System Design for Early Fault Diagnosis of Machines using Vibration Features

The faults diagnosis of machines consists of feature extraction and classification of faults. The fault diagnosis process is based on the fact that each fault in a machine has a unique vibrational feature. Every industry wants a compact device or embedded system of low cost for the early faults diagnosis of machinery. This paper presents the platform that is focused at the embedded system design for the early faults diagnosis of machine(s) and classification of faults. We performed our experiment on the test rig apparatus and collected the vibration signals of four states of machine, those were: normal state, cracking state, offset pulley state and wear state. In segmentation, we use the empirical mode decomposition (EMD) technique. For classification purpose, we are using k-nearest neighbors (K-NN). Achievement of this research is that embedded system design for the classification of different faults in the machines. The overall accuracy of our experiment is 91.5%.

[1]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[2]  Xianzhi Wang,et al.  Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine , 2018, Journal of Sound and Vibration.

[3]  Alessandro Goedtel,et al.  A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors , 2015 .

[4]  Ying Liang,et al.  A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis , 2019 .

[5]  Yongwha Chung,et al.  Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis , 2016, Sensors.

[6]  Sirish L. Shah,et al.  Fault detection and diagnosis in process data using one-class support vector machines , 2009 .

[7]  Walmir M. Caminhas,et al.  SVM practical industrial application for mechanical faults diagnostic , 2011, Expert Syst. Appl..

[8]  Jialin Li,et al.  A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM , 2019, Sensors.

[9]  V. Rao Vemuri,et al.  Use of K-Nearest Neighbor classifier for intrusion detection , 2002, Comput. Secur..

[10]  Tommy W. S. Chow,et al.  Induction machine fault detection using SOM-based RBF neural networks , 2004, IEEE Transactions on Industrial Electronics.

[11]  Ali Soleimani,et al.  Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets , 2015 .

[12]  Ming J. Zuo,et al.  A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditions , 2017 .

[13]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[14]  Chun-Chieh Wang,et al.  Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine , 2013, Entropy.

[15]  L. A. Morales-Hernandez,et al.  Thermographic technique as a complement for MCSA in induction motor fault detection , 2014, 2014 International Conference on Electrical Machines (ICEM).

[16]  Adam Glowacz,et al.  Acoustic based fault diagnosis of three-phase induction motor , 2018, Applied Acoustics.

[17]  Adam Glowacz,et al.  Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals , 2018 .

[18]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[19]  Jay Lee,et al.  Analysis of machine degradation using a neural network based pattern discrimination model , 1993 .

[20]  Yuyun Zeng,et al.  Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model , 2019, Renewable Energy.

[21]  Jérôme Antoni,et al.  Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions , 2012 .

[22]  Yaguo Lei,et al.  Application of an intelligent classification method to mechanical fault diagnosis , 2009, Expert Syst. Appl..

[23]  M. Senthil Kumaran,et al.  Nominal features-based class specific learning model for fault diagnosis in industrial applications , 2018, Comput. Ind. Eng..

[24]  Insoo Koo,et al.  Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features , 2017, IEEE Access.

[25]  Demba Diallo,et al.  Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults , 2019, Energies.

[26]  Nagarajan Murali,et al.  Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference , 2013, IEEE Transactions on Industrial Electronics.

[27]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[28]  Shibin Wang,et al.  Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis , 2016 .

[29]  Mehmet Karakose,et al.  A new method for early fault detection and diagnosis of broken rotor bars , 2011 .

[30]  Adam Glowacz,et al.  Diagnosis of stator faults of the single-phase induction motor using acoustic signals , 2017 .

[31]  Nguyen Phong Dien,et al.  Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps , 2004 .

[32]  Bin Li,et al.  Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification , 2019, IEEE Transactions on Industrial Electronics.

[33]  Tshilidzi Marwala,et al.  EARLY CLASSIFICATIONS OF BEARING FAULTS USING HIDDEN MARKOV MODELS, GAUSSIAN MIXTURE MODELS, MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND FRACTALS , 2006 .

[34]  Kjell G. Robbersmyr,et al.  Early detection and classification of bearing faults using support vector machine algorithm , 2017, 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD).

[35]  Rene de Jesus Romero-Troncoso,et al.  Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors , 2014, TheScientificWorldJournal.