Apparatus for predicting equipment damage

1. An apparatus for predicting equipment damage, the apparatus comprising:

an input;

a processor; and

an output;

wherein the input is configured to provide the processor with sensor data for an item of equipment,

wherein the processor is configured to implement at least one machine learning algorithm,

wherein the at least one machine learning algorithm has been trained based on a plurality of calibration sensor data for the 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,

wherein the processor is configured to implement the at least one machine learning algorithm to process the sensor data 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

wherein the output is configured to output the equipment state for the item of equipment.

2. The apparatus according to claim 1 , wherein: the moving average calculation is performed using an equation V(t)=β*V(t−1)+(1−β)*θ(t), wherein V(t) is a value of the moving average for a current time t, V(t−1) is a value of the moving average for a previous time t−1, β is a decay parameter, and θ(t) is an input at time t.

3. The apparatus according to claim 1 , wherein the determination of the at least two distributions of the transformed key condition indicator data is based on at least two peaks in the transformed key condition indicator data.

4. The apparatus according to claim 1 , wherein:

the sensor data are velocity data and the calibration sensor data are velocity data, and

the associated key condition indicator data are root mean square velocity data.

5. The apparatus according to claim 4 , wherein the sensor data are rotational velocity data, and the calibration sensor data are rotational velocity data.

6. The apparatus according to claim 1 , wherein training of the at least one machine learning algorithm comprises utilization of user input data relating to an equipment state associated with at least one subset of the calibration data.

7. The apparatus according claim 6 , wherein training of the at least one machine learning algorithm comprises utilization of user input data relating to a different equipment state associated with at least one second subset of the calibration data.

8. A system for predicting equipment damage, the system comprising:

at least one sensor; and

the apparatus for predicting equipment damage according to claim 1 ,

wherein the at least one sensor is configured to acquire the sensor data and provide the sensor data to the processor.

9. The system according to claim 8 , wherein the at least one sensor is configured to acquire the calibration sensor data.

10. 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.

11. 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.