Only two inventions in the past 40 years have been true breakthroughs that increased net product output and efficiency – in the 70’s the DCS increased the competency of everybody who stood up to the “operating panel” and then dynamic optimization from DMC of Houston (now AspenTech) because in real-time it drove manufacturing processes to optimal performance. Industrialized machine learning is the 3rd such breakthrough…and could have an even bigger impact.
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Optional Users

  • Maintenance Manager

  • Operations Manager

Operator

  • Alerts on process excursions, equipment faults

  • Highlights issues on heat maps

  • One place to see all asset information

  • Familiar way to enter and track maintenance work orders

  • Viewable on mobile devices

Maintenance Planner

  • Conditional alerts on equipment

  • Usage based maintenance planning

  • Automatic corrective work orders

Reliability
Engineer

  • Conditional alerts on equipment

  • Usage based maintenance planning

  • Automatic corrective work orders

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Optional Users

  • Maintenance Manager
  • Operations Manager
  • Analyst

Operator

  • Less reliance on rounds monitoring

  • Trust in early degradation warnings, allows changes in operations

  • Fewer breakdowns process interruptions

  • Increased production

  • Root cause – indicates equipment problems caused by process deviations and allows corrective action before damage

Maintenance Planner

  • Accurate time-to-failure allows precision in planning service/repair

  • Allows decisions to schedule early/later depending on operations/planning, needs plus parts, tools, & staff availability

Reliability
Engineer

  • Far less time and effort to set up more reliable, earlier detection

  • Improves his impact on profitability, safety, and risk to company

  • Mtell provides him with better mechanisms to detect & correct

  • Much easier startup/implementation work than current maintenance methodologies

  • Self-learning and adapting means no intense rules-based programming

  • Less care and feeding of maintenance strategies

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Optional Users

  • Managers
  • Executives

Data
Scientist

  • Full access to large data sets from multiple locations

  • Analysis in 3rd-party tools

  • Reports in 3rd-party reporting tools

Process
Engineer

  • Full real-time access to large data sets from multiple locations

  • Source for rolled up data streams from disparate processes potentially from many sites

  • Compare performance of equipment at multiple locations Reports in 3rd-party reporting tools

Business
Analyst

  • Source for slice-and-dice compare and contrast across time periods

  • Source for reporting, and analysis across multiple sites/products

  • Performance studies and reports in 3rd-party reporting tools

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Optional Users

  • Business Analyst
  • Data Scientist
  • Process Engineer
  • Maint. Service Provider
  • Insurance Analyst

Corp. Equip.
Specialist

  • Review performance of similar machines across sites

  • Understand how process affects reliability

  • Decision-making on run-to-failure rather than maintain

Remote Reliabilty
Engineer

  • Early heads up on issues at multiple sites

  • Offers remote prescriptive maintenance to many sites

  • Trains using population-learning across equipment sets

  • Can transfer learning between equipment at different site

Equipment OEM

  • Can track actual equipment performance efficiently across multiple sites

  • Gets early heads up warnings and opportunity to intervene

  • Can feedback causation into product designs

  • Compare performance of equipment at multiple locations Reports in 3rd-party reporting tools

Mtell View

Mtell View is a flexible data visualization application bundled with all current Mtell product offerings. It is designed primarily for those “close to the process and equipment” (the operators, maintenance technicians, and reliability engineers) displaying content in browsers, tablets, and smartphones. It is contextually sensitive to their specific needs; quickly alerting issues, and guiding users rapidly and effectively to the prioritized, important information. The approach assures all pertinent information about the assets is in Mtell View without the user needing to leave to view a 3rd-party application. For example Mtell View makes available:

  • Trends of process sensor values over wide-ranging time periods
  • Process/equipment status and alert conditions
  • A neutral gateway to enter and track work orders, which does not need client tools or training on the particular attached Enterprise Asset Management (EAM) system
  • Current and upcoming maintenance work orders for individual assets/groups of assets
  • Prescriptive activities following degradation/failure alerts
  • Availability, effectiveness, and usage of live monitoring agents
  • Charts of maintenance effectiveness over time
 Feature Comparison —————home_basis_iconBasis
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Local/Remote
Designed for on site or datacenter installation.
Local Local  Remote  Remote
Mtell View
Ubiquitous asset health visualization application for all Mtell products.
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Interoperation
Allows connections and interoperations with all contemporary operations and maintenance systems
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Monitor & Trigger
Process and machine conditions to execute rules to trigger action such as automatic work orders
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Heads Up Warnings
Accurate predictions of degradation and failure
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Prescriptive Maintenance
Recognizes WHEN and WHY equipment fails, orders specific maintenance
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Root Cause
Detects underlying subcomponents contributing to failure.
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Enterprise Class
Scales I/O and processing speed to allow big data and many users.
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