New Multifeature Information Health Index (MIHI) Based on a Quasi-Orthogonal Sparse Algorithm for Bearing Degradation Monitoring

• Main Authors: Xiao Zhang, Tengyi Peng, Shilong Sun, Yu Zhou
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2021/2221702

Data-driven intelligent prognostic health management (PHM) systems have been widely investigated in the area of defective bearing signals. These systems can provide precise information on condition monitoring and diagnosis. However, existing PHM systems cannot identify the accurate degradation trend and the current fault types simultaneously. Given that different fault types have various effects on the mechanical system, the corresponding maintenance strategies also vary. Then, choosing the appropriate maintenance strategy according to the future fault type can reduce the maintenance cost of the equipment operation. Therefore, a multifeature information health index (MIHI) must be developed to trace various bearing degradation trends with various types of faults simultaneously. This paper reports a new quasi-orthogonal sparse project algorithm that can mutually convert the degraded processing feature vector sets (such as spectrum) for each type of fault to orthogonal approximate spatial straight lines. The algorithm builds a MIHI through the spectrum of current state measured points. The MIHI is then transformed by a quasi-orthogonal sparse project algorithm to trace the various bearing degradation trends and recognize the fault type simultaneously. The case study of bearing degradation data demonstrates that this approach is effective in assessing the various degradation trends of different fault types.