Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples

Though the existing generative adversarial networks (GAN) have the potential for data augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal with extremely limited training samples and effectively fuse the representative and diverse information. To tackle the above challenges, an improved local fusion generative adversarial network (ILoFGAN) is proposed. Time-domain waveforms are firstly transformed into the time-frequency diagrams to highlight the fault characteristics. Subsequently, a local fusion module is used to fully utilize extremely limited samples and fuse the local features. Finally, a new generator embedded with multi-head attention modules is constructed to effectively improve the accuracy and flexibility of the feature fusion process. The proposed method is applied to the analysis of planetary gearbox vibration signals. The results show that the proposed method can generate a large number of samples with higher similarity and better diversity compared with the existing mainstream GANs using 6 training samples in each type. The generated samples are used to augment the limited dataset, prominently improving the accuracy of the fault diagnosis task

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods

Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images

Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method