Data Management & Analytics Services

Aspentech MTell is a predictive maintenance software solution that enables industrial companies to improve the reliability and availability of their critical assets. By using advanced analytics and machine learning algorithms, MTell can detect anomalies and predict equipment failures before they occur.

 

One of the key benefits of MTell is that it can be applied to a wide range of industrial assets, including pumps, compressors, turbines, and more. This flexibility makes it a valuable tool for industries such as oil and gas, chemical manufacturing, power generation, and others where equipment reliability is critical to operations.

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MTell works by collecting data from various sources, such as sensors, historians, and other data systems, and then applying machine learning algorithms to identify patterns and anomalies in the data. The software can then provide alerts and notifications to maintenance teams, enabling them to take proactive action to prevent equipment failures.

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Some common use cases for MTell include:

• Early detection of equipment failures before they occur

• Reduced maintenance costs through predictive maintenance scheduling

• Increased asset reliability and availability, leading to improved operational efficiency

• Improved safety through early detection of potential equipment issues

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Our team can help you with your initial MTell Deployment, troubleshoot an existing implementation, or help you solution and time-box your overall roadmap. Our consulting services normally include the following activities and deliverables: 

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Key Activities: 

1. Project Management: This involves managing the implementation process, ensuring that the project is delivered within budget, scope and timelines

2. Use Case Discovery & Portfolio management: Identifying key use cases for the implementation and managing them within a portfolio of use cases

3. System Analysis: A thorough analysis of the current system and the requirements of the business to identify use cases and data sources

4. Data Collection: Data collection and integration from various sources including historians, SCADA systems, and IoT devices

5. Model Development: Development of models using machine learning algorithms, which can identify patterns and predict failures in equipment and processes

6. Model Integration: Integration of the developed models into the MTell software platform for continuous learning

7. Testing: Rigorous testing to ensure that the models are working as intended, and identify any gaps or areas of improvement

8. Deployment: Successful deployment of the models into the operational environment with appropriate monitoring mechanisms in place

9. Model Scale-out: Identifying like-type equipment in other plant areas or facilities and extending the MTell models to cover these equipment / assets.

10. User Training: Training for users on the use of the MTell platform, including model configuration and interpretation of results

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Key Deliverables:

1. Cost Model: Providing a cost model that outlines the total cost of the MTell implementation, including hardware, software, personnel, and training costs.

2. Detailed system analysis report, including the identification of use cases and data sources.

3. Solution Architecture: Providing a solution architecture that outlines the overall structure and design of the MTell implementation.

4. Model development and integration report detailing the creation of models, model performance metrics, and continuous learning mechanisms.

5. Deployment report, including the details of the deployment process and final model configuration.

6. User training materials, including documentation and videos.

7. Ongoing maintenance and support services to ensure the models continue to deliver results as intended.

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Overall, successful Aspen MTell implementation can bring significant benefits to industrial operations, including increased equipment reliability, reduced maintenance costs, and improved process efficiency. Our team can help you get there.