Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

2020 ◽  
Vol 14 (10) ◽  
pp. 953-961
Author(s):  
Stéfano Frizzo Stefenon ◽  
Roberto Zanetti Freire ◽  
Luiz Henrique Meyer ◽  
Marcelo Picolotto Corso ◽  
Andreza Sartori ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Fault Detection ◽  
Ultrasonic Signal ◽  
Learning Technique

Fault detection in water pumps based on sound analysis using a deep learning technique

2021 ◽  
pp. 095440892110393
Author(s):  
Minh T Nguyen ◽  
Jin H Huang
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Operating Conditions ◽  
Water Tank ◽  
Sound Analysis ◽  
Proposed Model ◽  
Learning Technique ◽  
The Status ◽  
Water Pumps ◽  
Abnormal Operating Conditions

Machine fault detection is designed to automatically detect faults or damage in machines. When a machine operates, it produces vibrations and sound signals that can be analyzed to provide information about the status of the machine. This study proposed a method to detect the faults in a machine based on sound analysis using a deep learning technique. The sound signals generated by the machine were obtained and analyzed under different operating conditions. These signals were first pre-processed to eliminate noise, and then the features were extracted as mel-spectrograms so that the convolutional neural network could automatically learn the appropriate features required for classification. Experiments were conducted on three different water pumps during suction from and discharge to the water tank under normal and abnormal operating conditions. The high accuracies in fault detections in both known and unknown machines indicated that the proposed model performed very well in the detection of machine faults.

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

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