A slurry pump fault detection and life prediction system and method based on big data

Through the big data-based slurry pump fault detection and life prediction system, using outlier factor algorithm, singular value decomposition and neural network technology, the problems of large errors and slow speed in traditional slurry pump detection and prediction are solved, and efficient fault detection and accurate life prediction are achieved.

CN119441716BActive Publication Date: 2025-09-12JIANGXI NAIPU MINING MASCH CO LTD
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Patent Information

Application Number
CN202411401764.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-09
Publication Date
2025-09-12
Estimated Expiration
2044-10-09

AI Technical Summary

Technical Problem

Traditional slurry pump fault detection relies on experience-based judgment, which has large errors, inaccurate life prediction methods, and does not utilize neural network technology, resulting in slow detection speed and inaccurate prediction results.

Method used

A big data-based method is used to collect slurry pump vibration signals, detect abnormal signals using the outlier factor algorithm, perform singular value decomposition for noise reduction, generate vibration signal images and extract feature vectors, train convolutional neural networks for fault detection, and use LSTM neural networks for life prediction.

Benefits of technology

The accuracy and speed of slurry pump fault detection are improved, detection errors are reduced, the accuracy of life prediction is improved, and efficient fault detection and life prediction are achieved.

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Abstract

The present invention relates to the technical field of data analysis, and discloses a slurry pump fault detection and life prediction system and method based on big data. The present invention first obtains the slurry pump vibration signal, detects the abnormal vibration signal and deletes the abnormal vibration signal, and then performs noise reduction processing on the slurry pump vibration signal; secondly, the slurry pump vibration signal after noise reduction is processed by segmented aggregation approximation and Gram angle field to generate a slurry pump vibration signal image, and extracts the slurry pump vibration signal image features, trains a convolutional neural network, and optimizes the convolutional neural network parameters based on the electric eel foraging optimization algorithm to obtain a convolutional neural network recognition model to achieve slurry pump fault detection; then, the slurry pump vibration signal IMF component is decomposed, the LSTM neural network is trained, the LSTM neural network prediction model is obtained, and the slurry pump life prediction value is output. The present invention achieves the purpose of fault detection and life prediction by processing the slurry pump vibration signal, and the method is accurate and objective.
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