Abstract
Reciprocating compressors are one of the critical components in petrochemical and process industries. In order to minimize the downtime and reliability enhancement of the system, it is crucial to develop an intelligent fault detection approach that can identify system anomalies before it progresses into severe faults or leads to system break down. Existing studies suggest that signal processing and machine learning technique based combined fault detection models have produced effective results. However, quality of results produced by such approach heavily depends on the input features. Hence, it is imperative to build an effective fault detection model that intelligently overcomes the shortcomings of high dimensionality and efficiently predicts the impending faults. In order to do so, a binary Moth Flame Optimization (a swarm intelligence algorithm) based wrapper-type feature selection approach combined with K-nearest neighbours-based fault classification approach is proposed for fault detection in a reciprocating compressor. The proposed model produced superior results compared to contemporary feature selection techniques (such as, particle swarm optimization, grey wolf optimization, principal component analysis) combined with K-nearest neighbours. The proposed approach can intelligently eliminate redundant features and detect faults in reciprocating compressor at minimal false alarm rate (< 1%) and high detection rate (> 99%).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73:4773–4795. https://doi.org/10.1007/s11227-017-2046-2
Agarwal S, Ranjan P (2018) MR-TP-QFPSO: map reduce two phases quantum fuzzy PSO for feature selection. Int J Syst Assur Eng Manag 9:888–900. https://doi.org/10.1007/s13198-017-0682-9
Ahmadi H, Moosavian A, Khazaee M (2012) An appropriate approach for misalignment fault diagnosis based on feature selection and least square support vector machine. Int J Mech 6:97–104
Ahmed M, Baqqar M, Gu F, Ball AD (2012) Fault detection and diagnosis using principal component analysis of vibration data from a reciprocating compressor. In: UKACC international conference on control, pp 3–5
Al-Tashi Q, Rais HM, Abdulkadir SJ, Mirjalili S (2020) Feature selection based on grey wolf optimizer for oil gas reservoir classification. In: 2020 international conference on computational intelligence ICCI 2020, pp 211–216. https://doi.org/10.1109/ICCI51257.2020.9247827
Aljarah I, Mafarja M, Heidari AA et al (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput J 71:964–979. https://doi.org/10.1016/j.asoc.2018.07.040
Aravinth S, Sugumaran V (2018) Air compressor fault diagnosis through statistical feature extraction and random forest classifier. 12:192–205
Bahoura M, Simard Y (2010) Blue whale calls classification using short-time Fourier and wavelet packet transforms and artificial neural network. Digit Signal Process A Rev J 20:1256–1263. https://doi.org/10.1016/j.dsp.2009.10.024
Banga M, Bansal A, Singh A (2020) Proposed approach to predict software faults detection using Entropy. Int J Syst Assur Eng Manag 11:301–312. https://doi.org/10.1007/s13198-019-00934-2
Banga A, Ahuja R, Sharma SC (2021) Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-020-01049-9
Cabrera D, Guaman A, Zhang S et al (2019) Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.11.006
Cannas LM (2013) A framework for feature selection in high-dimensional domains. https://scholar.google.co.in/scholar?q=A+FRAMEWORK+FOR+FEATURE+SELECTION+IN+HIGH-DIMENSIONAL+DOMAINS&hl=en&as_sdt=0&as_vis=1&oi=scholart#d=gs_cit&u=%2Fscholar%3Fq%3Dinfo%3AWaLjIUubOP8J%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den
Chen J, Li Z, Pan J et al (2016) Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 70–71:1–35. https://doi.org/10.1016/j.ymssp.2015.08.023
Chomphu W (2020) Wellhead compressor failure prediction using attention-based bidirectional LSTMs with data reduction techniques, pp 16–22
Cui H, Zhang L, Kang R, Lan X (2009) Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method. J Loss Prev Process Ind 22:864–867. https://doi.org/10.1016/j.jlp.2009.08.012
