Gearbox Fault Identification and Classification with Convolutional Neural Networks

By Zhiqiang Chen, Chuan Li, Réne-Vinicio Sánchez. 2015

407 citations recorded by the source corpus.

Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/63166c408b504fe60cd7856872d8f4ecbaf1416a

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In Control and Systems Engineering

In Mechanics of Materials

Citations in the corpus

Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes (2018, background): “Deep belief networks (DBNs), deep auto-encoders (DAEs), and convolutional NNs (CNNs) are popular deep learning methods used for various fault diagnosis applications in recent years [7]–[14].”
A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes (2016, methodology): “[8] conducted a fault pattern diagnosis of gearboxes using the statistical measures and spectral information collected from time and frequency domains as the CNN input data.”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “[61] calculated statistical measures of vibration signals from the time and frequency domains as the model input and then applied one-layer convolutional network for fault identification of bearings and gears.”
An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “used CNN for gearbox fault identification and classification [22].”
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox (2017, methodology): “Root mean square (RMS), kurtosis, crest factor, sk wness, mean, minimum, ma imum, and vari ce are chosen as the handcraft features in the time domain [34,37,42].”
Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings (2017, methodology): “Since 2015, deep learning methodologies have been applied, with success, to diagnostics or classification tasks of rolling element signals [2, 16–26].”
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, neutral): “In [26], Chen et al.”
Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks (2018, background): “[24] proposed a CNN-based approach that achieved high fault diagnosis accuracy for gearbox with vibration signals.”