How Predictive Analytics can Predict Cable Failures
Do you know that unknown future events can be analyzed? Yes, it is called Predictive analytics. It works with data mining using predictive modeling to extract information from data and then practically make a statistical model of it so as to measure the future or a new data outcome.
Regarding cable asset failure, it has been observed that maintenance engineers have acquired a high volume of recorded alarms from top drives. However, in recent times, this method has been very challenging to manage manually as the number of alarms is being generated per second.
This could be resolved when Predictive analytics are combined with automated machine learning (AutoML) models as it can help to predict impending failures prior to their occurrence, thereby decreasing downtime and maintenance costs. Notably, there are other good method options to use.
Impending failures in advance to decrease cable asset downtime
Predictive analytics play a very crucial role in the cable maintenance system as it performs by collecting data from external sources like weather and ocean-related information, internal sources (like cable maintenance records), and also historical fault data and then projects what becomes of the cable's future if there is a hazardous risk on the way or not as well as the impending time. For instance, If cable failure could be analyzed, several months before the actual happening, then the organization could gain enough time to act on the impending failure thereby reducing downtime and repair costs.
This process takes less time, and it is also more efficient if compared to the manual identification of high-risk sections of the cable. It is worthy to note that this method has been used successfully to detect about 85% of Cable faults in a nine-month projected duration.
How to Utilize Predictive Analytical Analysis of Alarms
Alarm monitoring is another method used to predict impending cable failures. When using Alarm analytics, you can practically monitor and detect cable issues on time. Alarm analytics are typically gotten from the cable's alarm
Can predictive analytics of alarms drive preventive maintenance?
Yes! Predictive analytics of alarms can drive preventive maintenance although it depends on the following factors
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Data collection: this part is very necessary as it can dissolve the whole process of efficiency if the wrong data is used. False results could lead to false alarms. Make sure all parameters and equipment/ instruments used are well articulated and placed.
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Predictive modeling techniques: Thoroughly identify all the alarms involved for a specific preventive maintenance task, then analyze their characteristics and correlations using sophisticated mathematical algorithms. After that, safely store them as they could help you predict future foreseeable events.
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Use of solutions for specific tasks. When alarms are triggered, make sure to record correctly the specific triggered danger so as to be able to use the future on similar events. If they are wrongly placed, the whole process might be useless as wrong data will be collected.
In conclusion, the use of these methods is very necessary for predicting cable failures which is far better than waiting till the incident occurs before making a move. Many companies have saved millions of dollars simply by using these methods. There are a lot of cable failures that when dictated early could be well managed or avoided. Don't waste funds on avoidable issues!
Learn about this and more at the 2nd Annual Submarine Power Cable and Interconnection Forum coming up on the 15th - 16th of November 2022.
The 2nd Annual Submarine Power Cable and Interconnection Forum aim to provide participants with cutting-edge information on the hot topics trending in the submarine power cable market and how to navigate some critical challenges that are projected in the Submarine power market worldwide. This summit will also serve as an avenue for expert interactions and product exhibitions as key industry players will be available.
Join us so you don't feel left out in the industry.
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