Abstract
The frequent accidents caused by the main fan motor in coal mines have exposed the safety hazards of rolling bearings. When a rolling bearing fails, its symmetry is broken, resulting in a rapid decline in its safety performance and posing a great threat to the main fan. Therefore, accurate rolling bearing fault diagnoses are the key to ensuring the safe and durable operation of main fans. Thus, in this paper, we propose a new fault diagnosis method of rolling bearing based on wavelet packet analysis and deep forest algorithm. Firstly, experiments were conducted under different health states to guarantee the diversity of data relating to the rolling bearing’s main fan and then to ensure the accuracy of the fault diagnosis under different health states. On the basis of the collected vibration signal data, we conducted the wavelet packet analysis method to extract the characteristics of the vibration signal and obtained a feature vector that characterizes the health of the bearing. After that, the extracted feature vector was used as the feature vector of the deep forest algorithm to train the deep forest diagnosis model and determine the location and fault type of the bearing fault. Finally, the proposed method in this paper was validated with real-time monitoring data of a main ventilation fan and compared with other diagnostic algorithms, which not only verified the diagnostic capability of deep forest in handling small samples, but also verified the diagnostic capability of the fault diagnosis model. In summary, the proposed fault diagnosis approach is promising in real coal mine main fans.
Highlights
IntroductionNote: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Accepted: 8 January 2022Published: 29 January 2022 Publisher’sNote: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland
Aiming at the shortcomings of the intelligent fault diagnosis method described in this article, combined with the advantages of wavelet packet analysis in feature extraction, we propose a fault diagnosis method for the rolling bearing of coal mine main fan based on wavelet packet analysis and deep forest algorithm
Summary
Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Seeking an effective feature extraction for non-stationary vibration signals and a fault diagnosis machine learning method suitable for small training samples is of great significance to the fault diagnosis of the rolling bearing of the coal mine main fan [1]. Some scholars used machine learning methods to improve the deep forest model, solving the problems of the long characteristics of single-sample data of mechanical equipment vibration signals and the high cost of deep forest model data processing, and realized the fault diagnosis of mechanical equipment under small training samples [20]. We collected the vibration signals of the rolling bearing at the drive end of the main fan with different spectrum information, and we applied the wavelet packet analysis feature extraction method. The validity of the fault diagnosis method for the rolling bearing of the driving end of the coal mine main fan under the condition of small training samples was verified
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