PUBLISHED NOV 12, 2021 11:42 AM BY SUBRAT NANDA

 

We all know that damaged and stranded ships can carry enormous safety and cost implications. According to the latest Safety and Shipping Review from Allianz Global Corporate & Specialty (AGCS), machine faults are the top cause of shipping incidents globally, accounting for four in every 10 during 2020 and creating serious safety concerns.

And of the 26,000-plus incidents over the past decade, more than a third (9,334) were caused by machinery damage or failure – over twice as many as the next highest cause (collision).

The financial cost can also be eye-watering. In the offshore oil and gas space, for example, organizations experience on average $49 million annually in financial impacts due to unplanned downtime, with the worst performers losing upwards of $88 million. On top of this, equipment failures can also create serious safety concerns.

Being able to perform anomaly detection to identify potential anomalous conditions, and from there predict failures ahead of time, can yield tremendous results in terms of operational continuity and decision making for maintenance, reducing operational costs and safety benefits to vessel operators and crew.

In addition to being able to detect anomalies and predict failures, accurate and reliable diagnoses are key to ensuring effective responses to problems. Whether this be in terms of inventory management, maintenance work or vessel operation to minimize the fault’s impact, data science can help decision makers to make the right calls at the right time.

 

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https://www.maritime-executive.com/editorials/predicting-power-equipment-failure-with-data-science