scholarly journals Feature extraction and recognition of rotating machinery fault noise based on convolutional neural networks

2021 ◽  
Vol 2113 (1) ◽  
pp. 012014
Author(s):  
Yongshao Xu ◽  
Bingzheng Liu ◽  
Haotian Shang ◽  
Yueqi Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Detection Method ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Identification System ◽  
Acoustic Detection ◽  
Rotating Machine ◽  
Long Time ◽  
Non Destructive

Abstract Rotating machines are common equipment in industrial production, which may cause failure for a long time. Because of its convenient use and non-destructive to itself, acoustic detection method is suitable for fault diagnosis of rotating machinery. The convolution neural network model is used to identify several typical rotating machine faults. The repeatability experiments and different training sets show that the method has good universality. A visual fault identification system is built, and the effect of the system is verified by experiments.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Rotor Dynamics Analysis of Rotating Machine Systems Using Artificial Neural Networks

2003 ◽  
Vol 9 (4) ◽  
pp. 255-262 ◽  
Author(s):  
M. Kalkat ◽  
Ş. Yıldırım ◽  
I. Uzmay
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Vertical Direction ◽  
Rotor Dynamics ◽  
Experimental Setup ◽  
Dynamics Analysis ◽  
Rotating Machine ◽  
Rotating Systems ◽  
Machine Systems ◽  
Vibration Parameters

Adirect-coupled rotor system was designed to analyze the dynamic behavior of rotating systems in regard to vibration parameters. The vibration parameters are amplitude, velocity, and acceleration in the vertical direction. The system consisted of a machine analyzer, shaft, disk, master-trend software, and power unit. Four different points were detected and measured by the experimental setup. The vibration parameters were found and saved from master-trend software. These parameters were employed as the desired parameters of the network. A neural network is designed for analyzing a system's vibration parameters. The results showed that the network could be used as an analyzer of such systems in experimental applications.

scholarly journals KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandru Lavric ◽  
Popa Valentin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Treatment Options ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Rapid Screening ◽  
Test Set ◽  
Corneal Deformation ◽  
Long Time ◽  
High Level

Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.

scholarly journals Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

2019 ◽  
Vol 124 ◽  
pp. 05031 ◽  
Author(s):  
A.M. Sagdatullin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Oil And Gas ◽  
Gas Production ◽  
Operating Conditions ◽  
Identification System ◽  
Oil And Gas Production ◽  
The Neural Network ◽  
And Control

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

scholarly journals Improved Crack Detection and Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Keqin Chen ◽  
Amit Yadav ◽  
Asif Khan ◽  
Yixin Meng ◽  
Kun Zhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Crack Detection ◽  
Recognition Rate ◽  
Machine Learning Methods ◽  
Long Time ◽  
Detection And Recognition

Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

Increasing the Safety of Operation of Industrial Technical Systems from the Composite Materials by Predicting Their Life on the Basis of New Methods of Non-Destructive Control and Deep Neural Networks

2021 ◽  
pp. 7-12
Author(s):  
S.O. Kozelskaya ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Service Life ◽  
New Technologies ◽  
Preliminary Training ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
New Methods ◽  
Non Destructive

The problem is considered related to increase of the operational safety of industrial facilities made of composite materials by means of an a priori assessment of the maximum service life. Two tasks are being solved: development of the new methods and means of non-destructive testing allowing to identify the defects that appear in the process of testing products with various loads and in the process of their operation; development of the new methods and means for assessing service life of the products based on the results of non-destructive testing. The first problem is being solved by the development of optical-thermographic non-destructive testing, including the technologies of ultrasonic thermotomography and electric force thermography, which determine the state of the object by dynamic temperature fields and optical control technology based on the fiber-optic sensors that measure the amount of material internal deformation under a force effect on the structure. Solution to the second problem is based on the use of neural network analysis (artificial neural networks) for assessment and prediction of the service life using the results of non-destructive testing with preliminary training of the neural network. An estimate was obtained by the experimental studies related to the error in determining the products service life, which is 12.6 %. The implementation of the proposed approach will allow to create the new technologies for predicting the service life of elements and structures made of composite materials using the results of non-destructive testing, which will provide an additional opportunity for developing practical recommendations on the confirmation or extension of the service life and improvement of safety for structures operation.

