Shock and Vibration / 2018 / Article / Tab 10 / Research Article
Fault Diagnosis of Bearings with Adjusted Vibration Spectrum Images Table 10 Comparisons for multiclass fault detection between current work and some published work.
Method in refs. Training data Testing data Classification accuracy (%) RPS and GMM in [18 ] Load 1 (3840) Load 1 (3840) 99.95a DNN-based method in [9 ] Load 1 (1000) Load 1 (1000) 99.95 Load 2 (1000) Load 2 (1000) 99.61 Load 3 (1000) Load 3 (1000) 99.74 Load 1–3 (3000) Load 1–3 (3000) 99.68 LWPT and binary tree system in [19 ] Load 0 (400) Load 0 (200) 99.53 MFE and MSVM in [20 ] Load 0 (840) Load 0 (360) 94.50 TR-LDA2 and kNN classifier in [21 ] Load 1 (200) Load 1 (600) 98.00 Load 2 (150) Load 2 (650) 97.65 Multiple ANFIS combination in [22 ] Load 0–3 (300) Load 0–3 (300) 91.33 MKMFA and kNN in [23 ] Load 0–3 (500) Load 0–3 (500) 97.45 Dataset G2 in present work Load 1 (100) Load 1 (400) 100 Dataset G3 in present work Load 2 (100) Load 2 (400) 100 Dataset G4 in present work Load 3 (100) Load 3 (400) 100 Dataset G1 in present work Load 0 (100) Load 0–3 (1900) 99.13 Dataset G2 in present work Load 1 (100) Load 0–3 (1900) 99.99 Dataset G3 in present work Load 2 (100) Load 0–3 (1900) 98.07 Dataset G4 in present work Load 3 (100) Load 0–3 (1900) 95.72 Dataset G5 in present work Load 0–3 (400) Load 0–3 (1600) 100 Dataset G1 with nonadjusted spectrumb Load 0 (100) Load 0–3 (1900) 84.97 Dataset G2 with nonadjusted spectrumb Load 1 (100) Load 0–3 (1900) 92.30 Dataset G3 with nonadjusted spectrumb Load 2 (100) Load 0–3 (1900) 89.66 Dataset G4 with nonadjusted spectrumb Load 3 (100) Load 0–3 (1900) 90.72 Dataset G5 with nonadjusted spectrumb Load 0–3 (400) Load 0–3 (1600) 100
a This classification accuracy is computed based on Table
2 in [
18 ], which is the average of the ten classes.
b The test settings are same with that in G1–G5 using the adjusted spectrum images.