The Mtell Previse condition monitoring solution uses machine learning to prevent breakdowns, increase asset lifecycle, reduce maintenance costs,

and increase production output for any manufacturing process.

The truth is plain and simple: maintenance departments want to keep machines running and prevent breakdowns. Maintenance best practices have evolved over time to accommodate this, progressing from run-to-failure, to calendar/condition-based, to reliability- centered maintenance (RCM). But are these processes best-practices if improvements are hard won, take a lot of time, and require great expertise and expense? Industry leaders suggest something is not right:

85% of all equipment failures happen on a time-random

basis regardless of inspection and service. Boeing

Industries work hard to create machine reliability, but breakdowns continue to happen that are sometimes catastrophic. More often than not, the fault lies with the way we do maintenance and not with the machine. Mtell Previse brings a fresh approach to condition monitoring with evidence-based hard data that replaces customary policy and subjective opinions regard-ing maintenance best practices.

63% of all maintenance is unnecessary and causes

more problems than it fixes. Emerson

Contemporary condition monitoring applications use techniques to trap anomalies or changes in operational behavior of a machine that might indicate a problem. Such methods are complex, limited to certain equipment, prone to error, and ALWAYS require further expert investiga-tion and validation; producing high levels of false positives.

Mtell Previse uses machine learning agents to learn oper-ational behavioral patterns using actual data from sensors on and around a machine or manufacturing process. Mtell Previse recognizes diverse patterns in the sensor signals that indicate degradation, failure, and root cause.

Mtell Previse with Precise Pattern Recognition

Agents are created within minutes

Agents monitor for the earliest emergence of pre-recorded behavior patterns

Agents recognize minuscule multidimensional and temporal patterns across many sensor signals that humans and other technologies cannot see

Two types: Failure Agents and Anomaly Agents

- Anomaly Agents recognize patterns of normal op-erations and deviations that are anomalies

- Each machine (or process) might have 4-10 Agents watching and warning when degradation begins

Works 24/7 without breaks or interruptions; always ready to alert on degrading performance

Early warning with precise time-to-failure

Other Condition Monitoring with Imprecise Anomaly Detection

Prone to inaccuracies

Always require human validation

Only finds anomalies at the late stages of failure

Patterns are estimated (engineering models, calculations...)

Great skill and experience needed to build

Requires extensive maintenance - care and feeding

Mtell Machine Learning Agents

Self-Learning and Training When an anomaly is detected, Mtell Previse dispatches alert notifications and requests

for inspection. The results of inspection determine if the behavior is a failure signature or a new, normal operating state. The system trains itself to become smarter over time; learning and adapting to new asset operating conditions.

Predictive Scheduling Mtell Previse immediately sends work orders to the EAM system when Agents detect failure signatures. This provides time to plan and organize maintenance. Messages also contain the full scope of work including: cost, tools, labor, time, and safety concerns.

Transfer Learning Learned behaviors (normal, degradation and failure) captured on one machine are readily transferred to equipment of the same type with the same sensor configu-ration. After a very short retraining period, every machine shares the same safetyand breakdown protection.

Early Warning & Accurate Time-to-Failure Agents detect issues (the P in the P-F curve) far earlier than prevailing approaches. When a failure anomaly is detected, Mtell Previse searches further back into the sensor history to refine the signature, in order to improve

time-to-failure notifications. A 7-day Anomaly

alert can easily become a 30-day machine learning alert.

Root Cause Detecting symptoms is not the same as detecting the cause of degradation. Mtell Previse provides a sensor-ranking chart that shows which sensors contribute most to machine degradation.

Architecture IntegrationMtell Previse extracts sensor data from the installed plant historian, and the failure work order history from the existing enterprise asset management (EAM) system. Previse works with any equipment- connected sensors and requires only a few to activate machine learning.

Operations Systems

Asset Management Systems

Mtell Previse Features

*All trademarks are the property of their respective owners.

The Mtell Previse Difference A Customer ExperienceAt a major US oil and gas company, Mtell installed, configured,

and implemented Mtell Previse on five major rotating assets in

less than three days. Mtell Machine Learning Agents actively monitored the assets and found the cause of a major problem that had plagued the client for years. The customer respond-ed with surprise, Weve never seen such a rapid plug-n-play enterprise product deployed before! Mtell Previse made the discovery within days, whereas the competition budgeted 3-4 months just for implementation.

Unexpected Mtell Previse Manufacturing ProcessesLeading-edge customers drive even greater potential into Mtells Previses machine learning technology. Manufacturing processes are monitored for deterioration, or process degradation that may lead to product quantity and quality discrepancies. Mtell Previse can facilitate action to correct operating deviations that would otherwise spoil or waste multi-million dollar product batches.

Mtell Previse is creating a major positive change in maintenance culture. Crews become less dependent on ineffective tools and methodologies.

Production lines run smoothly with less downtime, higher quality, and increased net product output. Overall there is less risk to people,

the environment, and to the bottom line.

Far Earlier Warnings

Big Data

Less CostLess Risk

Interoperateswith Maintenance

Self-Learning

www.sense4things.io . Dubai World Central . Office A3-134 . Dubai, UAE . +971.55.2.129.129

Netherlands . Office Veghel . +31.413.85.1001

2014 Mtelligence Corporation (Mtell). All Rights Reserved. MTL-131