US 11,237,539 B2 · System and method for operational-data-based detection of anomaly of a machine tool

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.

Claims20

Dependent claims

A system for operational-data-based detection an anomaly of a machine tool, comprising:

a server comprising a processor and configured to execute code, comprising:
a stream module configured to collect a stream of operational data comprising positional parameters and movement parameters of machining processes performed by a machine tool;
a characterization module configured to characterize distribution patterns of the operational data using a plurality of analytical methods;
a collection module configured to collect further operational data comprising further positional parameters and further movement parameters of machining processes performed by the machine tool;
a comparison module configured to compare the further operational data against the distribution patterns of the operational data;
a determination module configured to determine a presence of one or more outliers against the distribution patterns based on the comparison;
a threshold module configured to compare a number of the outliers to a threshold;
a notification module configured to send a notification if the number of the outliers exceeds the threshold, wherein maintenance of the machine tool is performed based on the notification.

5 more elements

A method for operational-data-based detection of an anomaly of a machine tool, comprising:

collecting by a server a stream of operational data comprising positional parameters and movement parameters of machining processes performed by a machine tool;
characterizing by the server distribution patterns of the operational data using a plurality of analytical methods;
collecting by the server further operational data comprising further positional parameters and further movement parameters of machining processes performed by the machine tool;
comparing by the server the further operational data against the distribution patterns of the operational data;
determining by the server a presence of one or more outliers against the distribution patterns based on the comparison;
comparing by the server a number of the outliers to a threshold;
and
sending by the server a notification if the number of the outliers exceeds the threshold, wherein maintenance of the machine tool is performed based on the notification.

5 more elements

Dependencies are inferred from the claim text.

System and method for operational-data-based detection of anomaly of a machine tool

Life of this patent

System and method for operational-data-based detection of anomaly of a machine tool

Life of this patent

Abstract

Claims20