A new methodology for condition monitoring based on perceptual hashing

摘要

A common problem that machine condition monitoring encounters is the high data throughput and bandwidth consumption, especially for wave signals sampled with high frequency. In order to reduce the data dimension while retrieve much diagnostic information from the monitored data, a new methodology is proposed based on perceptual hashing, which can take the respective advantage of Cloud computing and Edge computing. The feasibility of this methodology is discussed and demonstrated through a naive perceptual hashing example with bearing vibration signals. Considerations in perceptual hashing of monitored data and modeling for performance assessment, diagnosis and prognosis are further discussed. With perceptual hashing, not only the data dimension can be reduced, but also the diagnostic modeling can benefit from the geometric structure of machine condition hash (MCH).