Forte LA, Garufi F, Milano L et al (2011) Blind source separation and Wigner-Ville transform as tools for the extraction of the gravitational wave signal. Phys Rev D - Part Fields Gravit Cosmol 83:1–12. https://doi.org/10.1103/PhysRevD.83.122006
Gan M, Wang C, Zhu C (2016) Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process 72–73:92–104. https://doi.org/10.1016/j.ymssp.2015.11.014
Guerra CJ, Kolodziej JR (2014) A data-driven approach for condition monitoring of reciprocating compressor valves. 136:1–13. https://doi.org/10.1115/1.4025944
He W, Zi Y, Chen B et al (2015) Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech Syst Signal Process 54:457–480. https://doi.org/10.1016/j.ymssp.2014.09.007
Hu P, Pan JS, Chu SC (2020) Improved Binary Grey Wolf Optimizer and Its application for feature selection. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2020.105746
Hyvärinen A, Ramkumar P, Parkkonen L, Hari R (2010) Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. Neuroimage 49:257–271. https://doi.org/10.1016/j.neuroimage.2009.08.028
Janssens O, Slavkovikj V, Vervisch B et al (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345. https://doi.org/10.1016/j.jsv.2016.05.027
Jia F, Lei Y, Lin J et al (2016) Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72–73:303–315. https://doi.org/10.1016/j.ymssp.2015.10.025
Khurma RA, Aljarah I, Sharieh A (2020) Rank based Moth flame optimisation for feature selection in the medical application. 2020 IEEE Congr Evol Comput CEC 2020 - Conf Proc. https://doi.org/10.1109/CEC48606.2020.9185498
Kumar A, Jaiswal A (2019) Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter. Multimed Tools Appl 78:29529–29553. https://doi.org/10.1007/s11042-019-7278-0
Lei J, Liu C, Jiang D (2019) Fault diagnosis of wind turbine based on Long Short-term memory networks. Renew Energy 133:422–432. https://doi.org/10.1016/j.renene.2018.10.031
Li X, Peng X (2019) A new non-destructive method for fault diagnosis of reciprocating compressor by measuring the piston rod strain A new non-destructive method for fault diagnosis of reciprocating compressor by measuring the piston rod strain. https://doi.org/10.1088/1757-899X/604/1/012055
Li Y, Wang J, Zhao H et al (2019) Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor. 639–653. https://doi.org/10.21595/jve.2018.20050
Lin YH, Wu HC, Wu CY (2006) Automated condition classification of a reciprocating compressor using time-frequency analysis and an artificial neural network. Smart Mater Struct 15:1576–1584. https://doi.org/10.1088/0964-1726/15/6/009
Liu Y, Wang J, Li Y et al (2017) Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve. 6007–6020. https://doi.org/10.21595/jve.2017.18726
Liu Y, Duan L, Yuan Z et al (2019) An intelligent fault diagnosis method. https://doi.org/10.3390/s19051041
Lu C, Wang S, Zhang C (2016) Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering. Proc Inst Mech Eng Part C J Mech Eng Sci 230:2913–2928. https://doi.org/10.1177/0954406215602285
Mafarja M, Aljarah I, Heidari AA et al (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Syst 161:185–204. https://doi.org/10.1016/j.knosys.2018.08.003
Majd AA, Samet H (2017) k-NN based fault detection and classification methods for power transmission systems. https://doi.org/10.1186/s41601-017-0063-z
Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
Mohammad L, Abualigah Q (2018) Feature Selection and Enhanced Krill
Moosavian A, Ahmadi H, Tabatabaeefar A, Khazaee M (2013) Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine. 20:263–272. https://doi.org/10.3233/SAV-2012-00742
Ong P, Hieng T, Tieh C et al (2019) Efficient gear fault feature selection based on moth – flame optimisation in discrete wavelet packet analysis domain. J Brazilian Soc Mech Sci Eng 41:1–14. https://doi.org/10.1007/s40430-019-1768-x
Pandya D, Solutions SG, Srivastava A et al (2018) OTC-28990-MS Increasing Production Efficiency via Compressor. Failure Predictive Analytics Using Machine Learning
Patri OP, Reyna N, Information C et al (2015) Predicting Compressor Valve Failures from Multi-Sensor Data. 1–11
