“So you pick a PoC, bring in the IT skills, bring in the Data Science person, get the engineering sponsor and domain knowledge, and it is a hit! Excellent! Then you do another project, refine your skills and in 5-7 years of success you will be at the point where Mtell is today!”

– Marketing Development Executive, Worldwide Software Company

Different by Design

The Mtell approach is a departure from current analytical techniques. During journeyman years, Mtell experienced firsthand the pervasive operational and maintenance issues. Those experiences have influenced our approach. The result automates as much as possible, to assure that skills and effort to implement and maintain are as low as practical, and that data scientists are helpful but are not essential.

Agents do the Work

Not data scientists in a “sand-box” – live agents run in real-time, all the time, doing the heavy-lifting, comparing incoming data for matches with a learned behavioral pattern of equipment in a specific operating mode or exact degradation cycle. They give off predictive alerts and prescriptive advice, and generate work orders and work processes to ensure facilitate problem resolution.

Closes the Competency Gap

Mtell products are designed from the beginning with the “rocket-science” built on the inside, so that more people can effectively use the software and derive value without significant skills or experience in machine and maintenance methodologies, IT systems, intense analytical techniques, or data science. The efficacy of Mtell assures the reliability professional can impart a far higher influence on industrial performance. 

Shared Learning

Again using live agents technology, Mtell can learn the patterns for normal operation and specific signatures of degradation/failure on one machine and transfer that learning to all similar machines. Within a short retraining period, all machines have the same protection. Additionally, Mtell embeds the latest machine learning algorithms to permit the most accurate collective learning of operational and failure modes across many pieces of equipment in pools or fleets simultaneously.

Net Output

Mtell products recognize symptoms of deviation from normal far earlier than other techniques, permitting action to prevent or minimize unwanted process excursions. Field experience has proven that operations are significantly more stable with consequent sizable increases in quality and net product output..

Rigorous 24/7 Surveillance

Live agents do the monitoring in real-time, all the time, examining incoming data feeds for matches and mismatches against precisely known signatures. They continue to learn and adapt, retaining and building knowledge into the future without taking breaks, weekends off, or vacations. Agents also, readily accept new monitoring signatures after the events that stimulate learning and adaptive cycles.

Asset Integrity, Risk

Equipment failure can cause regulatory compliance, safety, environmental, and business risks. Mtell has proved particularly effective in providing extreme early, actionable warnings to avoid risk of encountering circumstances that stress the operational integrity of equipment and introduce undue risk.

Causes of Failure

Normal wear and tear can cause equipment degradation, but a high portion of damage to equipment is caused by operational abuse such as pump cavitation, or liquid carry-over into compressors. Mtell is designed to cast a wide “net” around equipment to forewarn of all process and machine conditions that can lead to degradation and failure. Instead of alerting on symptoms of damage, Mtell detects the root cause of degradation and issues alerts allowing action to eliminate the source of damage.

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