Abstract
Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.
Highlights
Fault diagnosis is one of the hot topics in many fields, including machinery, power chemical engineering, aviation and space
The normal, inner race fault, outer race fault and rolling element fault vibration data under four different conditions are equivalently represented by recurrence plot (RP) and 20 sets of vibration data which contains 1000 points are selected for each fault mode under each condition
This study proposes a fault diagnosis method for rolling bearings under variable conditions This study proposes a fault diagnosis method for rolling bearings under variable conditions based on visual cognition
Summary
Fault diagnosis is one of the hot topics in many fields, including machinery, power chemical engineering, aviation and space. Failures of rolling bearings, which are the most widely used bearings in industry, may cause unexpected machine breakdowns and result in economic loss, which is an issue that has attracted considerable attention of specialists and scholars [2]. Various fault diagnosis have been proposed, including expert systems, spectrum analysis, fuzzy logic, statistical processing and so on [3,4]. Among these different processing techniques, vibration-based measurement is the most widely employed due to its high correlation with structure dynamics [5,6], and the vibration signals contain a wealth of information of the bearings. The working environment of rolling bearings is generally tough, complex, and especially variable, Materials 2017, 10, 582; doi:10.3390/ma10060582 www.mdpi.com/journal/materials
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