Add to ORKGthe research of convolutional neural networks in computer vision, natural language processing, speech recognition and other fields has made great progress and attracted the attention of researchers from all walks of life. Here we review the development history and working ideas of convolutional neural networks , and analyze applying convolutional neural networks to the problem of fault diagnosis of motor equipment, and hope that the analysis and research in this paper can provide a new viewpoint for the problem of motor equipment fault diagnosisBachelor of Engineering (Electrical and Electronic Engineering
Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals...
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and ...
Machine learning is a method generally used in defect detection of smart manufacturing. It uses data...
Bearing faults account for over 40% of induction motor faults, and for this reason, for several deca...
The demand for the condition monitoring of induction motors is increasing in various fields, such as...
Bearing faults account for over 40% of induction motor faults, and for this reason, for several deca...
In this paper, we explore the applicability of CNN for the classification of bearing defects. The ap...
As the degradation of bearing yield to an enormous adverse impact on machinery and the damage that c...
This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotat...
Many industrial facilities, amongst others, are very sensitive to any sudden hazards that can be exp...
Bearings are the significant components among the rolling machine elements subjected to high wear an...
The growth in cost and complexity of modern industrial plants leads to decreasing tolerance for perf...
Abstract The increasing complexity of modern industrial systems calls for automatic and innovative p...
Abstract Existing bearing fault diagnosis methods have some disadvantages, one being that most metho...
Traditional fault diagnosis methods require complex signal processing and expert experience, and the...
Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals...
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and ...
Machine learning is a method generally used in defect detection of smart manufacturing. It uses data...
Bearing faults account for over 40% of induction motor faults, and for this reason, for several deca...
The demand for the condition monitoring of induction motors is increasing in various fields, such as...
Bearing faults account for over 40% of induction motor faults, and for this reason, for several deca...
In this paper, we explore the applicability of CNN for the classification of bearing defects. The ap...
As the degradation of bearing yield to an enormous adverse impact on machinery and the damage that c...
This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotat...
Many industrial facilities, amongst others, are very sensitive to any sudden hazards that can be exp...
Bearings are the significant components among the rolling machine elements subjected to high wear an...
The growth in cost and complexity of modern industrial plants leads to decreasing tolerance for perf...
Abstract The increasing complexity of modern industrial systems calls for automatic and innovative p...
Abstract Existing bearing fault diagnosis methods have some disadvantages, one being that most metho...
Traditional fault diagnosis methods require complex signal processing and expert experience, and the...
Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals...
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and ...
Machine learning is a method generally used in defect detection of smart manufacturing. It uses data...