Author
Peng Zhou
Bio: Peng Zhou is an academic researcher from Anhui University. The author has contributed to research in topics: Filter (signal processing) & Bearing (mechanical). The author has an hindex of 2, co-authored 3 publications receiving 83 citations.
Papers
More filters
TL;DR: A new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique is investigated.
69 citations
TL;DR: In this article, the authors proposed a new synthetic quantitative index (SQI) via a back propagation neural network to guide the adaptive parameter selection of the stochastic resonance (SR) procedure.
55 citations
TL;DR: An improved fully connected layer (FC) layer in the multilayer perceptron model that can be used for rolling bearing fault diagnosis and verifies and compares the effects of different transformation methods of convolutional neural networks in each alternative module on small sample diagnosis and noise immunity diagnosis of rolling bearings.
2 citations
Patent•
22 Feb 2017
TL;DR: In this paper, a weak signal detection method based on a self-adaptive stochastic resonance filter was proposed, where 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 signal 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 input signal and the SQI value of the output signal.
Abstract: 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.
2 citations
Cited by
More filters
TL;DR: The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.
283 citations
TL;DR: This study is committed to providing a comprehensive review of SR from history to state-of-the-art methods and finally to research prospects, along with the applications in rotating machine fault detection.
252 citations
TL;DR: The proposed methods had good results for diagnosis of bearing, stator and rotor faults of the single-phase induction motor and can find applications for fault diagnosis of other types of rotating machines.
247 citations
TL;DR: The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach, and significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length.
Abstract: 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.
239 citations
TL;DR: Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge.
221 citations