Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Abstract

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

“…Guo et al [17,18] Input[32 seconds. Seven additional outer race faults were assessed at varying loads: 25, 50, 100, 150, 200, 250, and 300 lbs.…”

“…Since 2015, deep learning methodologies have been applied, with success, to diagnostics or classification tasks of rolling element signals [2,[16][17][18][19][20][21][22][23][24][25][26]. Wang et al [2] proposed the use of wavelet scalogram images as an input into a CNN to detect faults within a set of vibration data.…”

“…While these data sets did not involve rolling elements, the feature maps were time-based, therefore allowing the piecewise remaining useful life estimation. Guo et al [18] incorporated traditional feature construction and extraction techniques to feed a stacked autoencoder (SAE) deep neural network. SAEs do not utilize convolutional and pooling layers.…”

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

“…A harmonic signal y(t) is defined as follows: (6) where A and θ are the amplitude and phase of y(t), respectively; fr denotes the rotational frequency.…”

“…Additionally, with recent developments in both industry and the Internet, data acquisition has exponentially increased. Thus, fault diagnosis has entered the era of Big Data [5][6][7]. Because mechanical big data is typically characterized as large-volume, diverse, and of high-velocity [8], methods of extracting features rapidly and accurately from mechanical big data has become an urgent subject of research [9,10].…”