Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis
Abstract
:1. Introduction
2. Experimental Setup
3. Theoretical Background: Classic and Entropy Indicators
3.1. Classic Indicators
3.2. Entropy-Based Indicators
3.2.1. Approximate Entropy
3.2.2. Dispersion Entropy
3.2.3. Single Value Decomposition Entropy
3.2.4. Spectral Entropy of the Permutation Entropy Signal
- The permutation entropy (PerEn) is calculated for the analyzed signal [109]. Each value is obtained for a specific time window thus, a PerEn signal is achieved by sliding the time window along the analyzed signal;
- Using the signal calculated in the previous step, for a time window equivalent to one rotation of the bearing, the SpeEn value is acquired [124].
4. Methodology
4.1. Proposed Diagnosis Method
4.2. Entropy-Based Indicators
4.2.1. Approximate Entropy
4.2.2. Dispersion Entropy
4.2.3. Single Value Decomposition Entropy
4.2.4. Spectral Entropy of Permutation Entropy
4.3. Random Forest
- Set the parameters for RF, which include the intrinsic parameters for DT, and the number of trees to employ;
- After the number of DT is established, a stratified sampling of the data set is set as train and test data. The proportion between both groups of data is fixed for all DT, but the elements of each data group are selected randomly [129];
- Each DT classifies the data using a random feature from the train data, to arbitrarily classify and increase the difference between DTs. This step is fundamental because it improves the generalization error;
- The results of all DT enable the classification of the test samples to be determined using the vote of each DT. The decision is done by majority voting.
- The function criteria to measure the quality of a split, which is set as the Gini impurity;
- The maximum depth of the tree, which is set to three for this work;
- The maximum number of leafs, which is unlimited;
- The minimum number of samples required to split an internal node, which is two;
- The minimum number of samples required to be at a leaf node, which is one.
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AppEn | approximate entropy |
BL | large ball bearing damage scenario |
BS | small ball bearing damage scenario |
CBM | condition-based maintenance |
CEI | classic and entropy indicators |
CF | crest factor |
CI | classic indicator |
CM | condition monitoring |
DisEn | dispersion entropy |
DT | decision tree |
EI | entropy indicator |
EMD | empirical mode decomposition |
EU | European Union |
FC | frequency center |
HS | healthy scenario |
IF | impulse factor |
IMF | intrinsic mode function |
MF | margin factor |
NCDF | normal cumulative distribution function |
O&M | operating and maintenance |
PerEn | permutation entropy |
PSD | power spectral density |
RF | random forest |
RL | large raceway shaft washer damage scenario |
RMS | root mean square |
RMSF | root mean square frequency |
rpm | revolutions per minute |
RS | small raceway shaft washer damage scenario |
RVF | root variance frequency |
SepEn | spectral entropy of the permutation entropy signal |
SF | shape factor |
SpeEn | spectral entropy |
SvdEn | singular value decomposition entropy |
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HS | RS | RL | BS | BL | |
---|---|---|---|---|---|
10 rpm | 191 | 195 | 195 | 40 | 80 |
8 rpm | 156 | 156 | 156 | 156 | 156 |
5 rpm | 97 | 97 | 97 | 97 | 97 |
CIs | EIs | CEIs | ||||
---|---|---|---|---|---|---|
10 rpm | 73% | 0.027285 | 90% | 0.017744 | 90% | 0.018584 |
8 rpm | 78% | 0.025358 | 94% | 0.013072 | 97% | 0.010761 |
5 rpm | 77% | 0.033270 | 99% | 0.003561 | 98% | 0.014807 |
CIs | EIs | CEIs | |
---|---|---|---|
10 rpm | 74% | 90% | 93% |
8 rpm | 76% | 94% | 98% |
5 rpm | 75% | 100% | 98% |
CIs | EIs | CEIs | |
---|---|---|---|
10 rpm | 1.7091 | 1.5192 | 1.6991 |
8 rpm | 1.7624 | 1.5625 | 1.7391 |
5 rpm | 1.5992 | 1.4790 | 1.5792 |
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Sandoval, D.; Leturiondo, U.; Vidal, Y.; Pozo, F. Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis. Sensors 2021, 21, 849. https://doi.org/10.3390/s21030849
Sandoval D, Leturiondo U, Vidal Y, Pozo F. Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis. Sensors. 2021; 21(3):849. https://doi.org/10.3390/s21030849
Chicago/Turabian StyleSandoval, Diego, Urko Leturiondo, Yolanda Vidal, and Francesc Pozo. 2021. "Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis" Sensors 21, no. 3: 849. https://doi.org/10.3390/s21030849
APA StyleSandoval, D., Leturiondo, U., Vidal, Y., & Pozo, F. (2021). Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis. Sensors, 21(3), 849. https://doi.org/10.3390/s21030849