Apparatus for predicting equipment damage

A method for predicting equipment damage, the method comprising:

• training at least one machine learning algorithm based on a plurality of calibration sensor data for an item of equipment, wherein training of the at least one machine learning algorithm comprises:
• processing the plurality of calibration sensor data to determine a plurality of associated key condition indicator data,
• transforming the key condition indicator data using a moving average calculation over time to obtain transformed key condition indicator data, and
• determining at least two clusters representative of different equipment states by determining at least two distributions of the transformed key condition indicator data;
• implementing by a processor the at least one machine learning algorithm to process sensor data for the item of equipment to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment;
• and
• outputting the equipment state for the item of equipment.

A non-transitory computer-readable medium that stores a computer program, which upon execution by a processor, causing performance of a method, the method comprising:

• training at least one machine learning algorithm based on a plurality of calibration sensor data for an item of equipment, wherein the training of the at least one machine learning algorithm comprises:
• processing the plurality of calibration sensor data to determine a plurality of associated key condition indicator data,
• transforming the key condition indicator data using a moving average calculation over time to obtain transformed key condition indicator data, and
• determining at least two clusters representative of different equipment states by determining at least two distributions of the transformed key condition indicator data;
• implementing the at least one machine learning algorithm to process sensor data for the item of equipment to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment;
• and
• outputting the equipment state for the item of equipment.