Qasim OS, Mahmoud MS (2020) Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection. 5:1420–1428
Qin Q, Jiang Z, Feng K, He W (2012) A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine. 45:897–908. https://doi.org/10.1016/j.measurement.2012.02.005
Sayed GI, Darwish A, Hassanien AE (2020) Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses. J Classif 37:66–96. https://doi.org/10.1007/s00357-018-9297-3
Shie J, Da, Chen SM (2008) Feature subset selection based on fuzzy entropy measures for handling classification problems. Appl Intell 28:69–82. https://doi.org/10.1007/s10489-007-0042-6
Singh N (2019) ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing. 4:1239–1250
Sumathi T, Karthik S, Marikkannan M (2014) Artificial Bee Colony Optimization For Feature Selection in opinion Mining. Journal of Theoretical Applied Information Technology 66:368–379
Tang Y, Liu S (2013) 974. Time-frequency feature extraction from multiple impulse source signal of reciprocating compressor based on local frequency. 574–587
Tang B, Liu W, Song T (2010) Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution. Renew Energy 35:2862–2866. https://doi.org/10.1016/j.renene.2010.05.012
Tran VT, Althobiani F, Ball A (2014) An approach to fault diagnosis of reciprocating compressor valves using Teager – Kaiser energy operator and deep belief networks. Expert Syst Appl 41:4113–4122. https://doi.org/10.1016/j.eswa.2013.12.026
Tran VT, Althobiani F, Tinga T, Ball A (2017) Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network. 0:1–14. https://doi.org/10.1177/0954406217740929
Turabieh H, Mafarja M, Li X (2018) Iterated Feature Selection Algorithms with Layered Recurrent Neural Network for Software Fault Prediction Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. https://doi.org/10.1016/j.eswa.2018.12.033
Uniyal N, Studies E, Kumar A (2020) An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications Nitin Uniyal. https://doi.org/10.33889/IJMEMS.2020.5.4.058
Verma NK, Maini T, Salour A (2012) Acoustic Signature Based Intelligent Health Monitoring of Air Compressors with Selected Features. 2012 Ninth Int Conf Inf Technol - New Gener 839–845. https://doi.org/10.1109/ITNG.2012.67
Vijh S, Gaur D, Kumar S (2020) An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine. Int J Syst Assur Eng Manag 11:374–384. https://doi.org/10.1007/s13198-019-00866-x
Wang Z (2019) Fault diagnosis for reciprocating compressor based on GLCM and HOG features fusion of time-frequency image. https://doi.org/10.1109/SDPC.2019.00184
Wang WF, Qiu XH, Chen C, Sen et al (2018) Application Research on Long Short-Term Memory Network in Fault Diagnosis. Proc - Int Conf Mach Learn Cybern 2:360–365. https://doi.org/10.1109/ICMLC.2018.8527031
Wasnik PP (2019) Fault detection and classification based on semi- supervised machine learning using KNN, pp 79–83
Yang BS, Hwang WW, Kim DJ, Tan AC (2005) Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mech Syst Signal Process 19:371–390. https://doi.org/10.1016/j.ymssp.2004.06.002
Zhang L, Shan L, Wang J (2017) Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Comput Appl 28:2795–2808. https://doi.org/10.1007/s00521-016-2204-0
Zhao H, Wang J, Han H, Gao Y (2016) A feature extraction method based on HLMD and MFE for bearing clearance fault of reciprocating compressor. Meas J Int Meas Confed 89:34–43. https://doi.org/10.1016/j.measurement.2016.03.076
ZorarpacI E, Özel SA (2016) A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst Appl 62:91–103. https://doi.org/10.1016/j.eswa.2016.06.004
Funding
There has been no significant financial support for this work that could have influenced its outcome.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Informed consent
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.
We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patil, A., Soni, G. & Prakash, A. A BMFO-KNN based intelligent fault detection approach for reciprocating compressor. Int J Syst Assur Eng Manag 13 (Suppl 2), 797–809 (2022). https://doi.org/10.1007/s13198-021-01395-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01395-2