Impact type fault interpretable detection method for ship key equipment based on wavelet scattering and time-frequency feature enhancement

By improving wavelet scattering transform and time-frequency feature enhancement techniques, and combining complex Morlet wavelets and optimized filter banks to process vibration signals of key ship equipment, an interpretable fault detection model is constructed. This solves the problems of weak generalization ability and uninterpretable diagnostic results in existing technologies, and achieves high-precision, interference-resistant fault detection.

CN121919664APending Publication Date: 2026-04-24QINGDAO RUHAI SHIPBUILDING CO LTD
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Patent Information

Application Number
CN202610054473.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-15
Publication Date
2026-04-24

AI Technical Summary

Technical Problem

Existing methods for detecting impact-type faults in critical ship equipment have weak generalization ability, uninterpretable diagnostic results, and poor anti-interference ability, making it difficult to effectively identify fault characteristics in complex environments.

Method used

An improved wavelet scattering transform and time-frequency feature enhancement technique are employed, combined with complex Morlet wavelets and optimized filter banks to process vibration signals. An interpretable fault detection model is generated through a VGG16 network and class activation mapping (CAM), enabling visualization of fault features and high-precision diagnosis.

Benefits of technology

It enhances the anti-interference and generalization capabilities of detection, provides interpretable diagnostic results, improves the accuracy and reliability of fault detection for critical ship equipment, and adapts to different ships and operating conditions.

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Abstract

The invention discloses a ship key equipment impact type fault interpretable detection method based on wavelet scattering and time-frequency feature enhancement, and belongs to the field of ship equipment fault detection. According to the method, a vibration signal of ship key equipment is acquired through a sensor, and the vibration signal is converted into a time-frequency image with cross-domain consistency by adopting improved wavelet scattering transform; adaptive enhancement processing based on statistical features is carried out on the time-frequency image to realize noise suppression and fault feature enhancement; and inputting the processed time-frequency image into a pre-training model embedded with class activation mapping to complete fault identification and decision process visualization. Aiming at the impact type fault detection requirement of the ship key equipment, the method solves the problems that a traditional method is weak in generalization ability and cannot explain the diagnosis result, has the advantages of being high in detection precision, high in adaptability and traceable in diagnosis result, and can be widely applied to bearing fault detection of key equipment such as a ship main lubricating oil pump, an oil separator and a turbine.
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