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Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis
2013
Sensors
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This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. ...
Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. ...
The authors would like to thank Case Western Reserve University for offering free download of the bearing data, and the anonymous reviewers for their valuable comments. ...
doi:10.3390/s140100382
pmid:24379045
pmcid:PMC3926563
fatcat:bkkjqmj5vndebina5y6b4xgo7u
A novel manifold learning denoising method on bearing vibration signals
2016
Journal of Vibroengineering
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This paper presents time-frequency analysis and nonlinear manifold learning technique for denoising vibration signals corrupted by additive white Gaussian noise. ...
To retrieve the characteristic fault frequencies of the vibration signal, signal denoising is an essential processing step in fault diagnosis of the bearings. ...
Vibration sensor data denoising using a time-frequency manifold for
machinery fault diagnosis. Sensors, Vol. 14, Issue 1, 2013, p. 382-402.
188 © JVE INTERNATIONAL LTD. ...
doaj:3f51874750ec401086c8ec9edd36bb8e
fatcat:2c44twrfdvd53bydk355pnt4iu
Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis
2016
Mathematical Problems in Engineering
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Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. ...
The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. ...
Acknowledgments The authors acknowledge the financial support from the National Natural Science Foundation of China under Grant no. 51609203 and from the Fundamental Research Funds for the Central Universities ...
doi:10.1155/2016/2727684
fatcat:omyvxs7l6zacjfezj2glalw3lu
Advances in intelligent computing for diagnostics, prognostics, and system health management
2018
Journal of Intelligent & Fuzzy Systems
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[2] presents a new manifold learning framework for machinery fault diagnosis. ...
Li propose a denoising deep autoencoder for feature selection in fault diagnosis of rotating machinery. ...
doi:10.3233/jifs-169520
fatcat:vj2iejwahzaytcamcmx24fo5da
Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method
2018
Complexity
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As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. ...
This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling ...
(a) IRF of rolling bearing, (b) ORF of rolling bearings, and (c) location of sensor.
Figure 15 : 15 Time and frequency domain plots of collected vibration signal of IRF rolling bearing. ...
doi:10.1155/2018/3049318
fatcat:sd2eeynsqfbsllglk34b6aqjn4
Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software
2014
Research Journal of Applied Sciences Engineering and Technology
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Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diesel engine. ...
The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on discrete wavelet analysis and LabVIEW software. ...
Vibration measurement is one of the most common fault diagnosis methods. ...
doi:10.19026/rjaset.7.739
fatcat:otrmnmn3cnbohbxplwbdruxk5i
Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation
2016
Shock and Vibration
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The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. ...
To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. ...
Acknowledgments This project is supported by the National Natural Science Foundation of China (51275030) and the Fundamental Research Funds for the Central Universities (2011JBM093). ...
doi:10.1155/2016/3952325
fatcat:uudbjj647rh75gubi4xyf4do24
Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
2015
Sensors
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The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain ...
Nowadays, various fault diagnosis methods have been proposed for actual roller bearing fault detection based on vibration signals obtained from accelerometer sensors. ...
We are grateful to the support of the Case Western Reserve University Bearing Data Center website for providing the original bearing fault vibration signals. ...
doi:10.3390/s150716225
pmid:26153771
pmcid:PMC4541876
fatcat:qdevknnfxvaw7cyhziqrh7e7rq
Mathematical Methods and Modeling in Machine Fault Diagnosis
2014
Mathematical Problems in Engineering
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Wang et al. proposed a new tensor manifold method to realize the bearing fault feature extraction. The time-frequency characteristics of the signals are extracted using the tensor manifold. ...
Sun et al. studied a fault diagnosis method for rotating machinery based on a multiwaveletadaptive threshold denoising and mutation particle swarm optimization algorithm (MPSO). ...
We are also grateful to all the reviewers for their insightful and constructive comments.
Ruqiang Yan Xuefeng Chen Weihua Li Shuangwen Sheng Submit your manuscripts at http://www.hindawi.com ...
doi:10.1155/2014/516590
fatcat:neyrvxmv4reizn4kef3ia4qouq
Transient signal analysis using parallel time-frequency manifold filtering for bearing health diagnosis
2019
IEEE Access
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INDEX TERMS Sparse representation, time-frequency image, parallel time-frequency manifold filtering, image morphology filtering, fault diagnosis. ...
It is crucial to identify and extract the weak transient features embedded in the vibration signals for bearing health monitoring and fault diagnosis. ...
If the data is used as input for manifold learning without pretreatment, it will take a long calculation time (more than 12 hours) to obtain the TFM. ...
doi:10.1109/access.2019.2956824
fatcat:wlppato7lzctdmx63lx6cbqqiy
Weak Degradation Characteristics Analysis of UAV Motors Based on Laplacian Eigenmaps and Variational Mode Decomposition
2019
Sensors
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A validation experiment was conducted on a specific type of motor under operation with load, to obtain the degradation characteristics of multiple types of vibration signals, and to test the proposed method ...
the degradation information in system health data, avoid the loss of critical information and the interference of redundant information, and to optimize the description of a motor's degradation process ...
sensors Vibration signals acquisition 6 Sensors and data acquisition equipment Send and convert analog signals 7 Data acquisition equipment and upper computer Send digital signals For a general rotating ...
doi:10.3390/s19030524
fatcat:jx7epuajmjbcjlv7rpeisjiheu
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
2020
Shock and Vibration
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Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. ...
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial ...
Figure 5 shows a general block diagram of a noninvasive FDD for rotating machinery. As an example, preprocessing stage includes data denoising and filtering. ...
doi:10.1155/2020/8843759
fatcat:h4zyvhct6nb7lpsj7j5f3yror4
Ensembled mechanical fault recognition system based on deep learning algorithm
2021
Journal of Vibroengineering
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The fault diagnosis via data-driven methods have become a point of expansion with recent development in smart manufacturing and fault recognition techniques using the concept of deep learning. ...
deep neural network (DNN) for recognising the different fault states of the rotating machinery. ...
The 1D vibration signal is measured using acceleration sensor at various operating states and the time domain vibration signal as well as frequency domain signal spectrum is depicted in Fig. 5 . ...
doi:10.21595/jve.2021.21944
fatcat:mgjs6voggbfsrge2ompmblrcgy
A NEW DATA MINING APPROACH FOR GEAR CRACK LEVEL IDENTIFICATION BASED ON MANIFOLD LEARNING
2012
Mechanics
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[16] proposed a method of nonlinear time series noise reduction based on principal manifold learning applied to the analysis of gearbox vibration signal with tooth broken, but only for signal denoising ...
Fig. 5 5 Optical pick-up sensor for speed measurement
Fig. 6 6 Time and FFT frequency spectrum of the gear vibration signals: (left) normal, (middle) slight crack and (right) serious crack
Fig. 8 8 ...
The experimental vibration data acquired from the gear fault test-bed were processed for feature reduction and extraction using the proposed method. ...
doi:10.5755/j01.mech.18.1.1276
fatcat:a3fe7x3ta5bzrk4oe4n4vkvurq
Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis
2013
Measurement (London)
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It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. ...
Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. ...
[27] proposed a method for nonlinear time series noise reduction based on principal manifold learning applied to the analysis of gearbox vibration signal with tooth broken, but only for signal denoising ...
doi:10.1016/j.measurement.2012.06.013
fatcat:xjpaoxc5ofe3zajipn5tvybqaa
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