A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals

Machine fault diagnosis (MFD) has gained an important enthusiasm since the unfolding of the pattern recognition techniques in the last three decades. It refers to all of the studies that aim to automatically detect the faults on the machines using various kinds of signals that they can generate. The present work proposes a MFD system for the drilling machines that is based on the sounds they produce. The first key contribution of this paper is to present a system specifically designed for the drills, by attempting not only to detect the faulty drills but also to detect whether the sounds were generated during the active or the idling stage of the whole machinery system, in order to provide a complete remote control. The second key contribution of the work is to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. The created images, the so-called power spectrum density (PSD)-images, are then given to a deep convolutional autoencoder (DCAE) for a high-level feature extraction process. The final step of the scheme consists of adopting the proposed PSD-images + DCAE features as the final representation of the original sounds and utilize them as the inputs of a nonlinear classifier whose outputs will represent the final diagnosis decision. The results of the experiments demonstrate the high discrimination potential afforded by the proposed PSD-images + DCAE features. They were also tested on a noisy dataset and the results show their robustness against noises.

[1]  Torsten Söderström,et al.  Robust Fault Detection Using Index of Kullback Discrimination Information , 1996 .

[2]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[3]  Ling Wan,et al.  Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals , 2012, Sensors.

[4]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[5]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[6]  Myeongsu Kang,et al.  Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes , 2018, IEEE Transactions on Industrial Electronics.

[7]  Linjing Zhao,et al.  An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine , 2014 .

[8]  Marios M. Polycarpou,et al.  Learning methodology for failure detection and accommodation , 1995 .

[9]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  V. Sugumaran,et al.  Exploiting sound signals for fault diagnosis of bearings using decision tree , 2013 .

[11]  Hongmei Liu,et al.  Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals , 2016 .

[12]  Jong-Myon Kim,et al.  A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis , 2017, Sensors.

[13]  Iqbal Gondal,et al.  Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach , 2015, IEEE Transactions on Industrial Electronics.

[14]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[15]  Arturo Garcia-Perez,et al.  Reconfigurable Monitoring System for Time-Frequency Analysis on Industrial Equipment Through STFT and DWT , 2013, IEEE Transactions on Industrial Informatics.

[16]  K. I. Ramachandran,et al.  A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis , 2014, Int. J. Data Anal. Tech. Strateg..

[17]  Biswanath Samanta,et al.  Artificial neural networks and genetic algorithm for bearing fault detection , 2006, Soft Comput..

[18]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..

[19]  M. Miki,et al.  A Sound Monitoring System for Fault Detection of Machine and Machining States , 1986 .

[20]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[21]  C. Sujatha,et al.  Using neural networks for the diagnosis of localized defects in ball bearings , 1997 .

[22]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[24]  Wenguang Yang,et al.  Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine , 2017, J. Sensors.

[25]  Mehmet Fidan,et al.  Sound based induction motor fault diagnosis using Kohonen self-organizing map , 2014 .

[26]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[27]  Jong-Myon Kim,et al.  Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings , 2017 .

[28]  Shuilong He,et al.  Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery , 2017 .

[29]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Francesco Ferracuti,et al.  Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines , 2018, IEEE Transactions on Industrial Electronics.

[31]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[32]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Subhasis Nandi,et al.  Detection of Eccentricity Faults in Induction Machines Based on Nameplate Parameters , 2011, IEEE Transactions on Industrial Electronics.

[35]  Ruben Puche-Panadero,et al.  Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime , 2017, IEEE Transactions on Instrumentation and Measurement.

[36]  Jiaxu Wang,et al.  An integrated approach to planetary gearbox fault diagnosis using deep belief networks , 2017 .

[37]  Jing Lin,et al.  Feature extraction of machine sound using wavelet and its application in fault diagnosis , 2001 .

[38]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..