Abstract
This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.
Highlights
As modern industries inevitably utilize a wide range of rotating machinery, the imperative of securing its safety during the service life has escalated significantly
This study presents a comparative study on the condition monitoring of roller bearings through signal processing and optimization techniques
This study suggests and compares two different signal processing techniques (MED and the Teager-Kaiser Energy Operator (TKEO)), and their combinations, to enhance the resolution of kurtosis, for differentiating the condition of roller-bearing in terms of kurtosis
Summary
As modern industries inevitably utilize a wide range of rotating machinery, the imperative of securing its safety during the service life has escalated significantly. Kaiser [19] applied the TKEO to single time varying signals, for simultaneous modulation of amplitude and frequency This signal conditioning method has shown successful results in fault detection of a rotating machinery [20,21]. The GA optimized the weight of IMFs to improve the image quality, through minimizing the entropy of the pixel This concept can be applied to the bearing fault detection by finding an optimal set of weights to maximize the differences of kurtosis value between the healthy and damaged bearing condition. The study compares the kurtosis value of the measured vibration signals using a fused optimization tool, i.e., a GA combined with EMD [25], to enhance the sensitivity of defects in a roller-bearing. EMD is combined with GA, to optimize the weight of each IMF, to maximize the resulting kurtosis values
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