Research on mechanical rotor condition monitoring based on VCNN

Available online at www.sciencedirect.com

ScienceDirect

Available online at www.sciencedirect.com Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000

ScienceDirect ScienceDirect ScienceDirect

Energy Procedia 00 (2018) 000–000 Energy Procedia 158 Energy Procedia 00(2019) (2017)6393–6398 000–000

www.elsevier.com/locate/procedia

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, Research onThe mechanical rotor condition monitoring based 15th International Symposium Heating and Coolingon VCNN China on District

Xiaoxun*, Zhao Jianhong, Hou Dongnan, Zhonghe Assessing the feasibility of using the heatHan demand-outdoor Research onZhumechanical rotor condition monitoring based on VCNN Department of Power Engineering, North China Electric Power University (Baoding), China temperature function for a long-term district heat demand forecast Abstract a

Zhu Xiaoxun*, Zhao Jianhong, Hou Dongnan, Han Zhonghe a,b,c a a b c I. AndrićDepartment *, A.ofPina , P. Ferrão J. Fournier ., University B. Lacarrière , O. Le Correc Power Engineering, North, China Electric Power (Baoding), China

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b

Veolia Recherche Innovation, 291 Avenue Dreyfous Daniel, Limay, A vector convolutional neural network based&on convolutional neural network was 78520 proposed toFrance realize the recognition of onec Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France dimensional vectors. Aiming at the lack of deep learning and subjectivity in rotor condition monitoring feature extraction, a Abstract feature extraction method for rotor condition monitoring based on vector convolutional neural network was proposed. The method directly input the network vibrationbased signaloninto the network, extract features thetoconvolution layer and the subA vectorcan convolutional neural convolutional neural network wasthrough proposed realize the recognition of onesampling layer, and finally realize the vibration signal identification through the output layer of the network. This method avoids dimensional vectors. Aiming at the lack of deep learning and subjectivity in rotor condition monitoring feature extraction, a Abstract the subjectivity of feature the depth of the vibration signal is now learned. Theneural feasibility of thewas method is verified feature extraction methodextraction, for rotor and condition monitoring based on vector convolutional network proposed. The by experimental research the vibration method. signal method can directly inputonthe into the network, extract features the convolution and the subDistrict heating networks are commonly addressed in the literature as one of thethrough most effective solutionslayer for decreasing the sampling layer, and finally realize the vibration signal identification through the output layer of the network. This method avoids greenhouse gas emissions frombytheElsevier building sector. These systems require high investments which are returned through the heat © 2019 The Authors. Published Ltd. Keywords: Vectorof convolutional Neural network, Deep learning, Vbration signal, Feature extraction; the subjectivity extraction, and theCondition depthand of monitoring, the vibration signal is now learned. feasibility of the method verified sales. Due to access thefeature changed climate building renovation policies, heatThe demand in the future couldis decrease, This is an open article under theconditions CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) by experimental research on the method. prolonging under the investment returnofperiod. Peer-review responsibility the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy.

The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand

Keywords: Vector convolutional Neural network, Condition monitoring, Deep learning, Vbration signal, Feature extraction; 1. Introduction forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665

buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district At present, most were of the rotor condition basedtheonerror, the obtained "feature heat extraction machine renovation scenarios developed (shallow, monitoring intermediate, methods deep). To are estimate demand+values were 1. Introduction recognition" mode, is,a using signal processing andpreviously other methods to extract the characteristics compared with resultsthat from dynamic heat demand model, developed and validated by the authors. of the vibration signal, as the characteristics of the machine learning input, based on the diagnostic model to identify theapplications vibration The results showed that when only weather change is considered, the margin of error could be acceptable for some Aterror present, most of thework rotor condition monitoring methods based on the "feature extraction + renovation machine (the in annual demand was lower than around 20% forthese all weather scenarios considered). However, after introducing state. The current research revolves two parts. Inare terms of signal processing, wavelet, HHT, HVD recognition" mode, that is, using signal processing and other methods to extract the characteristics of the vibration scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). and other methods [1-4] are the most commonly used signal processing methods, which can extract signal The value ofinslope coefficient on average within the display range of 3.8% up to 8% per decade, that corresponds to the signal, as the characteristics ofincreased the learning input, based on the features diagnostic model to identify theespecially vibration information the time domain andmachine frequency domain, and fault more comprehensively, decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and state. The current research work revolves around these two parts. In terms of signal processing, wavelet, HHT, HVD dealing with nonlinear and non-linear Smooth signal. renovation scenarios considered). On the other hand, function intercept increased for methods, 7.8-12.7% per decade (depending on the and other methods [1-4] are the most commonly used signal processing which can extract signal The improvements in these methods have also become the focus of current research. In the diagnostic model, coupled scenarios). The values suggested could bedomain, used to modify the function parameters for the scenarios considered, and information in the time domain frequency fault features moreusually comprehensively, especially support vector machine, neural and network, expert system and and display other methods [5-6] are used to establish fault improve the accuracy of heat demand estimations. dealing with nonlinear and non-linear Smooth signal.

