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Fault detection and diagnosis by artificial intelligence

Machine learning techniques for fault detection and diagnosis

See also: Machine learning

In fault detection and diagnosis, mathematical classification models which in fact belong to supervised learning methods are trained on the training set of a labeled dataset to accurately identify the redundancies, faults and anomalous samples. During the past decades there are quite different classification and preprocessing models developed and proposed in this research area.[1] k-nearest-neighbors algorithm(kNN) is one of the oldest techniques which have been used to solve fault detection and diagnosis problems.[2] Despite the simple logic that this instance-based algorithm has, there are some problems with large dimensionality and processing time when it is used on large datasets.[3] Since kNN is not able to automatically extract the features to overcome curse of dimensionality, so often some data preprocessing techniques like Principal component analysis(PCA), Linear discriminant analysis(LDA) or Canonical correlation analysis(CCA) accompany it to reach a better performance.[4] In many industrial cases the effectiveness of kNN has been compared with other methods, specially with more complex classification models such as Support Vector Machines(SVMs), which is widely used in this field. Thanks to their appropriate nonlinear mapping using kernel methods, SVMs have an impressive performance in generalization, even with small training data.[5] However, general SVMs do not have automatic feature extraction themselves and just like kNN, are often coupled with a data pre-processing technique.[6] Another drawback of SVMs is that their performance is highly sensitive to the initial parameters, particularly to the kernel methods[7], so in each signal dataset a parameter tuning process is required to be conducted firstly. Therefore, the low speed of the training is a limitation of SVMs to be used in some fault detection and diagnosis cases.[8]

Artificial Neural Networks(ANNs) are among the most mature and widely used mathematical classification algorithms in fault detection and diagnosis. ANNs are well-known for their efficient self-learning capabilities of the complex relations (which generally exists inherently in fault detection and diagnosis problems) and are easy to operate.[6] Another advantage of ANNs is that they perform automatic feature extraction by allocating negligible weights to the irrelevant features, helping the system to avoid dealing with another feature extractor.[9] However, ANNs tend to over-fits the training set, which will have consequences of having poor validation accuracy on validation set. Hence, often, some regularization terms and prior knowledge are added to the ANN model to avoid over-fiting and achieve higher performance. Moreover, properly determining the size of the hidden layer needs an exhaustive parameter tuning, to avoid poor approximation and generalization capabilities.[8] In general, different SVMs and ANNs models (i.e. Back-Propagation Neural Networks and Multi-Layer Perceptron) have shown successful performances in the fault detection and diagnosis in industries such as gearbox[10], machinery parts (i.e. mechanical bearings[11]), compressors[12], wind and gas turbines[13][14] and steel plates[15] fault detection.

Deep learning techniques for fault detection and diagnosis

See also: Deep learning

Recently, by research advances in ANNs and advent of deep learning algorithms, using deep and complex layers, novel classification models are developed to cope with signal processing problems in FDI.[16]

Time domain waveform(top) and CWTS(bottom) of a normal signal

Most of the shallow learning models extract a few feature values from signals, causing a dimensionality reduction from the original signal. While, by using a Convolutional neural network, the continuous wavelet transform scalogram can be directly classified to normal and faulty classes. Such a technique avoids omitting any important fault message and results in a better performance of FDI.[17] Also by transforming signals to image constructions a 2D Convolutional neural network can be implemented for FDI.[18] Deep belief networks[19], Restricted Boltzmann machines[20] and Autoencoders[21] are other deep neural networks architectures which are successfully used in signal based FDI. Comparing to traditional machine learning, due to the deep architecture, deep learning models are able to learn more complex structures from datasets, however they need larger samples and longer processing time to achieve higher accuracy.

