A Data-Driven Fault Diagnosis Method Using Modified Health Index and Deep Neural Networks of a Rolling Bearing

To improve fault diagnosis accuracy, a data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) was proposed. This model can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of the product more accurately. To achieve this goal, we first used the modified ensemble empirical mode decomposition (MEEMD) scalar index to capture the bearing condition; then, by using the key intrinsic mode function (IMF) extracted by AMTS as the input of classifier, the optimized properties of bearing is decomposed and extracted effectively. Next, to improve the accuracy of the fault diagnosis, we tested different modes, employing the modified health index (MHI), which is designed to overcome the shortcomings of the proposed health index as a classifier in a single fault mode and the deep neural networks (DNNs) as a classifier in a multifault mode. To evaluate the effectiveness of our model, the Case Western Reserve University (CWRU) bearing data were used for verification. Results indicated a strong robustness with 99.16% and 1.09s, 99.86% and 6.61s fault diagnosis accuracy in different data modes. Furthermore, we argue that this data-driven fault diagnosis obviously lowers the maintenance cost of complex systems by significantly reducing the inspection frequency and improves future safety and reliability.