Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection

Hamadache, Moussa; Lee, Dongik

doi:10.1007/s12555-015-0196-7

Cited by 48 publications

(25 citation statements)

References 26 publications

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“…[332][333][334][335][336][337][338] The utilization of advanced signal processing and adaptive control algorithms in the future can be helpful to achieve earlier detection of the hemodynamic response not to mention the accuracy. 11,[339][340][341][342][343]…”

Section: Processing Of Fnirs Signalsmentioning

confidence: 99%

Application of functional near-infrared spectroscopy in the healthcare industry: A review

Hong

Yaqub

2019

J. Innov. Opt. Health Sci.

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Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.

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Section: Processing Of Fnirs Signalsmentioning

confidence: 99%

Application of functional near-infrared spectroscopy in the healthcare industry: A review

Hong

Yaqub

2019

J. Innov. Opt. Health Sci.

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“…These methods usually require the extraction of a set of features from the aforementioned raw signals in order to classify the bearing faults [9]. For extracting useful features, usually the acquired signals are processed using Fourier transform (FT) or its variants (i.e., fast FT (FFT), discrete FT (DFF)) [10], wavelet transform (WT) or its variants (i.e., continuous WT (CWT), discrete WT (DWT), wavelet packet transform (WPT)) [11,12], envelope analysis [13,14], or statistical methods (e.g., principal component analysis (PCA), linear discriminant analysis (LDA)) [15][16][17][18][19]. Thus, the extracted features could be in time domain, frequency domain, or time-frequency domain, which usually have physical meanings, or purely statistical features, which usually do not have physical meanings [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, in the previous paper [4], the authors developed the AVPCA algorithm and used it as a feature extraction and then for classification to detect and diagnose the different bearing faults using only the rotor-speed signal. Furthermore, the PCA method was used for the condition monitoring systems (CMSs) [29][30][31] and for bearing fault detection and diagnosis [4,17,32,33]. However, it was never used as an image recognition tool to BFDD as proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

et al. 2018

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This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.

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“…Principal component analysis (PCA) [41][42][43]-PCA extracts k principal components by using a linear transformation of the singular value decomposition (SVD) to maintain most of the variability in input data. • Latent semantic analysis (LSA) [44]-Contrary to the PCA, LSA performs the linear dimensionality reduction by means of the truncated SVD.…”

mentioning

confidence: 99%

“…and 'RFE algo.,' only the original variables are used as input variables in the learning algorithm. Dimension-reduction by PCA-Before executing the learning method, the dimension of the input space consisting of all of the newly constructed features and the original variables can be finally reduced by the PCA [41][42][43]. The 'PCA flag' and 'PCA para.'…”

mentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection

Cited by 48 publications

References 26 publications

Application of functional near-infrared spectroscopy in the healthcare industry: A review

Application of functional near-infrared spectroscopy in the healthcare industry: A review

Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

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