EP-4734873-A1 - PREDICTIVE MAINTENANCE FOR ROBOTICALLY ASSISTED SURGICAL SYSTEM

Abstract

A robotically assisted surgical system includes a robot and various control systems for facilitating assistance with a medical procedure. A predictive maintenance module obtains various operational data associated with the robot and applies a machine learning model trained to predict failures or degradations, classify a health state of the robot, and/or detect anomalous conditions that may be indicative of a future failure. The predictive maintenance module may invoke various actions in response to inferences generated by the machine learning model, such as generating notifications, generating messages to a connected software platform, and/or initiating automated actions associated with the operation of the robot.

Inventors

• GOLDADE, Anton Viktorovich
• KOCHMAN, Matthew

Assignees

• Auris Health, Inc.

Dates

Publication Date

20260506

Application Date

20240828

Claims (20)

1. 1. A method for predicting maintenance activities in a robotically assisted surgical system, the method comprising: obtaining operational data associated with operation of the robotically assisted surgical system; applying a machine learning model to the operational data to predict a likelihood of a future failure event in an absence of a maintenance action; determining if the likelihood meets an action threshold; responsive to the likelihood meeting the action threshold, generating action data indicative of a preventative maintenance action item predicted to counteract the future failure event; and outputting the action data.
2. 2. The method of claim 1, wherein the machine learning model is trained according to an unsupervised learning approach with respect to historical operations to learn characteristics of anomalous operation.
3. 3. The method of claim 1, wherein the machine learning model is trained according to a supervised learning approach to learn relationships between a set of training operational data obtained from historical operations and failure events occurring in the historical operations.
4. 4. The method of claim 1, wherein the operational data include at least one of: a power input to a motor of the robotically assisted surgical system, a rotational velocity of the motor, a linear velocity of a component of the robotically assisted surgical system, a displacement of the component of the robotically assisted surgical system, a force applied by the component of the robotically assisted surgical system, a count of brake actuations, an error code issued by the robotically assisted surgical system, a fault rate associated with the robotically assisted surgical system, a log file associated with the robotically assisted surgical system.
5. 5. The method of claim 1, wherein the operational data comprise at least one time-based data series representing a monitored parameter value over a time period.
6. 6. The method of claim 1, wherein generating the action data comprises outputting a notification for display on an output device.
7. 7. The method of claim 1, wherein generating the action data comprises outputting an application programming interface (API) message to trigger an action in a platform connected to the robotically assisted surgical system.
8. 8. The method of claim 1, wherein generating the action data comprises initiating an automated remedial action associated with the robotically assisted surgical system.
9. 9. The method of claim 1, wherein generating the action data comprises recommending an on-demand maintenance activity independent of a scheduled maintenance plan.
10. 10. A non-transitory computer-readable storage medium storing instructions for predicting maintenance activities in a robotically assisted surgical system, the instructions when executed by a processor causing the processor to perform steps including: obtaining operational data associated with operation of the robotically assisted surgical system; applying a machine learning model to the operational data to predict a likelihood of a future failure event in an absence of a maintenance action; determining if the likelihood meets an action threshold; responsive to the likelihood meeting the action threshold, generating action data indicative of a preventative maintenance action item predicted to counteract the future failure event; and outputting the action data.
11. 11. The non-transitory computer-readable storage medium of claim 10, wherein the machine learning model is trained according to an unsupervised learning approach with respect to historical operations to learn characteristics of anomalous operation.
12. 12. The non-transitory computer-readable storage medium of claim 10, wherein the machine learning model is trained according to a supervised learning approach to learn relationships between training operational data obtained from historical operations and failure events occurring in the historical operations.
13. 13. The non-transitory computer-readable storage medium of claim 10, wherein the operational data include at least one of: a power input to a motor of the robotically assisted surgical system, a rotational velocity of the motor, a linear velocity of a component of the robotically assisted surgical system, a displacement of the component of the robotically assisted surgical system, a force applied by the component of the robotically assisted surgical system, a count of brake actuations, an error code issued by the robotically assisted surgical system, a fault rate associated with the robotically assisted surgical system, a log fde associated with the robotically assisted surgical system.
14. 14. The non-transitory computer-readable storage medium of claim 10, wherein the operational data comprise at least one time-based data series representing a monitored parameter value over a time period.
15. 15. The non-transitory computer-readable storage medium of claim 10, wherein generating the action data comprises outputting a notification for display on an output device.
16. 16. The non-transitory computer-readable storage medium of claim 10, wherein generating the action data comprises outputting an application programming interface (API) message to trigger an action in a platform connected to the robotically assisted surgical system.
17. 17. The non-transitory computer-readable storage medium of claim 10, wherein generating the action data comprises initiating an automated remedial action associated with the robotically assisted surgical system.
18. 18. A robotically assisted surgical system comprising: a robot for facilitating assistance associated with a medical procedure; a processor; and a non-transitory computer-readable storage medium storing instructions for predicting maintenance activities in a robotically assisted surgical system, the instructions when executed by the processor causing the processor to perform steps including: obtaining operational data associated with operation of the robot; applying a machine learning model to the operational data to predict a likelihood of a future failure event in an absence of a maintenance action; determining if the likelihood meets an action threshold; responsive to the likelihood meeting the action threshold, generating action data indicative of a preventative maintenance action item predicted to counteract the future failure event; and outputting the action data.
19. 19. The robotically assisted surgical system of claim 18, wherein the machine learning model is trained according to an unsupervised learning approach with respect to historical operations to learn characteristics of anomalous operation.
20. 20. The robotically assisted surgical system of claim 18, wherein the machine learning model is trained according to a supervised learning approach to learn relationships between training operational data obtained from historical operations and failure events occurring in the historical operations.

