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In response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, based on compressed sensing (CS) and deep multi-kernel extreme learning machine (D-MKELM) theory, a CS-DMKELM intelligent diagnosis model for rolling bearings was proposed. Firstly, sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Secongly, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization (PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis. Results demonstrate that the proposed method, using only a small amount of bearing diagnostic data,automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM.The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s, a final recognition accuracy of 99.29% was achieved. The proposed method reduces the diagnostic time and exhibits the high diagnostic accuracy,providing a new approach for handling massive bearing data in the fault diagnosis.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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