A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery

Abstract

Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal data from the rotating machinery is transformed into time–frequency feature 2-D image data by a continuous wavelet transform and used for fault classification with the neural network method. The adaptive deep convolution neural network (ADCNN) is then combined with the generative adversarial networks (GANs) to improve the performance of the feature self-learning ability from input data. Compared with different fault diagnosis methods, the proposed method has better performance for image feature classification in rotating machinery.

A review: the application of generative adversarial network for mechanical fault diagnosis

“…Meanwhile, many popular generative models based on GAN including DCGAN, ACGAN, CGAN, and their respective variants have been widely applied in the field of fault diagnosis. For example, Gao et al [116] proposed an enhanced DCGAN model that integrated DCGAN and adaptive deep CNN, aiming to enhance the features self-learning capability for the input data and achieve the high-quality augmentation of CWT images. Ye and Liu [117] proposed a semi-supervised CNN fault diagnosis framework, named GAN-MRFCD-SSCNN, which is based on GAN and manifold-regularized fuzzy clustering discriminant augmented semi-supervised CNN to tackle the challenge posed by limited labeled samples in mechanical fault diagnosis.…”