System and method for operational-data-based detection of anomaly of a machine tool
US11237539B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 7, 2020 |
| Grant date | Feb 1, 2022 |
| Priority date | — |
| Expiry date | Aug 7, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/50185
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.