The Archives

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Learn about our Fuel Efficiency Solutions at 2013 Fleet Optimization Conference

Friday, October 4th, 2013

SI LogoMACSEA will be an exhibitor at the upcoming Fleet Optimization Conference presented by Shipping Insight, October 22 – 24, 2013 at the Sheraton Stamford Hotel in Stamford, Connecticut.  Read More

DEXTER Diesel Expert™ Module Helps Keep Engines Fuel Efficient

Friday, March 22nd, 2013

MACSEA successfully tested its Diesel Expert combustion analysis intelligence software aboard the USS Fort McHenry (LSD-43) during recent testing in Norfolk, Virginia. The Diesel Expert software accepts data inputs from cylinder combustion analyzers and automatically diagnoses any existing engine faults. Diesel Expert contains expert-level diagnostic intelligence in the form of an embedded neural network that has been trained to recognize symptoms associated with various engine problems. Read More

Fuel Vision Now Works with KRAL Fuel Flowmeters

Saturday, December 15th, 2012

Stonington, CT (USA), December 15, 2012 – MACSEA’s Fuel VisionTM system now works with KRAL® rotary displacement flow meters for high-accuracy fuel monitoring to help ship operators quantify the real effects of various energy conservation measures. Read More

DEXTER Covered in Motorship

Friday, November 30th, 2012

Wendy Laursen of The Motorship magazine covers DEXTER in the November 2012 issue. She explains how alarm flooding due to poor sensor maintenance can desensitize operators and hide important machinery plant problems. Read More

Fuel Vision Demonstrated at SHIPPING Insight Fleet Optimization Conference

Wednesday, October 10th, 2012

The 2012 Fleet Optimization Conference, organized by ShippingInsight, provided a two-day forum during October 9-10 for ship owners and operators to explore how to achieve gains in ship efficiency. MACSEA teamed up with Emerson Process Management to demonstrate a simple, yet effective solution, for measuring and monitoring a ship’s fuel performance. Read More

High-Accuracy Fuel Monitoring with Fuel Vision

Monday, October 1st, 2012

MACSEA’s Fuel Vision system now works with Emerson’s Micro Motion Coriolis® flow meters for high-accuracy fuel monitoring to help ship operators quantify the real effects of various energy conservation measures. Read More

Data Security for Remote Equipment Health Monitoring

Friday, August 24th, 2012

According to the U.S. Department of Commerce, approximately 90 percent of the nation’s critical infrastructures are privately owned and operated. Various industries included in these infrastructures, such as transportation, electric, oil and gas, water, etc. have come to rely heavily on industrial control systems for equipment health monitoring, quality control, and remote maintenance support.

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DEXTER Supports Open Systems Architecture for Diagnostic Data Interchange

Wednesday, August 1st, 2012

DEXTER reports machinery diagnostic and prognostic results in XML-based formats for importing into most computerized maintenance management systems. This capability facilitates data interchange between disparate IT systems using XML, including legacy systems. High design and development costs associated with compliance to specific protocols can be avoided using XML schema definitions.

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Teaching Computers to Think Like Engineers

Monday, July 30th, 2012

The marine industry is increasingly adopting Condition-Based Maintenance (CBM) as cost-effective strategy for Reduced Total Ownership Cost, fostering the approach of performing maintenance only when objective evidence of need exists.  However, because of the special skills and time required to implement CBM, particularly as ship systems become more complex, future ship systems should employ artificial intelligence to make the equipment smart enough to assess its own health and alert control systems and crews of failures and  performance degradations.

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Sensors – The Eyes and Ears of Ship Automation – Part 2

Wednesday, June 13th, 2012

Sensors are the eyes and ears of your automation. Their health is essential to all shipboard monitoring and control functions that require reliable data to synthesize decisions.

In Part 2 of this series, we present some advanced research involving two multivariate machine learning algorithms; nonlinear state estimation and support vector machines, both applicable to shipboard sensor diagnostics. Data collected from a ship’s main propulsion gas turbine engine is used in the case study.

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