Prakash Mohan,Manikandan Sundaram,



Principal Component Analysis,Support Vector Machine,Fault Prognosis,Air Compressor,


Machine Fault Diagnosis is an important domain in Mechanical Engineering which concerns about finding fault in the machine parts. Among many techniques to identify and classify the faults, this paper concerns about using machine learning algorithms to distinguish healthy machines fro mtheun healthy machines. Inordertodistinguishthestateofamachine,classificationalgorithmshas to beused.The accuracy of an algorithm depends upon the pattern, that the data set follows. The suitability of the five most commonly used classification algorithm has been discussed. Various transforms can be applied to such sensor data. Here various algorithms have been tested for wave let packet transform. Thea ccuracy of the fit has been measured for all the five algorithms. Hyper-parametertuning has been done to make the fitbetter.


I. Ali J, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015)
Application of empirical mode decomposition and artificial neural
network for automatic bearing fault diagnosis based on vibration signals.
Applied Acoustics89:16– 27.
II. II. C S, B D, S, Manivannan K (2014) Bearing fault diagnosis using
wavelet packet transform, hybrid PSO and support vector machine, vol
III. C W, Y R, M, Cheng Y (2016) Fault diagnosis for rotating machinery: A
method based on image processing. PLoS. ONE 11(10):1–22
IV. DH, MSC, Song G, RenL,LiH (2015)A review of damage detection methods
V. Desmet A, et al. (2017) Leak detection in compressed air systems using
unsupervised anomaly detection techniques pp 1–10
VI. Devendiran S(2016)Vibration Based Condition Monitoring and Fault Diagnosis Technologies
for Bearing and Gear Components AReview11(6):3966–3975
VII. F D, S S, F, Pecht M (2017) Current Noise Cancellation for Bearing Fault
VIII. FM. Arkkio, A. Roivainen J(2014) Electrical Fault Diagnosis for an
Induction Motor Using an Electromechanical FEModel
IX. Fengtao, Song L, Zhang L, Li H (2011) Fault Diagnosis for
Reciprocating Air Compressor Valve Using P-V Indicator Diagram and
X. G Y, T Z, T, Cao L (2018) A multiscale noise tuning stochastic
resonance for fault diagnosis in rolling element bearings. Chinese
Journal of Physics 56(1):145–157
XI. H S, Ben S, Bacha K, Zeadally S (2015) Smart wireless sensor networks
for online faults diagnosis in an induction machine. Computers and
Electrical Engineering 41:226–239
XII. H Y, Lee WS, Wu CY (2014) Automated fault classification of
reciprocating compressors from vibration data: A case study on
optimization using a genetic algorithm
XIII. HeM, HeD, Bechhoefer E(2016) Using Deep Learning Based Approaches for
Bearing Fault Diagnosis with AE Sensors
XIV. JL,MW, K,SunL (2015a) Mechanical Fault Diagnosis for HV Circuit Breakers Based on
Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine.
XV. J NV, D, Kim JM (2015b) Accelerating 2d fault diagnosis of an induction
and Ubiquitous Engineering 10(1),
XVI. K Z, C X, Fang JQ, Zheng PF, Wang J (2017) Fault Feature
Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs
Network. International Journal of Rotating Machinery 2017, URL 10.1155/2017/9602650
XVII. Kavathekar S, Upadhyay N, Kankar PK (2016) Fault Classification of
Ball Bearing by Rotation Forest Technique. Procedia Technology

XVIII. M,BeloiuR(2014) Faultsdiagnosisforelectricalmachinesbasedonanalysis
of motorcurrent
XIX. M, Ushakumari S (2011) Incipient fault detection and diagnosis of
induction motor using fuzzy logic, vol 2013
XX. M A, M R, M, Ehtiwesh I (2010) A combined practical approach to
condition monitoring of reciprocating compressors using IAS and
dynamicpressure. World Academy of Science, Engineering and
XXI. Omid M (2016) An intelligent approach
XXII. Prakash A (2014) A review on machine condition monitoring and fault
diagnostics using wavelet transform
XXIII. Q . A S, L S, G, Shao L (2015) Vibration sensor based intelligent fault
diagnosis system for large machine units in petrochemical industries.
XXIV. R,Sugumaran V (2015) Fault diagnosis of automobile hydraulic brake system
using statistical features and support vector machines, vol 52
XXV. R D, S S, K R, Verma NK, Salour A (2016) Generating feature sets for
fault diagnosis using denoising stacked auto-encoder.
XXVI. RP,S,Jennions IK (2013) Rotor dynamic faults: Recent advances in diagnosis
and prognosis. International Journal of Rotating Machinery 2013,
XXVII. S,Zhou D (2016) Study on a New Fault Diagnosis Method Based on Combining
Intelligent. Technologies11(6):61–72
XXVIII. S E, H J, K, Shahzad T (2017a) Vibration Feature Extraction and
Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey.
Asia Pacific Journal of Multidisciplinary Research5(51):103–110
XXIX. S G, A PJ, Kulkarni JV (2015a) Fault Diagnosis of Bearing of Electric
Motor Using Wavelet Transformand Fault Classification Based on Support Vector.
XXX. S L, Z, Hu K (2017b) Traction inverter open switch fault diagnosis based
XXXI. S M, Tan ACC, Mathew J (2015b) A review of prognostic techniques for
non- stationary and non-linear rotating systems, vol 62
XXXII. Shaheryar A, Yin XC, Ramay WY (2017) Robust Feature Extraction on Vibration Data under Deep-Learning Framework:
An Application for Fault Identification in Rotary. Machines International Journal of Computer Applications 167(4):975–8887, URL-
XXXIII. T, Wu Z (2015) A vibration analysis based on the wavelet entropy method of a scroll compressor
XXXIV. T L, X, Tan ACC (2017a) Fault diagnosis of rolling element bearings
based on Multiscale Dynamic Time Warping. Measurement: Journal of the International Measurement Confederation,
XXXV. T S, M K, P, Ramachandran KI (2014) Fault diagnosis of automobile gearbox
XXXVI. T V, AlThobiani F, Tinga T, Ball A, Niu G (2017b) Single and combined
network. vol 0, URL
XXXVII. Verma NK, Sevakula RK, DixitS, Salour A (2016) Intelligent Condition Based
Monitoring Using Acoustic Signals for Air Compressors. IEEE Transactions on Reliability65(1):291–309
XXXVIII. XL,S,HuJ(2017)Improving Rolling Bearing Fault Diagnosis by DS Evidence
Theory Based FusionModel
XXXIX. Y,Al-khassaweneh M (2014) Fault Diagnosis inInternal Combustion Engines
Using Extension Neural Network. IEEE Transactions on Industry
XL. Y, Benjelloun K (2016) Sleeve Bearing Fault Diagnosis and
Classification Zhao R (2016) Deep Learning and Its Applications to
Machine Health Monitoring: A Survey. vol 14, pp 1–14

View Download