Image Processing based FDI

In the past few years many

  1. ^ Chen, Kunjin; Huang, Caowei; He, Jinliang (1 April 2016). "Fault detection, classification and location for transmission lines and distribution systems: a review on the methods". High Voltage. 1 (1): 25–33. doi:10.1049/hve.2016.0005.
  2. ^ Verdier, Ghislain; Ferreira, Ariane (February 2011). "Adaptive Mahalanobis Distance and $k$-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing". IEEE Transactions on Semiconductor Manufacturing. 24 (1): 59–68. doi:10.1109/TSM.2010.2065531.
  3. ^ Tian, Jing; Morillo, Carlos; Azarian, Michael H.; Pecht, Michael (March 2016). "Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis". IEEE Transactions on Industrial Electronics. 63 (3): 1793–1803. doi:10.1109/TIE.2015.2509913.
  4. ^ Safizadeh, M.S.; Latifi, S.K. (July 2014). "Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell". Information Fusion. 18: 1–8. doi:10.1016/j.inffus.2013.10.002.
  5. ^ Liu, Jie; Zio, Enrico (December 2016). "Feature vector regression with efficient hyperparameters tuning and geometric interpretation". Neurocomputing. 218: 411–422. doi:10.1016/j.neucom.2016.08.093.
  6. ^ a b Liu, Ruonan; Yang, Boyuan; Zio, Enrico; Chen, Xuefeng (August 2018). "Artificial intelligence for fault diagnosis of rotating machinery: A review". Mechanical Systems and Signal Processing. 108: 33–47. doi:10.1016/j.ymssp.2018.02.016.
  7. ^ Genton, Marc G. (2001). "Classes of Kernels for Machine Learning: A Statistics Perspective". Journal of machine learning research. 2: 299–312. doi:10.1162/15324430260185646.
  8. ^ a b Kotsiantis, S.B.; Zaharakis, I.D.; Pintelas, P.E. (2006). "Machine learning: a review of classification and combining techniques". Artificial Intelligence Review. 26 (3): 159–190. doi:10.1007/s10462-007-9052-3.
  9. ^ Vercellis, Carlo (2008). Business intelligence : data mining and optimization for decision making ([Online-Ausg.]. ed.). Hoboken, N.J.: Wiley. p. 436. ISBN 978-0-470-51138-1.
  10. ^ Saravanan, N.; Siddabattuni, V.N.S. Kumar; Ramachandran, K.I. (January 2010). "Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)". Applied Soft Computing. 10 (1): 344–360. doi:10.1016/j.asoc.2009.08.006.
  11. ^ Hui, Kar Hoou; Ooi, Ching Sheng; Lim, Meng Hee; Leong, Mohd Salman (15 November 2016). "A hybrid artificial neural network with Dempster-Shafer theory for automated bearing fault diagnosis". Journal of Vibroengineering. 18 (7): 4409–4418. doi:10.21595/jve.2016.17024.
  12. ^ Qi, Guanqiu; Zhu, Zhiqin; Erqinhu, Ke; Chen, Yinong; Chai, Yi; Sun, Jian (January 2018). "Fault-diagnosis for reciprocating compressors using big data and machine learning". Simulation Modelling Practice and Theory. 80: 104–127. doi:10.1016/j.simpat.2017.10.005.
  13. ^ Santos, Pedro; Villa, Luisa; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús (9 March 2015). "An SVM-Based Solution for Fault Detection in Wind Turbines". Sensors. 15 (3): 5627–5648. doi:10.3390/s150305627.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  14. ^ Wong, Pak Kin; Yang, Zhixin; Vong, Chi Man; Zhong, Jianhua (March 2014). "Real-time fault diagnosis for gas turbine generator systems using extreme learning machine". Neurocomputing. 128: 249–257. doi:10.1016/j.neucom.2013.03.059.
  15. ^ Tian, Yang; Fu, Mengyu; Wu, Fang (March 2015). "Steel plates fault diagnosis on the basis of support vector machines". Neurocomputing. 151: 296–303. doi:10.1016/j.neucom.2014.09.036.
  16. ^ Lv, Feiya; Wen, Chenglin; Bao, Zejing; Liu, Meiqin (July 2016). "Fault diagnosis based on deep learning". 2016 American Control Conference (ACC): 6851–6856. doi:10.1109/ACC.2016.7526751.
  17. ^ Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen (4 May 2018). "A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network". Sensors. 18 (5): 1429. doi:10.3390/s18051429.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  18. ^ Hoang, Duy-Tang; Kang, Hee-Jun (March 2018). "Rolling element bearing fault diagnosis using convolutional neural network and vibration image". Cognitive Systems Research. doi:10.1016/j.cogsys.2018.03.002.
  19. ^ Lei, Yaguo; Jia, Feng; Lin, Jing; Xing, Saibo; Ding, Steven X. (May 2016). "An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data". IEEE Transactions on Industrial Electronics. 63 (5): 3137–3147. doi:10.1109/TIE.2016.2519325.
  20. ^ Shao, Haidong; Jiang, Hongkai; Zhang, Xun; Niu, Maogui (1 November 2015). "Rolling bearing fault diagnosis using an optimization deep belief network". Measurement Science and Technology. 26 (11): 115002. doi:10.1088/0957-0233/26/11/115002.
  21. ^ Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na (May 2016). "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data". Mechanical Systems and Signal Processing. 72–73: 303–315. doi:10.1016/j.ymssp.2015.10.025.
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