Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence

Abstract

Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis method is proposed from a statistical perspective. The ensemble empirical mode decomposition (EEMD) together with the Hilbert transform is employed to extract 95 time-and frequency-domain features from raw and processed signals. The distance-based evaluation approach is used to select a subset of fault-sensitive features by removing the irrelevant features. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions; whereas the KLID is able to quantify the discrepancy between two probability distributions of a selected feature before and after adding a testing sample. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault modes/damage levels. The effectiveness of the proposed method is demonstrated via the case studies of fault diagnosis for bevel gears and rolling element bearings, respectively. The observations from the case studies show that the proposed method outperforms the support vector machine (SVM)-based and neural network-based fault diagnosis methods in terms of classification accuracy. Additionally, the influences of the number of selected features and the training sample size on the classification performance are examined by a set of comparative studies.

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

“…For more details about HT, please refer to Ref. [20]. The corresponding results are shown in Figure 14.…”

“…The raw vibration data were collected by an accelerometer that was mounted on the top of the gearbox. The data sampling rate was 20 kHz, and the data length was 4096 points [20]. Case 2: The experimental data are from the Case Western Reserve University [21].…”

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

“…As dimensionless indices, skewness, kurtosis, peak indicators, waveform index, pulse index, and margin index can be used to represent rolling bearing fault features. These quantities are widely used in mechanical fault diagnosis [2]. Kullback-Leibler (K-L) divergence is called relative entropy.…”

“…Due to its widespread industrial applications, roller bearing fault diagnosis is critical to prevent catastrophic failure of machines, thereby preventing economic losses [1,2]. Status of rolling element bearings is typically monitored by processing vibration signals [3].…”

Geospatial Big Data-Based Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area

“…According to Zhang et al [29], previous studies have indicated that kernel density function selection does not significantly affect the results; however, bandwidth (h) significantly affects the results, and no perfect measure exists for its determination [30].…”