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Question 235 - MLS-C01 discussion

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A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.

The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and ...perature for each crane. The company contracts AWS ML experts to implement an ML solution.

Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

A.
The historical sensor data does not include a significant number of data points and attributes for certain time periods.
Answers
A.
The historical sensor data does not include a significant number of data points and attributes for certain time periods.
B.
The historical sensor data shows that simple rule-based thresholds can predict crane failures.
Answers
B.
The historical sensor data shows that simple rule-based thresholds can predict crane failures.
C.
The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.
Answers
C.
The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.
D.
The historical sensor data from the cranes are available with high granularity for the last 3 years.
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D.
The historical sensor data from the cranes are available with high granularity for the last 3 years.
E.
The historical sensor data contains most common types of crane failures that the company wants to predict.
Answers
E.
The historical sensor data contains most common types of crane failures that the company wants to predict.
Suggested answer: D, E

Explanation:

The best indicators that an ML-based solution is suitable for this scenario are D and E, because they imply that the historical sensor data is sufficient and relevant for building a predictive maintenance model.This model can use machine learning techniques such as regression, classification, or anomaly detection to learn from the past data and forecast future failures or issues12.Having high granularity and diversity of data can improve the accuracy and generalization of the model, as well as enable the detection of complex patterns and relationships that are not captured by simple rule-based thresholds3.

The other options are not good indicators that an ML-based solution is suitable, because they suggest that the historical sensor data is incomplete, inconsistent, or inadequate for building a predictive maintenance model.These options would require additional data collection, preprocessing, or augmentation to overcome the data quality issues and ensure that the model can handle different scenarios and types of cranes4.

References:

1:Machine Learning Techniques for Predictive Maintenance

2:A Guide to Predictive Maintenance & Machine Learning

3:Machine Learning for Predictive Maintenance: Reinventing Asset Upkeep

4:Predictive Maintenance with Machine Learning: A Complete Guide

: [Machine Learning for Predictive Maintenance - AWS Online Tech Talks]

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asked 16/09/2024
Victor Bogdan Grecu
34 questions
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