Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder

Algorithm 1

Training and testing of the SW1DCAE model.

	Step 1: unsupervised training
	(0) Sliding window processing training set
	(1) Build the SW1DCAE model. Set learning rate, training epochs, and other hyperparameters
	(2) Randomly initialize the weights and biases of the network model
	(3) Input training set
	(4) Cycle training times N
	(5)  Encoding, convolution, pooling
	(6)  Decode, compute upsampling, deconvolution
	(7)  Calculation error
	(8)  Gradient descent, update the gradient parameters of each layer
	(9) End loop
	(10) Save model structure, parameters
	Output: Reconstructed data , approximately equal to input data X.
	Step 2: exception test
	(0) Sliding window processing test set
	(1) Input the test set into the trained model saved in step 1.
	(2) Set the loss threshold, and the reconstruction error of the samples in the test set exceeds the threshold, which is judged as abnormal
	Output: test sample for anomalies.