A fault detection and classification method of multi-sensors is provided. The method includes the following steps: collecting the plurality of raw sensory data by the plurality of sensors, conducting the data normalization process and data augmentation process by the processor, conducting the feature extraction process by using the convolution neural network having the convolution layer, the activation layer, and the pooling layer, setting a diagnosis layer by connecting the plurality of feature maps to the single neuron, and obtaining the plurality of weight values of the plurality of sensors by using the activation function, and obtaining the abnormal probability by using the calculation of the multilayer perceptron neural network.
METHOD AND SYSTEM FOR GENERATING TWO DIMENSIONAL BARCODE INCLUDING HIDDEN DATA