摘要：Abstract A novel degradation assessment index (DAI) for the detection of slow speed bearing faults was modelled using an integrated approach. Data was obtained from a slow speed bearing test rig under variable operational conditions of speed and load. Incipient damage was detected under changing operating conditions. The proposed PKPCA-GMM-EWMA model from the integration of polynomial kernel principal component analysis (PKPCA), a Gaussian mixture model (GMM) and an exponentially weighted moving average (EWMA) is found to be useful in the detection of slow speed bearings faults under variable operating conditions of speed and load.
