# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
###### Abstract
The components are transformed into 2D images through a Markov transition field and are then multiplied, conducting data augmentation through image transformation methods. An adapted and improved classical VGG convolutional neural network architecture is used in classification. In training, the accuracy reached 97%, and the classification accuracy was 88.33%. In [PERSON] et al. [12], the lower-level network parameters of a CNN are transferred (transfer learning) from a previously trained (using natural images) deep architecture (ImageNet), in order to accelerate the training of the proposed deep neural network. The original signals from sensors (datasets) are converted into labeled time-frequency images by using CWT. The proposed machine fault diagnosis, based on fine-tuning a pre-trained model, is verified on datasets from an induction motor, a drivetrain dynamic simulator with gearboxes, and a popular bearing dataset. For the gearbox dataset, a classification improvement from 94.8% to 99.64% was achieved. A deep CNN-based transfer learning for spur gear fault diagnosis based on a private gearbox system is proposed in [PERSON] et al. [13]. Nine pinion health conditions (healthy, missing tooth, root crack, spalling, and five levels of severity for chipping tooth tip) were observed. The transferred layers are pre-trained from ImageNet, too. In the final stage, the CNN is trained as a classifier using limited time-domain experimental data specific to the gear fault diagnosis task. The images are generated after the time synchronous averaging (TSA) is applied to the vibration signals. [PERSON] et al. [14] propose a new gearbox fault diagnosis method based on the Efficient-NetV2 network and uses a lightweight channel attention mechanism. A transfer learning strategy is applied to extract features from limited samples. The model offers fast and accurate fault diagnosis in different working conditions and components (gear and bearing), and good performance under limited samples. At this stage, the validation datasets (from Southeast University on the drivetrain dynamic simulator) were completely balanced. Three severe gear faults (pitting, wear, and fractured tooth in a two-stage gearbox) are detected in [PERSON] et al. [15] by processing the raw acoustic signals (measured in an anechoic chamber with four microphones) for three well-separated speeds and two loads. To improve the classification accuracy, a multi-scale 2D convolutional learning structure (low, mid, and high filter sizes) and a novel temporal attention mechanism instead of a standard CNN model are used. The accuracy achieved is 93.3%. We further mention similar CNN models, which address various mechanical faults. In [PERSON] et al. [16], the time-domain raw signals fill the pixels (in the interval [0, 255]) of a 2D square-shaped gray image, which then feeds the modified LeNet-5 (a pre-trained CNN model) to solve the image classification task in fault diagnosis of rotating machinery. An accuracy of 99.79% was achieved in motor bearing fault diagnosis for ten distinct health states on the CWRU (Case Western Reserve University) dataset. The CWT method is chosen in [PERSON] et al. [17] to convert the raw vibration signals, generated by the core and windings of the dry-type power transformer, into red-green-blue (RGB) images. Fault diagnosis using an improved CNN for different faults (working states) under various loads is presented, achieving an accuracy of 99.95%. In Luczak [18], RGB and grayscale images are obtained using fusion strategies (by sensor, as grid, or by axis) for data collected from a six-degrees-of-freedom multi-axis sensor (three-axis accelerometer and three-axis gyroscope) inertial measurement unit (IMU). The time-series data are processed by using STFT, CWT, Fourier synchrosqueezed transform (FSST), and wavelet synchrosqueezed transform (WSST). The proposed CNN-based fault classification method was validated on the CWRU dataset with thirteen bearing fault types and on a microcontroller-based demonstrator with fan vibration data.