“Higher Productivity is Created by Simple Solutions”

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Oil & Gas

In upstream drilling, Mtell drastically reduces drilling downtime avoiding delays in completion, rig drilling costs, and production deferrals. In downstream and midstream major saves have occurred on compressors, pumps, incinerators, etc. In one multi-stage compressor, Mtell gave 7 weeks earlier warning than the dedicated vibration system.

Company Saves Millions on Averted Failures

Challenge: A large, multi-site mid-stream company was having persistent failures of their pumps for their oil wells and failures of pipeline compressors, costing the business as much as $1m per incident.

Solution: Using historical data captured directly from the plant historian, Mtell demonstrated that its anomaly detection process could have averted all of the well failures that were included in the pilot evaluation. As a result the company is implementing a wider roll-out of the software across the division.

Benefit: The company is now able to predict and avoid failures on its protected machinery, saving millions of dollars in the process.


Mining saves major costly mine shutdowns by averting failures on tailings discharge pumps. Those metals refining units with Mtell run smoother, more reliably, with fewer process perturbations. Wholesale changes in maintenance culture have occurred due to Prescriptive actions allowing early simple repairs that avoid costly shutdowns.

Reliability Improved After Deploying Mtell

Challenge: Rigorous adherence to the maintenance schedule on its large slurry pumps failed to deliver the expected reliability performance for this mining customer.

Solution: Customer implemented Mtell machine learning for its predictive-scheduling capability.

Benefit: The pumps now give a “heads up” of potential failures and changes in operation well before serious damage can occur. Now, predictive-scheduling provides maintenance in an as-needed, timely, and orderly fashion. Customer avoids major production losses and equipment failure with dollar savings in six figure sums each month.


Mtell is deployed in light and heavy chemicals protecting process plant equipment. Significantly, ease of use and rapid deployment ensures Mtell Previse can monitor and protect the whole range of large and small equipment alike. Chemical companies are also deploying Mtell Previse for early warnings of degradation that occur due to both process conditions and equipment wear and tear.


Mtell began its journey into transportation doing pattern recognition on manually collected engine lubrication oil samples from locomotives. Spectacular saves on locomotives have occurred with weeks’ worth of advance warnings on conditions that were previously undetectable by other contemporary predictive methods. Mtell Previse is being deployed in real-time analysis of locomotive drive, power, transmission, cooling systems, and hot bearing detection.


Impending failure warnings continue to prevent major failures in drug production through early warnings on utility equipment which would stop the process and spoil expensive batches of drugs. Today, pharma companies are investigating Mtell proficiency in detecting minor (currently undetectable) process irregularities that can cause ultimate batch failures and using Mtell prescriptive action to correct the manufacturing process conditions to avoid product waste.

Large Pharmaceutical Company Averts Failures, Saves Millions

Challenge: At a large-scale pharmaceutical manufacturing location, large chillers and compressors are critical equipment infrastructure. In spite of all six sigma efforts, failures still caused enormous losses. Aging equipment, increasing energy usage over time, higher maintenance (where 35% is corrective and 65% preventive), and inadequate equipment health status reporting contributed to the problem.

Solution: The equipment now warns of impending failures and advises when it should be maintained.

Benefit: Mtell predictive-scheduling using advance machine learning reversed the situation, providing sufficient notice for orderly, rapid problem correction at the lowest cost. A direct link to the maintenance system also increased operator involvement. The net result is a dramatic improvement in overall production valued at $millions per year.


In an extremely price sensitive business, Mtell protects critical equipment and assists in maintenance scheduling for many customers world-over. Water industries have been the earliest adopters of Mtell technologies, starting with the Mtell Basis product to upload run-time hours from operations systems to maintenance systems for better maintenance planning. Water companies have also adopted machine learning in Mtell Previse to protect the larger assets and reduce the risk to its customers.

Water Treatment Facility Improves Reliability on Tight Budget

Challenge:  A large water treating company faced enormous problems with budget cuts, increasing energy costs, personnel reduction, aging equipment, and increased regulatory scrutiny resulting in a desperate need for improved reliability with lower costs

Solution:  Mtell implemented condition-based monitoring using machine learning on a large gas-powered generator and other assets. Mtell learned normal and abnormal conditions and applied them in real-time monitoring profiles. The software now senses critical conditions and observes new operating states. Then, predictive-scheduling automatically requests maintenance and alerts operations and maintenance staff.

Benefit:  Timely maintenance and better visibility of asset health has prompted the systems manager to declare, “This (Mtell) system saves us literally thousands of dollars in reduced downtime.”

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