Reference health
An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks
26/26 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
15 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 26 checked references that resolve
resolves10.1016/j.ymssp.2011.08.002
Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
resolves10.1016/j.jlp.2009.08.012
Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method
resolves10.1016/j.ymssp.2007.08.003
Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring
resolves10.1016/j.eswa.2011.04.060
A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis
resolves10.1016/j.ymssp.2010.07.004
Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment
resolves10.1109/ICMTMA.2009.421
Bearing Faults Diagnosis Based on Teager Energy Operator Demodulation Technique
resolves10.1016/j.ymssp.2009.12.007
An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection
resolves10.1109/78.277799
Energy separation in signal modulations with application to speech analysis
resolves10.1109/ISSPIT.2011.6151564
Fault detection in reciprocating compressor valves for steady-state load conditions
resolves10.1016/j.measurement.2012.02.005
A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine
resolves10.1016/j.isatra.2012.12.006
Application of the Teager–Kaiser energy operator in bearing fault diagnosis
resolves10.1007/s00421-010-1521-8
Teager–Kaiser energy operator signal conditioning improves EMG onset detection
resolves10.1016/j.ress.2013.02.022
Failure diagnosis using deep belief learning based health state classification
resolves10.1109/FSKD.2010.5569802
Research and application of manifold learning to fault diagnosis of reciprocating compressor
resolves10.1016/j.ymssp.2004.06.002
Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines
resolves10.3923/itj.2013.287.296
Adaptive Peak Decomposition Approach for the Fault Diagnosis of Reciprocating Compressor based on General Frequency
resolves10.1016/j.ymssp.2012.07.018
Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping
The 15 references without a DOI — listed, not checked
no DOI — not checkedTeager–Huang analysis applied to sonar target recognition
no DOI — not checkedOnline continuous monitoring of mechanical condition and performance for critical reciprocating compressors
no DOI — not checkedA practical guide to training restricted Boltzmann machines
no DOI — not checkedIncrease reliability of reciprocating hydrogen compressors
no DOI — not checkedFault diagnosis of natural gas compressor based on EEMD and Hilbert marginal spectrum
no DOI — not checkedRolling bearing fault detection based on the Teager energy operation and Elman neural network
no DOI — not checkedMohamed, A. R., Dahl, G., & Hinton, G. E. (2009). Deep belief networks for phone recognition. In NIPS workshop on deep learning for speech recognition and related applications.
no DOI — not checkedNair, V., & Hinton, G. E. (2009). 3-D object recognition with deep belief nets. In Proceeding of advances in neural information processing systems.
no DOI — not checkedComparison of Kalman filter and wavelet filter for denoising
no DOI — not checkedSalakhutdinov, R. (2009). Learning deep generative models. Doctor of Philosophy Dissertation, University of Toronto.
no DOI — not checkedFirst attempt of Boltzmann machines for speaker verification
no DOI — not checkedSparse Bayesian leaning and relevance vector machine
no DOI — not checkedFinding sensitive sensor positions under faulty condition of reciprocating air compressors
no DOI — not checkedComparison and application of signal denoising techniques based on time-frequency algorithms
no DOI — not checkedFault diagnosis for reciprocating air compressor valve using P–V indicator diagram and SVM
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