diagnosis by establishing a diagnostic model for various samples. In this century, deep learning as a new field of The improvements in these methods have also become the focus of current research. In the diagnostic model, © 2017 The Authors. Published by Elsevier Ltd. support vector machine, neural network, expert system and other methods [5-6] are usually used to establish fault Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and diagnosis Cooling. by establishing a diagnostic model for various samples. In this century, deep learning as a new field of * Corresponding author. Tel.:13303128923. Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected] 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. * Corresponding author. Tel.:13303128923. Selection peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). E-mailand address: [email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 Copyright © 2018 Elsevier All rights reserved.of The 15th International Symposium on District Heating and Cooling. Peer-review under responsibility of theLtd. Scientific Committee 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.208

Zhu Xiaoxun et al. / Energy Procedia 158 (2019) 6393–6398 Xiaoxun Zhu/ Energy Procedia 00 (2018) 000–000

6394 2

machine learning has become a research hotspot, mainly including deep belief network (DBN), convolutional neural network (CNN), restricted Boltzmann machine (RBM). As a new machine learning method, deep learning uses multi-layer nonlinear information processing to extract and represent feature information, which can ensure the depth of learning and improve the adaptive ability of feature extraction. Therefore, it is widely used in the fields of image recognition, face recognition, speech recognition, etc. [7-9]. Among them, CNN uses technologies such as local sensing domain and sharing right in the identification process to ensure the accuracy of recognition and reduce the computational complexity of the whole network. Therefore, it has gained more and more attention and application in the field of pattern recognition. Through the above analysis, the paper improves the method on the basis of CNN, and obtains the vector convolutional neural network (VCNN) method. The method uses the one-dimensional vibration signal directly as input to the VCNN model to identify the vibration signal. The method can ensure the integrity of information, and has the advantages of high diagnostic precision and strong intelligence. 2. Vector convolutional neural network 2.1. vector convolutional neural network VCNN is a multi-layer neural network, each layer consists of multiple one-dimensional vector and each vector by a number of independent neurons, its structure is shown in Figure 1.

Input layer

Convolutional layer-C1

Pooling layer-S2

Convolutiona l layer-C2

Pooling layer-S4

Full connection Output layer layer

Fig. 1. VCNN network structure diagram

（1）Input layer. The vibration signal as a one-dimensional vector input network, and then the feature of the vibration signal can be learned and extracted by the network. （2）Convolutional layer(The C layer, also known as the feature extraction layer). It reduces the complexity of the network model and greatly improves the efficiency of network learning. （3）Pooling layer (S layer, also called the feature mapping layer). After the convolutional operation, the data features will be scaling invariance by the pooling sampling layer to extract data feature. （4）Full connection layer. The data characteristics will be summarized as the input to the full connection layer after all convolved and pooling processes. （5）Output layer. The feature vector extracted from the VCNN model will be classified and identified, and the classification result of the output model is obtained in this layer. According to the problem, we need to choose the appropriate classification method, the commonly used classification methods are polynomial logistic regression, Soft-max classifier, support vector machine[10].



