Clayton Christensen introduced his concept of disruptive innovation where simplicity, convenience, accessibility, and affordability overturn markets where complication and high cost are the status quo.

The World’s Most Advanced Condition Monitoring

The architecture comprises a technology stack of Microsoft Windows, SQL Server, Linux, IIS, with web services into browsers, tablets, and smartphones. This stack allows many Agents to work on an asset, where each tracks a single previously recorded exact pattern to give THE earliest warnings of WHEN degradation begins and exactly HOW equipment will fail. These Agents work relentlessly monitoring 24/7 for exact degradation pattern matches and initiating prescriptive, corrective action.

Mtell Summit

Local to Remote

Mtell provides connectivity from local installations to BIG data, scalable datacenter platforms. Here, federated views of equipment at multiple locations permit subject matter experts to oversee equipment and push local learning from one machine to many others at diverse locations.

Even Bigger,

Higher-Level Learning

The datacenter stacks up for world-class performance with BIG data in an enterprise time-series repository tested to ingest 100 million points per second. This Industrial Internet of Things (IIoT) capable platform supports “deep learning” – THE dominant predictive analytics technology.

Here, “population-learning” across a “fleet” of equipment means the highest accuracy and transferable behaviors across multiple assets.

Mtell Reservoir

Industrial Internet of Things (IIoT)

The Internet of things (IoT) is the network of physical objects or “things” with sensors, embedded software and connectivity to create value using remote software and services applied across and between the disparate things. The (Industrial) IIoT works in the same way for industrial equipment. Articles suggest the IIoT will impact energy efficiency, performance, and process quality. Although, combining information from multiple diverse sensors allows insight, only advanced analytical techniques can extract significant value in the IIoT – usually in solving problems that were previously unsolvable. In this respect, Mtell is a cornerstone and a true pillar of the IIoT; with predictive and prescriptive analytics that extend the useful life of equipment and prevent breakdowns.

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Analytics, Data Science, and PPP

Often, the term analytics is used to mean the techniques used for understanding known data, whereas data science pertains to predictions of unknown data. Regardless, Mtell product offerings do both and deliver the 3 P’s of maintenance analytics:

  • Performance analytics provide insights into collections of data and is purely observational.

  • Predictive analytics aims to find correlations in known data to use for accurately identifying how events will occur in the future.

  • Prescriptive analytics endeavor to understand causation and offer advice that can change predicted events.

A hierarchical relationship binds the 3 P’s. Full proficiency in Performance Analytics is a prerequisite of Predictive Analytics, which in turn must be mastered thoroughly before advancing to Prescriptive Analytics. Mtell is the only company offering any meaningful Prescriptive Analytics solution in industrial environments.

Surprisingly, you do not need to be an expert in the 3 P’s, or analytics or the data science technologies to use Mtell products. The “heavy-lifting” is done inside the product and is presented in easy-to-use functions that concentrate on WHAT the user is trying to do with the equipment, and not HOW it must be done.

Prescriptive Maintenance

Prescriptive Maintenance is the process of determining what action to take based on predicted degradation and failure patterns, and scheduling corrective action without human involvement. Autonomous Agents, using machine learning, diagnose the early onset of a fault pattern, predict failure and inherent degradation with pinpoint accuracy, and Prescriptive Maintenance can automatically schedule inspection or corrective service with action to avoid or mitigate a failure. Work orders or inspection requests may be sent directly to the asset management system. Prescriptive Maintenance ensures service or repair occurs while service efforts are simple before damage causes extended shutdowns and complex repairs.

Machine Learning

Machine learning is a computer science subfield that has evolved from the study of pattern recognition using algorithms that learn and make predictions from data. It is now the dominant predictive analytics technology in all IT fields.

Mtell has added value by industrializing machine learning with nuance and inside knowledge of interpreting and massaging complex, problematic sensor data in order to manage the health of industrial equipment.

By wrapping machine learning in a product structure that automates the learning, analytics, predictions, and prescriptive action, the Mtell solution requires a minimum of human effort to recognize and solve complex equipment maintenance issues. Equipment issues are uncovered by correlating maintenance history with run-time operational data patterns to:

  • Distinguish normal and fault conditions

  • Precisely detect early recurring degradation symptoms

  • Accurately predict WHEN and WHY equipment will fail

  • Detect and classify new operating modes

  • Discover unknown failure patterns and convert them into more precisely and earlier detecting signatures

  • Self-learn and adapt over time

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Transfer Learning (M2M)

With Mtell, patterns of normal and atypical behavior are learned from the actual data coming from sensors on an around machines. Mtell applied years of effort in developing the intricate techniques for fitting patterns to multiple machines rather than a single machine. But, now the Mtell machine-to-machine (M2M) process easily transfers those learned behaviors to similar machines across both local and global networks. This way, pools or fleets of equipment learn and share all types of failures ensuring every machine has the same state-of-the-art safety and breakdown protection.

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