Dhiraj Neupane

With the rapid development of science and technology, the production facilities are also growing advanced. An intelligent production facility is the outcome of smart systems used within a factory. The smart factories yield more production; thus, the faults in the machinery are prompt to increase when they are operated on a daily basis and for almost all applications. Different deep learning-based methods have been used and implemented in detecting and diagnosing bearing faults using raw vibration data. To detect and analyze the machinery bearing faults, we have proposed a deep learning-based convolutional neural network, which uses the 2-D image representation of 1-D raw vibration data from the Case Western Reserve University (CWRU) bearing dataset as input. With the use of the data augmentation technique for increasing training data, the proposed model has achieved 99.38% accuracy. The proposed method is computationally less expensive and simple than most of the complex algorithms ...

With the rapid development of science and technology, the production facilities are also growing advanced. An intelligent production facility is the outcome of smart systems used within a factory. The smart factories yield more production; thus, the faults in the machinery are prompt to increase when they are operated on a daily basis and for almost all applications. Different deep learning-based methods have been used and implemented in detecting and diagnosing bearing faults using raw vibration data. To detect and analyze the machinery bearing faults, we have proposed a deep learning-based convolutional neural network, which uses the 2-D image representation of 1-D raw vibration data from the Case Western Reserve University (CWRU) bearing dataset as input. With the use of the data augmentation technique for increasing training data, the proposed model has achieved 99.38% accuracy. The proposed method is computationally less expensive and simple than most of the complex algorithms used for detecting and diagnosing the bearing faults.

A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the propo...

Bearings play a vital role in all rotating machinery, and their failure is one of the significant causes of machine breakdown leading to a profound loss of safety and property. Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. This paper presents the model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). State-of-the-art accuracy was achieved with the proposed model using the Case Western Reserve University (CWRU) bearing dataset, which serves as the primary dataset for validating various algorithms for bearing failure detection. Batch normalization techniques were also employed and compared to the proposed model. The spectrogram images were also used as input for further comparison. Using switchable normalization, the proposed model achieved the testing accuracy in between 99.44% and 100% for different batc...

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other nec...