Rolling element bearings are regarded as one of the critical components in industrial applications. In order to avoid the malfunctions and awful failures of the machinery, fault diagnosis plays a major role. Also, to increase the efficiency of monitoring systems, conventional diagnostic techniques have been replaced by Artificial Intelligence (AI) based methods. Deep learning (DL) as an advancement in AI, is found to be useful compared to the shallow structured Artificial Neural Networks (ANNs), in addition to the elimination of the need for diagnostic expertise. This paper presents a new 1-Dimensional Deep Convolutional Neural Network (1-D DCNN) based intelligent fault diagnosis method for rolling element bearings. The model is trained and tested using Case Western Reserve University (CWRU) dataset and is designed to classify ten fault classes using the acquired vibration signals. Unlike conventional DCNN architectures, the model does not use Fully connected (FC) layer. Thus, considerable decrease in the number of parameters is achieved and also leads to an increase in classification accuracy and decrease in computation time. This indeed reduces the computation power required and thus eliminates the need for using Graphical Processing Units (GPUs) for training deep learning neural networks, which is a significant contribution of this work.

The automatic detection and recognition of characters in images are an important problem in various applications. The traditional shallow networks in machine learning have limitations in image classification due to their inability to effectively utilize the spatial relationships between the pixels of the image. But the incredible advances in deep learning methods and deep architecture in the recent years have opened doors to the possibility of employing these techniques. In this paper, a deep convolutional neural network (CNN) with minimal preprocessing for the effective classification of handwritten characters has been proposed. The application of this network yielded an accuracy of 99.50 and 94.66% on the test data of MNIST and EMNIST datasets, respectively.