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Enhancement of Operational Efficiency for Shipbuilding Company

Ship engines are critical engineering systems, comprising several subsystems and auxiliary systems. Maintenance of these engines through conventional methods often results in longer turnaround times to repair or replace components, impacting operational efficiency and safety. A leading Shipbuilding company partnered with Futurism to develop an AI-based condition monitoring system to assess as well as predict the health of engines, reduce maintenance costs, and improve the overall safety of ships.

Challenges Faced by the Client:

The client faced several challenges, including:

  • Analyzing and correlating relevant data and events related to multiple systems and auxiliary subsystems of ship engines
  • Lack of sufficient historical data from engine sensors impeding the development of a reliable prediction model
  • The existing OEM system supported sensor data extraction in PDF format only
  • Past events were manually logged in ledgers and needed to be digitized


Our objective was to develop an AI/ML solution that could predict the market price with an accuracy rate of over 90%, helping the client arrive at the best carrier cost and shipper price, leading to optimal margins. The client also aimed to increase the bid-win rate from 1% and to procure better carrier costs, considering lane and load attributes, while offering better shipping prices to boost gross margins per employee.


Futurism developed an AI/ML-based condition monitoring system that automated data extraction from PDF format and converted it into time-series data. The solution had a system administrator who performed activities such as raw data correlation analysis and feature selection, data cleansing (missing value imputation/outlier removal), exploring and analysis of data using charts, automatic labeling of data, and running multiple algorithms to compare KPIs and finalize the best model based on accuracy and error metrics.

The solution also had a system operator who could view the dashboard to get information on the predicted health of an engine and its subsystems based on the latest data. The operator could also perform what-if analysis of the predicted health by changing any of the engine parameters or values.


The AI-based condition monitoring system delivered several benefits, including:

  • Engine and subsystem level health predictions at least 72 hours in advance
  • Improved accuracy of prediction models
  • What-if analysis in case of any change in engine parameter
  • Reduced inventory, maintenance, and operational cost
  • Enhanced performance and safety of the ship

The modular and scalable solution built using open source technologies allowed for the extension to multiple data providers for real-time data. The solution provided a configurable solution to predict the health of any asset with sensor and event data, not limited to ship engines.

Overall, the AI-based condition monitoring system reduced the turnaround time for maintenance, improved the safety of ships, and increased operational efficiency, thereby resulting in significant cost savings for the client.


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