https://escholarship.org/content/qt4999765n/qt4999765n.pdf?t=psy2c4

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

A review of stochastic resonance in rotating machine fault detection

Fault diagnosis of single-phase induction motor based on acoustic signals

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.

Deep residual learning-based fault diagnosis method for rotating machinery

Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network

The invention discloses a weak signal detection method based on a self-adaptive stochastic resonance filter. According to the detection method, a sensor is installed on a bearing to be detected to acquire the vibration signals of the bearing, and then envelope demodulation is performed on the vibration signals so that the input signals Z[n] of the filter are obtained; and the filter parameters are adjusted by using a genetic algorithm to filter the Z[n] and the SQI value of the output signals is calculated and optimized. The detection method has the following advantages that firstly, self-adaptive enhancement of the bearing fault weak signals can be realized under the condition that the fault frequency is unknown; secondly, the detection method has a great filtering effect so that the high-frequency and low-frequency noise interference can be simultaneously filtered; and thirdly, the computing speed can be enhanced by the genetic algorithm so as to enhance the efficiency of bearing fault diagnosis.

Peng Zhou

Papers

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network

Weak signal detection method based on self-adaptive stochastic resonance filter