2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Abstract

Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification.

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps

“…For 2D-CNN models, vibration data cannot be used in their raw form. Rather, they are initially converted into time-frequency image representations such as spectrograms [38][39][40], scalograms [40,41] or other types of vibration images [42]. Then these images which represent the vibration signal in image form are used as input in 2D-CNN model.…”

“…In another study Pham et al presented a fault classification method under different rotational speeds between 250 rpm and 500 rpm. Spectrogram images and CNN were utilized [38].…”

Acoustic Signal Classification Using Symmetrized Dot Pattern and Convolutional Neural Network

“…They established a historical wind power database using CNN and collected long-term weather data to estimate short-term wind power using the wind power curve. In 2021, Pham et al [22] used CNN to determine the type of fault classification in the bearing, because the bearing is a component that supports rotation. When the working state of the bearing and its fault state can be classified accurately, it can effectively solve the complex fault diagnosis problem.…”

A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

“…CNN is a neural network that mimics the mammalian visual system [19]. Since the design concept of CNN possesses three key architectural ideas: local receptive fields, weight sharing, and pooling in spatial domain, CNN is suitable for the recognition of two-dimensional visual data [20,21]. As a result, CNN and its variants are extensively exploited in the image classification topic [22,23].…”