Systems and Methods for Determining Operability Status of a Ball-Throwing Machine Through Machine Learning Techniques

1 . A computerized method for determining an operability status of a ball-throwing machine, the computerized method comprising:

obtaining sensor data collected by a sensor during deployment of the ball-throwing machine to perform operations according to a training program, wherein the sensor is disposed on the ball-throwing machine;

deploying a trained machine learning model on the sensor data resulting in a classification of a current operability status of the ball-throwing machine or a prediction of a future operability status of the ball-throwing machine; and

generating an alert when the classification indicates that the ball-throwing machine is operating abnormally or the prediction indicates the ball-throwing machine is predicted to operate abnormally.

2 . The computerized method of claim 1 further comprising:

obtaining training sensor data from the sensor collected during deployment of the ball-throwing machine under known conditions to perform the operations according to the training program;

obtaining labels for the training sensor data; and

processing the labeled training sensor data with a machine learning algorithm resulting in the trained machine learning model through adjustment of internal parameters of the trained machine learning model causing a mapping of the training sensor data to the labels.

3 . The computerized method of claim 2 , wherein the machine learning algorithm is a classification algorithm such that the trained machine learning model resulting from processing the labeled training sensor data with the machine learning algorithm is configured to classify the current operability status of the ball-throwing machine.

4 . The computerized method of claim 2 , wherein the machine learning algorithm is a predictive algorithm such that the trained machine learning model resulting from the processing the labeled training sensor data with the machine learning algorithm is configured to predict the future operability status of the ball-throwing machine.

5 . The computerized method of claim 4 , wherein the machine learning algorithm is linear regression.

6 . The computerized method of claim 1 , wherein the sensor data includes at least one of audio data collected by a microphone, vibration data collected by an accelerometer, ball-presence data collected by a ball-sensor, temperature data collected by a temperature sensor, or current data collected by a current sensor.

7 . The computerized method of claim 6 , wherein the microphone and the accelerometer are disposed on a control unit of the ball-throwing machine.

8 . A computerized method for determining an operability status of a ball-throwing machine, the computerized method comprising:

obtaining sensor data collected by a sensor during deployment of the ball-throwing machine to perform operations according to a training program, wherein the sensor is disposed on the ball-throwing machine;

deploying a neural network to process the sensor data resulting in a classification of a current operability status of the ball-throwing machine or a prediction of a future operability status of the ball-throwing machine; and

generating an alert when the classification indicates that the ball-throwing machine is operating abnormally or the prediction indicates the ball-throwing machine is predicted to operate abnormally.

9 . The computerized method of claim 8 further comprising:

obtaining training sensor data collected by the sensor during deployment of the ball-throwing machine under known conditions to perform the operations according to the training program;

obtaining labels for the training sensor data; and

performing a neural network training process including multiple iterations of a feedforward pass, computing a loss function, a backpropagation step, and updating weights of the neural network resulting in adjustment of the weights of each layer of the neural network.

10 . The computerized method of claim 9 , wherein the neural network utilizes an activation function and an output layer function each configured to classify a current operability status of the ball-throwing machine.

11 . The computerized method of claim 9 , wherein the neural network utilizes an activation function and an output layer function each configured to predict a future operability status of the ball-throwing machine.

12 . The computerized method of claim 8 , wherein generating the alert includes causing display of the alert on a display screen of a network device communicatively coupled to the ball-throwing machine.

13 . The computerized method of claim 8 , wherein the sensor data includes at least one of audio data collected by a microphone, vibration data collected by an accelerometer, ball-presence data collected by a ball-sensor, temperature data collected by a temperature sensor, or current data collected by a current sensor.

14 . The computerized method of claim 13 , wherein the microphone and the accelerometer are disposed on a control unit of the ball-throwing machine.

15 . A computerized method for determining an operability status of a ball-throwing machine, the computerized method comprising:

obtaining sensor data collected by a sensor during deployment of the ball-throwing machine to perform operations according to a training program, wherein the sensor is disposed on the ball-throwing machine;

determining either a classification of a current operability status of the ball-throwing machine or a prediction of a future operability status of the ball-throwing machine through deployment of a machine learning technique; and

generating an alert when the classification indicates that the ball-throwing machine is operating abnormally or the prediction indicates the ball-throwing machine is predicted to operate abnormally.

16 . The computerized method of claim 15 further comprising:

obtaining training sensor data collected by the sensor during deployment of the ball-throwing machine under known conditions to perform the operations according to the training program;

obtaining labels for the training sensor data; and

processing the labeled training sensor data with a machine learning algorithm resulting in a trained machine learning model through adjustment of internal parameters of the trained machine learning model causing a mapping of the training sensor data to the labels, wherein the machine learning technique is deployment of the trained machine learning model.

17 . The computerized method of claim 16 , wherein the machine learning algorithm is a classification algorithm such that the trained machine learning model resulting from processing the labeled training sensor data with the machine learning algorithm is configured to classify the current operability status of the ball-throwing machine.

18 . The computerized method of claim 16 , wherein the machine learning algorithm is a predictive algorithm such that the training machine learning model resulting from the processing the labeled training sensor data with the machine learning algorithm is configured to predict the future operability status of the ball-throwing machine.

19 . The computerized method of claim 15 further comprising:

obtaining training sensor data collected by the sensor during deployment of the ball-throwing machine under known conditions to perform the operations according to the training program;

obtaining labels for the training sensor data; and

performing a neural network training process including multiple iterations of a feedforward pass, computing a loss function, a backpropagation step, and updating weights of the neural network resulting in adjustment of the weights of each layer of the trained neural network, wherein the machine learning technique is deployment of the neural network.

20 . The computerized method of claim 19 , wherein the neural network utilizes an activation function and an output layer function each configured to either classify a current operability status of the ball-throwing machine or predict a future operability status of the ball-throwing machine.