Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy

Most existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes a multistage fault detection scheme for rolling bearings. In the first stage, the sensitivity of the range entropy to bearing failure is used to define a threshold, based on which the health status of the bearing is judged. If the unknown bearing is judged to be faulty, the next stage is implemented. In the second stage, a fault feature extraction method based on dual-tree complex wavelet packet transform (DTCWPT), time-shifted multiscale range entropy (TSMRE), and t-distributed stochastic neighbor embedding (t-SNE) is proposed, and a random forest (RF) discriminator is used for fault classification. To achieve the desired performance of fault classification, a new coarsening approach for complexity measurement called TSMRE is developed on the basis of the range entropy (RE). First, the RE value of each time-shifted coarse-grained time series is calculated, and the TSMRE is obtained by averaging the entropy values. The TSMRE improves the coarse-graining processing of the MRE and enhances the stability and reliability of the algorithm. In addition, it can obtain more information from short time series using the time-shifted coarse-grained technology. Therefore, it is less dependent on the length of the original time series. Two sets of rolling bearing data are used for this experiment. The fault recognition rate of each category of samples is 100%. Therefore, the proposed multistage fault diagnosis method can pre-screen healthy bearings and accurately identify the failure types of faulty bearings.