until the scheduled maintenance period, OnePact Predict detected abnormal conditions in one of the generators.

Following an investigation into bearing parameters, temperature deviation was observed. The hydro maintenance team replaced the generator guide bearing heat exchanger.

All before the real damage was done.

SUMMARY:

Prevented Fault

2 Weeks of Outage Major

Approximate full generator bearing replacement time.

Prevented Loss

280 MWh of Production

Approximate equivalent of loss is: $20,000.

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