Sound-recognition-based method for monitoring operating condition abnormity of oil pump electric motor and outlet pipes thereof

By arranging acoustic sensors at the oil pump motor and outlet pipeline, and combining acoustic recognition technology and feature extraction algorithms, the problems of low monitoring efficiency and safety hazards of oil pump motors in existing technologies have been solved. Real-time monitoring and rapid fault location of the oil pump motor and its outlet pipeline have been achieved, improving the level of intelligent equipment management.

WO2025241206A1 Publication Date: 2025-11-27CHINA YANGTZE POWER
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Patent Information

Application Number
PCT/CN2024/095732
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-05-21
Filing Date
2024-05-28
Publication Date
2025-11-27

AI Technical Summary

Technical Problem

Current technology for monitoring oil pump motors and their outlet pipelines relies on manual inspection, which is inefficient, lacks real-time capability, poses safety hazards, makes it difficult to accurately locate the cause of abnormalities, image monitoring cannot identify internal seal failures, and manual analysis is time-consuming and lacks reliability.

Method used

Using voiceprint sensors and voiceprint recognition technology, voiceprint information is collected by sensors placed at the oil pump motor and outlet pipeline. Feature extraction and analysis are performed by combining Mel frequency cepstral coefficients (MFCC), universal background model (UBM), and i-vector method for calculating identity vector features. Probabilistic linear discriminant analysis (PLDA) is used for fault determination, and a voiceprint fault sample library is established to achieve real-time monitoring and rapid location.

Benefits of technology

It enables real-time monitoring of the oil pump motor and its outlet pipeline, eliminating the uncertainties of manual inspection, improving fault diagnosis efficiency, enhancing the level of intelligent equipment management, and enabling timely location and handling of abnormal operating conditions to avoid secondary faults.

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Abstract

A sound-recognition-based method for monitoring an operating condition abnormity of an oil pump electric motor and outlet pipes thereof. Applying soundprint recognition technology to a speed-regulating hydraulic system of a hydropower station, and rational layout of on-site soundprint sensors realize real-time monitoring of operating conditions of an oil pump electric motor and outlet pipes thereof, thereby improving the intelligence level of device management, and achieving an industrial demonstration effect. By means of deep learning analysis of a soundprint fault sample library combined with multi-dimensional factors such as pressure, liquid level and temperature, a soundprint-recognition-based multi-dimensional-factor logical determination process for abnormal operating conditions of an oil pump electric motor and outlet pipes is designed, such that real-time monitoring and rapid locating of abnormal operating conditions such as base vibration, lubricating oil deficiency in an oil pump electric motor, pump body blade fracture, check valve malfunction, incomplete closure or internal leakage of a drain valve, frequent loading and unloading of an oil pump, and servomotor pipe pulsation are realized, so as to promptly use response measures such as automatic switching of main and standby oil pump electric motor units or automatic adjustment of unit load. Using soundprint sensors and soundprint recognition technology to monitor the state of on-site devices in real time eliminates uncertainties in manual inspection, and improves the fault diagnosis and analysis efficiency.
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