scholarly journals Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection

2020 ◽  
Vol 51 ◽  
pp. 266-273
Author(s):  
Syed Muhammad Tayyab ◽  
Eram Asghar ◽  
Paolo Pennacchi ◽  
Steven Chatterton
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Features Extraction ◽  
Intelligent Fault Diagnosis ◽  
Rotating Machine ◽  
Machine Elements

scholarly journals Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2411
Author(s):  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Michał Pająk ◽  
Mihael Gudlin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Bayesian Optimization ◽  
Intelligent Fault Diagnosis ◽  
Accelerometer Signal ◽  
Rotary Machinery

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

A Bearing Intelligent Fault Diagnosis Method Based on Cluster Analysis

2012 ◽  
Vol 152-154 ◽  
pp. 1628-1633 ◽  
Author(s):  
Su Qun Cao ◽  
Xiao Ming Zuo ◽  
Ai Xiang Tao ◽  
Jun Min Wang ◽  
Xiang Zhi Chen
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Test Data ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Type ◽  
Diagnosis Method

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

Research of Intelligent Fault Diagnosis Based on Machine Learning

2021 ◽  
Author(s):  
Ziwei Ding ◽  
Jiangwei Zhou ◽  
Bailin Liu ◽  
Wanmin Bai
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis

scholarly journals Performance Improvement of Classifier in Fault Diagnosis of Rotating Machines Using Sensor Fusion Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 1136-1141
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Vibration Signal ◽  
Fault Classification ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Vibration Signal Analysis

The shaft, rotor, bearing and gear are the important elements of the rotating machines. Most of the problems in rotating machines are caused due to bearings and shaft. The failure of rotating machine causes production downtime and economic & safety issues. Vibration signal analysis is highly accepted technique in fault diagnosis of rotating machine. For automation of fault diagnosis, machine learning approach has been followed. Machine learning classifies fault based on variation in signatures pattern of the machine. But its effectiveness gets reduced when it is used for multi fault class problem. So in the present work, sound signals are also used along with vibration signals for applying sensor fusion techniques. In sensor fusion, signals from various sensors are fused in three levels such as data fusion, feature fusion and decision level fusion and the fused data sets are used for fault classification using machine learning algorithm. The performance of each technique is studied in detail and compared using classification accuracy. A new method is proposed by combination of fusion techniques to enhance the performance

scholarly journals Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
M. Saimurugan ◽  
R. Nithesh
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Cost Effective ◽  
Rotating Machinery ◽  
Decision Tree Algorithm ◽  
Vibration Signals ◽  
Rotating Machine ◽  
Machine Elements ◽  
Diagnosis Model

The failure of rotating machine elements causes unnecessary downtime of the machine. Fault in the rotating machinery can be identified from noises, vibration signals obtained from sensors. Bearing and shaft are the most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques for diagnosis of machine elements. Fault diagnosis from sound signals is cost effective than vibration signals. Sound signal analysis is not well explored in the field of automated fault diagnosis. Under various simulated fault conditions, the sound signals are obtained by placing microphone near the bearing for different speeds. The features are extracted by using statistical and histogram methods. The best features of sound signals are obtained by decision tree algorithm. The extracted features are used as inputs to the classifier-Artificial Neural Network. The classification accuracy results from statistical and histogram features are obtained and compared.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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