scholarly journals Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

2021 ◽  
Author(s):  
George Jordan ◽  
Allan Brimicombe ◽  
Yang Li
Keyword(s):  
Feature Extraction ◽  
Data Compression ◽  
Frequency Domain ◽  
Health Indicators ◽  
Fast Fourier Transformation ◽  
Time Frequency ◽  
Vibration Data ◽  
Bearing Vibration ◽  
Machine Health ◽  
Health Deterioration

Various data-driven methods have been applied to predict machine health indicators especially in the field of prognostics. Machine health indicators reveal the condition of equipment and/or its components including bearings by monitoring their operation data such as frequency vibration. To aid the prediction of the machine health indicators, this study applies the BDQRA method to monitor the health of bearings as a component of the machine. The BDQRA method involves applying data compression techniques like feature extraction to the bearing vibration data, to extract the most important features like time-domain, frequency domain, and time–frequency domain features. Due to the complexity of the feature extraction process, this study proposes fast Fourier transformation for the data compression. This is followed by obtaining a time series profile of the bearing vibration data to analyse the health status of component bearing. It the uses change-point analysis to predict the period at which the bearing health deterioration is imminent. Since the bearing health deterioration could be due to the independent operation of a component bearing or through communication between the component bearing and other components (or bearings) within the process machinery, the method also applies the principle of interaction effect to investigate the contributions from the other components of the machinery to the health deterioration of the component bearing detected. The accuracy of the prediction of the point of imminent health deterioration of the component bearing is investigated by comparing the outcome of the BDQRA method with the outcome of other methods published in literature which have been applied to the dataset used in this study. The findings reveal the BDQRA method have comparative advantages to the methods used in the related studies.

scholarly journals Similarity-based Feature Extraction from Vibration Data for Prognostics

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Wei Huang ◽  
Ahmed Farahat ◽  
Chetan Gupta
Keyword(s):  
Feature Extraction ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Training Data ◽  
Future Health ◽  
Frequency Spectra ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Vibration Data ◽  
Useful Life

Many techniques for prognostics depend on estimating then forecasting health indicators that reflect the overall health or performance of an asset. For vibration data, health indicators are typically calculated by combining various vibration measurements along with derived features extracted from time, frequency or time-frequency domain analysis. However, selecting or handcrafting good features is a labor-intensive task. On the other hand, deep learning models might be able to learn health indicators automatically from vibration data but require large amount of training data, which are typically hard to obtain from real assets. In this paper, we propose an innovative similarity-based feature extraction method for vibration data which can then be used to learn health indicators and estimate remaining useful life of equipment. The method learns a set of representative templates of frequency spectra for both normal and failure states, and then calculates similarity-based features between new vibration data and the set of learned templates. These features are used to estimate health indicators which are then extrapolated to estimate the future health condition of the asset and its remaining useful life. The proposed method has been tested on the PRONOSTIA bearing dataset provided by FEMTO-ST Institute and achieved a higher accuracy in estimating the remaining useful life of bearings compared to other studies. The results demonstrate the effectiveness of the proposed method for assets with limited training data.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Event Discrimination Method for Distributed Optical Fiber Intrusion Sensing System Based on Integrated Time/Frequency Domain Feature Extraction

2019 ◽  
Vol 39 (6) ◽  
pp. 0628002 ◽  
Author(s):  
彭宽 Kuan Peng ◽  
冯诚 Cheng Feng ◽  
王森懋 Senmao Wang ◽  
艾凡 Fan Ai ◽  
李豪 Hao Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optical Fiber ◽  
Frequency Domain ◽  
Time Frequency ◽  
Sensing System ◽  
Distributed Optical Fiber ◽  
Discrimination Method

The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings

2019 ◽  
Vol 233 (13) ◽  
pp. 4543-4555 ◽  
Author(s):  
Akhand Rai ◽  
Sanjay H Upadhyay
Keyword(s):  
Frequency Domain ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Health Indicators ◽  
Data Matrix ◽  
Bearing Faults ◽  
Time Frequency ◽  
Factorization Algorithm ◽  
Incipient Faults

