Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

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.

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Gearbox Fault Identification and Classification with Convolutional Neural Networks

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

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An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

Condition-based monitoring (CBM) is widely used methodology for the fault diagnosis, which provides the analysis for the safe and proper operations of any device or element. Vibration analysis is most accurate and reliable technique of CBM to reveal the condition of device or element. In this technique, fault can be diagnosed by analysing the vibration data acquired from accelerometer. Convolutional Neural Network (CNN) has emerged as one of the most widely used methodology in application of pattern recognition and acoustic data analysis. In this paper, CNN is used as back-end classifier for bearing fault detection. Vibration data is collected for three different conditions of bearings i.e. normal condition, inner race fault and outer race fault. Statistical features are extracted from vibration data and used as input to CNN classifier. Convolution filters are learned by training CNN and are used to detect the unique features for each condition of bearing. The obtained accuracy shows that CNN is very reliable and effective technique for bearing fault diagnosis. It exhibits good performance compared to peer algorithms.

Bearing fault identification and classification with convolutional neural network

We propose a model for a system with middle temporal neurons and medial superior temporal (MST) neurons by using a three-layered autoencoder. Noise effect is taken into account by using the framework of statistical physics. We define a cost function of the autoencoder, from which a learning rule is derived by a gradient descent method, within a mean-field approximation. We find a pair of values of two noise levels at which a minimum value of the cost function is attained. We investigate response properties of the MST neurons to optical flows for various types of motion at the pair of optimal values of two noise levels. We obtain that the response properties of the MST neurons are similar to those obtained from neurophysiological experiments.

Masataka Ando

Papers

Model of MT and MST areas using an autoencoder

Models of MT and MST areas using wake-sleep algorithm