P
Peng Zhou
Researcher at Anhui University
Publications - 4
Citations - 128
Peng Zhou is an academic researcher from Anhui University. The author has contributed to research in topics: Fault (power engineering) & Filter (signal processing). The author has an hindex of 2, co-authored 3 publications receiving 83 citations.
Papers
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Journal ArticleDOI
Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network
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.
Journal ArticleDOI
Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis
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.
Journal ArticleDOI
Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network
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.
Patent
Weak signal detection method based on self-adaptive stochastic resonance filter
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.