## 1 Introduction
[22; 23] proposed an adaptive noise reduction model, which effectively reduced the noise in the impaired signal. The resultant impaired signal is then used for the classification of gearbox multi-level tooth cut faults under variable speed conditions. The effectiveness of the adaptive noise reduction model lies in adaptively adjusting optimal parameters of the Gaussian function, which are connected to the optimal weights of the adaptive filter, along the whole frequency range of a vibration signal. Nevertheless, the frequency spectrum of a vibration signal obtained from a gearbox is composed of meshing frequency harmonics, sideband components, and random noises, with different probability distributions. It should be noted that the influence of random noises and the change in stiffness of the gear under defect makes the vibration signal non-stationary and complex. Therefore, a single optimal parameter set of the Gaussian reference signal along the entire frequency range is less effective for noise reduction. To address this issue, a localized adaptive denoising technique (LADT) is proposed in this paper. The proposed LADT is a modified version of the adaptive noise reduction model proposed in [22]. The LADT adaptively transforms the raw vibration signals to the optimized sub-bands, which accounts for the majority of the defect-related information. The proposed method can reduce noise more effectively than the previous adaptive denoising models, while maintaining original fault-related information. The resultant impaired signal from LADT is then used for feature engineering and fault classification in the proposed scheme of gearbox fault diagnosis. After signal preprocessing, feature preprocessing and fault classification are the most important steps in the fault identification system. Conventional methods for the fault diagnosis of gearboxes used handcrafted features. After extracting a limited number of features from the signal in the conventional methods, domain knowledge was used for discriminant feature selection. These discriminant features were then classified using machine learning algorithms, such as support vector machines (SVMs), k-nearest neighbors (KNNs), decision tree algorithms (DTAs), and artificial neural networks (ANNs) [24, 25, 26, 27, 28, 29]. However, the handcrafted features need domain knowledge and expertise for the identification of discriminant features. Furthermore, feature engineering techniques, such as dimensionality reduction for discriminant feature selection, result in fault-related information loss. Thus, the conventional methods might not be appropriate for the classification and identification of MDTF defects in the gearbox. In addition, classification algorithms, such as KNNs, SVMs, DTAs, and ANNs, is strongly dependent on the quality of the provided features. To address the above-discussed problems, this paper proposes a scheme of self-generating feature space. The proposed scheme first transforms a low-noise vibration signal into a two-dimensional (2D) image using wavelet transform and obtains WVI's. The WVI's reflect the 2D distributed power spectra of the optimized vibration sub-bands. To obtain fault-related information from the WVI and classify them into their representative classes, the proposed method used DCNA. Deep learning models (DLMs) have been used widely in the areas of finance, natural language processing, and image processing [30, 31, 32, 33]. For condition monitoring of a rotating machine, there exist a variety of DLMs based fault diagnosis frameworks, such as stacked denoising autoencoder [34], recurrent neural network [35], long short term memory (LSTM) networks [36], gated recurrent unit network [37], and convolutional neural network (CNN) [37, 38]. One of the deep learning models, CNN, is a famous model because of its visual understanding [39].