Vibration-based gearbox fault diagnosis using deep neural networks

Vibration-based analysis is the most commonly used technique to monitor the condition of gearboxes. Accurate classification of these vibration signals collected from gearbox is helpful for the gearbox fault diagnosis. In recent years, deep neural networks are becoming a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. In this paper, a study of deep neural networks for fault diagnosis in gearbox is presented. Four classic deep neural networks (Auto-encoders, Restricted Boltzmann Machines, Deep Boltzmann Machines and Deep Belief Networks) are employed as the classifier to classify and identify the fault conditions of gearbox. To sufficiently validate the deep neural networks diagnosis system is highly effective and reliable, herein three types of data sets based on the health condition of two rotating mechanical systems are prepared and tested. Each signal obtained includes the information of several basic gear or bearing faults. Totally 62 data sets are used to test and train the proposed gearbox diagnosis systems. Corresponding to each vibration signal, 256 features from both time and frequency domain are selected as input parameters for deep neural networks. The accuracy achieved indicates that the presented deep neural networks are highly reliable and effective in fault diagnosis of gearbox

Gearbox Fault Identification and Classification with Convolutional Neural Networks

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

Metodología para la ubicación del sensor de emisión acústica en una caja de engranajes

The purpose of this document is to identify the best position of an acoustic emission sensor in a gearbox for fault detection. The acoustic emission sensor was located in the four side walls of the gearbox, where three signals were acquired in each position to be analyzed in the time domain by condition indicators and finally the best signals were analyzed in the frequency do-main by means of the gear step frequency. As a result, it was obtained that the sensor must be positioned in the shorter length lateral parts of the gearboxO objetivo deste documento é identificar a melhor posição de um sensor de emissão acústica em uma caixa de engrenagens para a detecção de falhas. O sensor de emissão acústica foi lo-calizado nas quatro paredes laterais da caixa de transmissão, onde três sinais foram adquiridos em cada posição para serem analisados no domínio do tempo por indicadores de condição e, finalmente, os melhores sinais foram analisados no domínio da frequência por meio da frequên-cia de passo da engrenagem. Como resultado, foi obtido que o sensor deve ser posicionado nas partes laterais do menor comprimento da caixa de engrenagens.Este documento tiene como objetivo identificar la mejor posición de un sensor de emisión acús-tica en una caja de engranajes para la detección de fallos. El sensor de emisión acústica fue localizado en las cuatro paredes laterales de la caja de engranajes, donde se adquirió tres se-ñales en cada posición para ser analizadas en el dominio del tiempo mediante indicadores de condición y finalmente las mejores señales fueron analizadas en el dominio de la frecuencia por medio de la frecuencia de paso de engranajes. Como resultado se obtuvo que el sensor debe ser posicionado en las partes laterales de menor longitud de la caja de engranajes