Zhu Xiaoxun et al. / Energy Procedia 158 (2019) 6393–6398 Author name / Energy Procedia 00 (2018) 000–000

6395 3

2.2. The learning and training of VCNN network Referring to the CNN algorithm, VCNN uses the back propagation algorithm (BP) to implement the training of network parameters[11-12]. VCNN main optimization parameters include the convolutional kernel parameters (W) of the C layer, the weight coefficient (β) of S layer, the weight coefficient (ω) of the full connection layer, the bias value (b) of each layer and so on. E is the sensitivity of each layer, which can be calculated by calculating the difference between the actual network output (O=[o1, o2, …, oc])and the expected network output (Y=[y1, y2, …, yc]). E ( w,  , k , b) 

1 1 c (Y  O) 2   ( y k  ok ) 2 2 2 k 1

 lj  '(u lj ) (olj  ylj )   l  j [ lj 1  wlj1 ] g '(u lj )   l l 1 l l 1  j   j [ f '(u j ) up( j )]

(1)

Output layer Pooling layer Convolutional layer

(2) Where 1n1 is vector with the elements of 1, up(x)=x1n1,  represents Kronecker plot, represents the vector l+1 corresponding elements multiplied, w j is the weight coefficient from l layer to l+1 layer. The weights and bias of the convolutional layer, the pooling layer, and the output layerare calculated according to equations respectively:  E N l l 1  k    j  h j  l 1  N  E   l   b l 1 j (3)

 E N l l 1      j  h j  l 1  N  E   l   b l 1 j

(4)  E l 1 l T  w  x ( )  l  E  E u   l l  b u b (5) According to equation , the parameters of the network will be updated to make the actual output value of the network closer to the ideal output value by updating the network parameters. E  k  k  k  k   k           E      w  w  w  w   E  w   E b  b  b  b    b

(6)

3. Vibration Fault Diagnosis Based on VCNN For the vibration signal, which contains the most original and comprehensive fault information, these fault information will be in the form of images contained in the vibration waveform, VCNN can simulate the neural system with rich hierarchical structure, and establish a hierarchical model structure similar to the human brain. Through the local perception technology, the vibration signal can be accurately identified. Therefore, this paper presents a vibration signal fault diagnosis method based on VCNN. This method does not require artificial feature

Zhu Xiaoxun et al. / Energy Procedia 158 (2019) 6393–6398 Xiaoxun Zhu/ Energy Procedia 00 (2018) 000–000

6396 4

extraction process, through the direct vibration signal adaptive feature extraction and deep learning training, to achieve vibration fault identification and diagnosis, The specific training and diagnostic model of the method model are as shown in Fig 2. Real-time gather the fault vibration

Train date input C1

Input S2 layer of

Input C3 layer of

data

layer of CNN

CNN

CNN

Input output

Input full

Input S3 layer of

Parameter

Calculate the bias between the target value and the actual

connection layer

CNN

optimization

value (e)

layer

Set the learning rate (η) and the number of training (n)

Weight learning

Test

Calculate the error

Calculate the error

gradient

of the hidden layer

Diagnostic

Fault diagnosis

samples

model

Y N

e do not meet requirements and i
Fig. 2. Method Schematic

3.1. Experimental study of the method The experiment is based on the Bently-RK4 rotor vibration test bed. Obtain the steam turbine rotor rubbing, imbalance, misalignment and whirl faults of the 120 sets(1024 vibration data for a set) of which the 80 sets were training samples, and the 40 sets were the test samples. In the VCNN network structure, the VCNN selected in this paper ontains three convolutional layers (C1, C3, C5) and three pooling layers (S2, S4, S6).In the case of weighing the diagnostic accuracy and the operating rate, we take the 5-5-6 model as the convolutional model of the three convolutional layers in this paper. At the same time, select n1=6，n2=12，n3=24 as the number of convolution kernels. The pooling layers(S2, S4, S6) is averaged which size is 2×1. Activation function using Relu function. In terms of VCNN network parameters, the learning rate, training frequency and training dimension in VCNN network are optimized by grid search. The optimal parameters are obtained by calculation, as shown in Table 1. Table 1.VCNN network parameters Learning rate

Training times

Training size

0.001

5

5

Based on the model, 10 sets of test samples were taken for the diagnosis of the four kinds of faults. The diagnostic accuracy was shown in Table 2. Table 2.Fault diagnosis under each faultFault

Test samples /set

category

Error sample

Accuracy

/set

rubbing

10

1

90%

imbalance

10

0

100%

misalignment

10

1

90%

whirl

10

0

100%



Zhu Xiaoxun et al. / Energy Procedia 158 (2019) 6393–6398 Author name / Energy Procedia 00 (2018) 000–000

