Non-destructive identification of moldy pear core using acoustic vibration analysis and dual-channel deep feature fusion

Nondestructive Detection of Sweet Orange Granulation Using Noncontact Acoustic Vibration and Attention-Based Deep Learning

Granulation is a major physiological disorder that compromises sweet orange quality. Conventional destructive detection methods lead to food waste and cannot be used for batch inspection. This study proposes a nondestructive approach for detecting granulation in sweet oranges using air-jet transient excitation and a la...

Acoustic–vibration fusion bearing fault diagnosis via a multi-scale Swin–CNN hybrid architecture

An acoustic–vibration fusion method for bearing fault diagnosis based on a multi-scale Swin–CNN hybrid architecture that employs a Bayesian optimization-based tunable Q-factor wavelet transform (BO-TQWT) to enhance fault-sensitive subbands under low signal-to-noise ratio conditions, and converts acoustic and vibration...

Single-sensor Acoustic Emission Damage Location in Orthotropic Steel Decks Based on Continuous Wavelet Transform and Convolutional Neural Network

Feature extraction and recognition based on distributed optical fiber network vibration signals

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) offers advantages such as a simple structure, multi-point vibration localization, and long-distance disturbance detection in optical fiber networks. However, accurately distinguishing diverse environmental vibration events remains challenging. In this study, ex...

Gearbox multimodal fault diagnosis based on microphone array and cross-attention

In order to solve the problems of limited vibration signal acquisition, incomplete single-modal features, and sound signals being vulnerable to noise interference in gearbox fault diagnosis, a Multi-Channel Convolutional-Transformer Cross- Attention Diagnosis Model is proposed. Its core innovations are as follows: comb...

Evaluation of multi-location accelerometer sensor configurations for vibration-based classification of CNC milling conditions

In CNC milling processes, vibration signals are a crucial data source that directly reflects machining dynamics and changes in cutting conditions. This study aims to classify material type, feed rate, and depth-of-cut using data-driven vibration signals obtained during CNC milling of Al6061 and Al7075 aluminum alloys...