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…

An insufficient number of data points and attributes directly hinders the ability of an ML model to learn robust patterns, making an ML solution unsuitable due to data scarcity.

If simple rule-based thresholds are sufficient to predict failures, then an ML-based solution, which is more complex and resource-intensive, is not necessary or suitable, as simpler methods achieve the desired outcome.

Historical failure data for only one type of crane model limits the generalizability of an ML model to the entire fleet, making it less suitable for a comprehensive predictive maintenance solution across diverse crane types.

High granularity historical sensor data over a long period (3 years) provides a rich dataset for an ML model to learn complex patterns and relationships between sensor readings and crane health, which is crucial for accurate predictive maintenance.

Having historical data that includes most common types of crane failures is essential because it provides the necessary labels for the ML model to learn to identify and predict these specific failure modes. Without examples of failures, an ML model cannot effectively predict them.