6397 5

According to the experimental study, the diagnostic accuracy of the model can reach 95%, which indicates that the diagnostic system proposed in this paper has high reliability. At the same time, in order to compare the performance advantages of this method relative to other methods, this paper selects three other methods as the contrast method. Contrast method 1:the traditional diagnostic method based on SVM which takes spectral characteristics of the vibration signal as the model input. Contrast method 2:deep learning diagnosis method based on DBN. Contrast method 3:deep learning diagnosis method based on RBN. The diagnostic results of this method and contrast method are compared between training time and diagnostic accuracy, as shown in Table 3. Table 3.Comparison between SVM and Deep Learning Fault diagnosis method SVM DBN RBM VCNN

Accuracy

Training time /s

85% 83% 80% 95%

99.72 94.03 97.11 90.53

The feasibility of rotor vibration signal fault diagnosis model based on VCNN is verified by the experiment. At the same time, it can be seen that the VCNN fault diagnosis model has improved the training time compared with the traditional diagnosis method and other deep learning methods. 4. Conclusion In this paper, the VCNN is applied to the condition monitoring of rotor. The method uses the vibration signal of the original mechanical rotor as input directly, and the fault is diagnosed by network which adaptively extract feature. Through the relevant theoretical and experimental research, the following conclusions are obtained: (1) The improved VCNN model can directly input the most original vibration signal vector into the model, which avoids the loss of signal information in the signal processing process and improves the diagnostic accuracy. (2) The vibration signal is used as the input of the VCNN model without the need of artificial feature selection, which avoids the subjectivity of feature extraction. Compared with the traditional feature extraction fault diagnosis, the diagnosis precision is improved. (3) Because VCNN can extract the implicit data feature automatically, and its weight sharing technology is adopted in the network, the computational complexity of the whole network is reduced. Therefore, VCNN has higher diagnostic accuracy and less computational complexity than SVM, DBN, RBM and other machine learning methods. Acknowledgements This paper was supported by‘the Fundamental Research Funds for the Central Universities（2018MS111）’. References [1] MAIIAT S,W L HWANG．Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory,1992. 38(2): 617-643． [2] YAN Daoru, LI Xiaobo, GU Yijiong. The current situation of the development of technology and application of fault diagnosis of steam turbine[J].China Electric Power Education, 2005(S1): 239-240+270. [3] ZHU Xiaoxun, XU Bochao, JIAO Hongchao, HAN Zhonghe. Research on Vibration Fault Diagnosis Based on HVD[J]. Turbine Technology, 2005(S1): 239-240+270. [4] XIANG Ling, ZHU Yongli, TANG Guiji. Application of Hilbert-Huang Transform Method in Vibration Faults Diagnosis for Rotor System[J]. Proceedings of the CSEE, 2007,(35): 84-89. [5] ZHU Xiaoxun, HAN Zhonghe. Research on LS-SVM Wind Speed Prediction Method Based on PSO[J]. Proceedings of the CSEE,2016,(23):6337-6342+6598. [6] LIN Sheng, HE Zhengyou, ZANG Tianlei, QIAN Qingquan. Novel Approach of Fault Type Classification in Transmission Lines Based on Rough Membership Neural Networks[J]. Proceedings of the CSEE,2010,(28):72-79. [7] FENG Xiaoxia. Research on image recognition based on Deep Learning Algorithm[D]. Taiyuan University of Technology, 2015.

6398 6

Zhu Xiaoxun et al. / Energy Procedia 158 (2019) 6393–6398 Xiaoxun Zhu/ Energy Procedia 00 (2018) 000–000

[8] ZHANG Hui, SU Hong, ZHANG Xue liang. Convolutional Neural Network for Robust Pitch Determination[J]. ACTA AUTOMATICA SINICA, 2016, 42(06): 959-964. [9] GU Jialing, PENG Hongjing. Incremental convolution neural network and its application in face detection[J]. Journal of System Simulation, 2009, 21(8):2441-2445. [10] YU Ping, ZHAO Jisheng. Image classification method based on linear Superposition features and Convolutional Neural Networks[J]. Microelectronics and Compute, 2015, 32(10): 36-40. [11] LIU Jianwei, LIU Yuan, LUO Xionglin. Research and development on deep learning[J]. Application research of computers, 2014, 31(07): 1921-1930+1942. [12] SUN Zhijun, XUE lei, XU Yangming, et al. Overview of deep learning[J]. Application Research of Computers, 2012.