Genetic Programming for Letters Identification Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Programming ◽  
Network Structure ◽  
High Accuracy ◽  
Identification System ◽  
Neural Network Structure ◽  
Hidden Layer

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.

Artificial neural network–based internal leakage fault detection for hydraulic actuators: An experimental investigation

2017 ◽  
Vol 232 (4) ◽  
pp. 369-382 ◽  
Author(s):  
Zhikai Yao ◽  
Yongping Yu ◽  
Jianyong Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Experimental Investigation ◽  
Signal Processing ◽  
Deep Neural Network ◽  
Detection Method ◽  
The Other ◽  
Detection Accuracy ◽  
Artificial Neural

Internal leakage is a typical fault in the hydraulic systems, which may be caused by seal damage, and result in deteriorated performance of the system. To study this issue, this article carries out an experimental investigation of artificial neural network–based detection method for internal leakage fault. A period of pressure signal at one chamber of the actuator was taken in response to sinusoidal-like inputs for the closed-loop controlled system as a basic signal unit, and totally, 1000 periodic signal units are obtained from the experiments. The above experimental measurements are repetitively implemented with 11 different active exerted internal leakage levels, that is, totally 11,000 basic signal units are obtained. For signal processing, the pressure signal in the operation condition without active exerted leakage is chosen to generate a baseline with suitable pre-proceed, and the relative values of the other basic signal units (D-value between the baseline and other original signals) act as the global samples of the following artificial neural networks, traditional back propagation neural network, deep neural network, convolution neural network and auto-encoder neural network, separately; 8800 samples by random extraction as train samples to train the above neural networks and the other samples different from the train samples act as test samples to examine the detection accuracy of the proposed method. It is shown that the deep neural network with five layers can obtain a best detection accuracy (92.23%) of the above-mentioned neural networks. In addition, the methods based on wavelet transform and Hilbert–Huang transform are also applied, and a comparison of these methods is provided at last. From the comparison, it is shown that the proposed detection method obtains a good result without a need to model the internal leakage or a complicated signal processing.

scholarly journals Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 205
Author(s):  
Hassan Tariq ◽  
Muhammad Rashid ◽  
Asfa Javed ◽  
Eeman Zafar ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Deep Convolutional Neural Networks ◽  
Appropriate Treatment ◽  
Long Time ◽  
Computer Based ◽  
Degree Classification ◽  
Eye Exams

Diabetic retinopathy (DR) is a human eye disease that affects people who are suffering from diabetes. It causes damage to their eyes, including vision loss. It is treatable; however, it takes a long time to diagnose and may require many eye exams. Early detection of DR may prevent or delay the vision loss. Therefore, a robust, automatic and computer-based diagnosis of DR is essential. Currently, deep neural networks are being utilized in numerous medical areas to diagnose various diseases. Consequently, deep transfer learning is utilized in this article. We employ five convolutional-neural-network-based designs (AlexNet, GoogleNet, Inception V4, Inception ResNet V2 and ResNeXt-50). A collection of DR pictures is created. Subsequently, the created collections are labeled with an appropriate treatment approach. This automates the diagnosis and assists patients through subsequent therapies. Furthermore, in order to identify the severity of DR retina pictures, we use our own dataset to train deep convolutional neural networks (CNNs). Experimental results reveal that the pre-trained model Se-ResNeXt-50 obtains the best classification accuracy of 97.53% for our dataset out of all pre-trained models. Moreover, we perform five different experiments on each CNN architecture. As a result, a minimum accuracy of 84.01% is achieved for a five-degree classification.

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