Acoustic signal - based misfire detection in internal combustion engines using machine learning techniques

Deep Learning-Based Anomaly Detection in Electric Motor Production Using Mel-Spectrogram Representations and a Hybrid Neural Network

Experimental results demonstrate strong discrimination between healthy and faulty motors, indicating that the proposed approach is suitable for automated EoL quality inspection in manufacturing environments where labeled faulty data are scarce.

Vibration signal analysis using Short-Time Fourier Transform and hierarchical convolutional neural networks for multi-level diesel engine fault diagnosis

Acoustic Traffic Monitoring with MEMS Microphones, Concatenated log-Bark Spectrograms and CNN

The conclusion was that the MEMS-microphones could be used to develop edge-AI type sensors for motion’s direction determination and detection of passing vehicle in background noise using the concatenated log-Bark spectrograms converted to grayscale images as input to a CNN-classifier.

Early fault identification method for coal mill in thermal power units based on machine acoustic signature and deep neural network fusion

As one of the most safety- and efficiency-critical auxiliary machines in thermal power units, the running condition of a coal mill has a direct bearing on the stability and economy of the whole unit. Conventional diagnostic schemes, however, tend to respond too late and to raise too many false alarms. To address these...

Spectrogram-Based CNN Autoencoder for One-Class Anomaly Detection in Machining Vibration Signals

This work presents a one-class anomaly detection framework for vibration-based condition monitoring in machining processes using a convolutional neural network autoencoder. Three-axis vibration signals acquired during industrial broaching are transformed into time–frequency spectrograms and combined into a compact thre...

Multiscale denoising residual convolutional neural network for oil temperature monitoring of marine thrust bearings