Bearing faults are a major reason for the catastrophic breakdown of rotating machinery. Therefore, the early detection of bearing faults becomes a necessity to attain an uninterrupted and safe operation. This paper proposes a novel approach based on semi-nonnegative matrix factorization for detection of incipient faults in bearings. The semi-nonnegative matrix factorization algorithm creates a sparse, localized, part-based representation of the original data and assists to capture the fault information in bearing signals more effectively. Through semi-nonnegative matrix factorization, two bearing health indicators are derived to fulfill the desired purpose. In doing so, the paper tries to address two critical issues: (i) how to reduce the dimensionality of feature space (ii) how to obtain a definite range of the indicator between 0 and 1. Firstly, a set of time domain, frequency domain, and time–frequency domain features are extracted from the bearing vibration signals. Secondly, the feature dataset is utilized to train the semi-nonnegative matrix factorization algorithm which decomposes the training data matrix into two new matrices of lower ranks. Thirdly, the test feature vectors are projected onto these lower dimensional matrices to obtain two statistics called as square prediction error and Q2. Finally, the Bayesian inference approach is exploited to convert the two statistics into health indicators that have a fixed range between [0–1]. The application of the advocated technique on experimental bearing signals demonstrates that it can effectively predict the weak defects in bearings as well as performs better than the earlier methods like principal component analysis and locality preserving projections.

scholarly journals OPTIMAL ACCURACY PERFORMANCE IN MUSIC-BASED EEG SIGNAL USING MATTHEW CORRELATION COEFFICIENT ADVANCED (MCCA)

2021 ◽  
Vol 83 (6) ◽  
pp. 53-61
Author(s):  
Mahfuzah Mustafa ◽  
Zarith Liyana Zahari ◽  
Rafiuddin Abdubrani
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Correlation Coefficient ◽  
Frequency Domain Analysis ◽  
Index Formula ◽  
Eeg Signal ◽  
Time Frequency ◽  
Matthew Correlation Coefficient ◽  
Accuracy Performance ◽  
Arousal And Valence

The connection between music and human are very synonyms because music could reduce stress. The state of stress could be measured using EEG signal, an electroencephalogram (EEG) measurement which contains an arousal and valence index value. In previous studies, it is found that the Matthew Correlation Coefficient (MCC) performance accuracy is of 85±5%. The arousal indicates strong emotion, and valence indicates positive and negative degree of emotion. Arousal and valence values could be used to measure the accuracy performance. This research focuses on the enhance MCC parameter equation based on arousal and valence values to perform the maximum accuracy percentage in the frequency domain and time-frequency domain analysis. Twenty-one features were used to improve the significance of feature extraction results and the investigated arousal and valence value. The substantial feature extraction involved alpha, beta, delta and theta frequency bands in measuring the arousal and valence index formula. Based on the results, the arousal and valance index is accepted to be applied as parameters in the MCC equations. However, in certain cases, the improvement of the MCC parameter is required to achieve a high accuracy percentage and this research proposed Matthew correlation coefficient advanced (MCCA) in order to improve the performance result by using a six sigma method. In conclusion, the MCCA equation is established to enhance the existing MCC parameter to improve the accuracy percentage up to 99.9% for the arousal and valence index.

scholarly journals Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 852 ◽  
Author(s):  
Pengjie Qin ◽  
Xin Shi
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
Evaluation Method ◽  
Lower Limbs ◽  
Thigh Muscle ◽  
Gaussian Kernel ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Time Frequency

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

scholarly journals An Automated method for the analysis of bearing vibration based on spectrogram pattern matching

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
P. Arun ◽  
S. Abraham Lincon ◽  
N. Prabhakaran
Keyword(s):  
Pattern Matching ◽  
Similarity Index ◽  
Structural Similarity ◽  
P Value ◽  
Outer Race ◽  
Time Frequency ◽  
Vibration Data ◽  
Automated Method ◽  
Pattern Similarity ◽  
Bearing Vibration

As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and the baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from the normal and faulty bearings differ with a P value of 4.43693x 10-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention

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