pattern recognition neural network

i am currently doing an investigation to develop an AI based bearing fault diagnosis model. within the process comes signal processing done in matlab as well as neural network done in matlab which i have never done and i am not getting enough information to do this as due to the missing pieces i am not able to complete the technical issues concerning the investigation. i would like to know how to ensure that my target variable option is available for selection the target tab in the neural network pattern recognition.

[KSSV]

What data you have? What is your input and target?

[bpk]

My data is the bearing inner race defect data for 5 cases and each data shows acceleration and time with sampling frequency. I’m trying to figure out what will be the inputs and targets according to the data. And I have to proof that the inputs and targets would show the bearing defects and defect sizes. Another thing I wanted to clear out is the regarding the envelope analysis of the data and parameters chosen from it how do I go about this ?

[bpk]

Please can anyone respond quicker as I have a deadline approaching and I would like major help as I’m new to matlab.

Answer 1

[Shubham]

Hi bpk,

To ensure that your target variable is available for selection in the Neural Network Pattern Recognition Tool (nnprtool) in MATLAB, you need to follow several steps. Below is a guide to help you prepare your data and use the tool effectively:

Prepare Your Data:

• Your data should be organized in a way that MATLAB can recognize. Typically, this involves having your input features in one matrix and your target variables in another matrix.
• For a bearing fault diagnosis model, your input features might be derived from signal processing techniques such as Fast Fourier Transform (FFT), time-domain features, or wavelet transforms.
• Your target variable should be a categorical variable representing different states of the bearing (e.g., healthy, inner race fault, outer race fault, etc.).

Format Your Data:

• Input matrix: Each column represents an individual sample, and each row represents a feature.
• Target matrix: Each column represents an individual sample, and each row represents a target class. The target matrix is usually formatted in a one-hot encoded style, where each class is represented by a column with a 1 in the row corresponding to the class and 0s elsewhere.

Load Your Data into MATLAB:

• Use the load function if your data is stored in a .mat file or use appropriate functions like readmatrix, csvread, or xlsread if your data is in a CSV or Excel file.

Open the Neural Network Pattern Recognition Tool:

• In the MATLAB Command Window, type nnprtool and press Enter.

Import Data in nnprtool:

• Once the nnprtool GUI opens, you'll need to import your input and target data.
• Click on the “Import Data” button and select your input and target matrices. Make sure they are in the MATLAB workspace.

Configure Neural Network:

• After importing the data, you can configure your neural network by specifying the number of hidden layers, the number of neurons, the training function, etc.

Train the Neural Network:

• Proceed to train the network with your data. The tool will use the input features to predict the target variables.
• The tool may automatically split your data into training, validation, and test sets, or you can specify the split ratios.

Ensure Target Variable Availability:

• If you've correctly formatted and imported your target matrix, it should be available for the neural network to use.
• If you encounter issues, double-check that the target matrix is correctly one-hot encoded and matches the number of samples in your input matrix.

Train and Validate the Model:

• After setting up, train the model by clicking the "Train" button.
• Once training is complete, you can validate the performance of the neural network using the provided tools and confusion matrix.