Description

PREDICTIVE MAINTENANCE FOR ROBOTICALLY SSISTED SURGICAL SYSTEM BACKGROUND TECHNICAL FIELD [0001] The described embodiments relate to a system and a method for facilitating predictive maintenance activities in a robotically assisted surgical system. DESCRIPTION OF THE RELATED ART [0002] Robotically assisted surgical systems may be employed to provide critical assistance for a variety of surgical procedures. Such systems may incorporate advanced imaging or other sensing capabilities and precision instrument control systems capable of facilitating complex medical procedures. Such systems may rely on various mechanical components, sensing devices, and control systems that may be prone to degrading or failing over time. Such degradations or failures can impact the scheduling and/or outcome of medical procedures. BRIEF DESCRIPTION OF THE DRAWINGS [0003] Figure (FIG.) 1 is an example embodiment of a medical environment for a robotically assisted surgical system. [0004] FIG. 2 is a block diagram illustrating an example embodiment of a predictive maintenance module for a robotically assisted surgical system. [0005] FIG. 3 is a flowchart illustrating an example embodiment of a process for operating a predictive maintenance module for a robotically assisted surgical system. [0006] FIG. 4 is a visual representation of a technique for classifying operating states of a robot of a robotically assisted surgical system for facilitating predictive maintenance decisions. [0007] FIG. 5 is a diagram illustrating example output actions associated with predictive maintenance decisions of a robotically assisted surgical system. SUMMARY [0008] 1. A robotically assisted surgical system determines predictive maintenance actions using machine learning techniques. Input data is obtained (which may include operational data, kinematics data, or other information) that are associated with operation of the robotically assisted surgical system. A machine learning model is applied to the input data to predict a likelihood of a future failure event that may occur in an absence of a maintenance action. The robotically assisted surgical system determines if the likelihood meets an action threshold for taking an action relaying to predictive maintenance. Responsive to the likelihood meeting the action threshold, the robotically assisted surgical system determines action data indicative of a preventative maintenance action predicted to counteract the future failure event, and outputs the action data. [0009] In some embodiments, the machine learning model may be trained according to an unsupervised learning approach with respect to historical operations to learn characteristics of anomalous operation. In other embodiments, the machine learning model is trained according to a supervised learning approach to learn relationships between training data (e.g., operational data, kinematics data, etc.) obtained from historical operations and failure events occurring in the historical operations. [0010] In an embodiment, the training and/or input data may relate to sensed operational data include at least one of: a power input to a motor of the robotically assisted surgical system, a rotational velocity of the motor, a linear velocity of a component of the robotically assisted surgical system, a displacement of the component of the robotically assisted surgical system, a force applied by the component of the robotically assisted surgical system, a count of brake actuations, an error code issued by the robotically assisted surgical system, a fault rate associated with the robotically assisted surgical system, a log fde associated with the robotically assisted surgical system. The set of operational data may furthermore comprise at least one time-based data series representing a monitored parameter value over a time period. [0011] In various instances, generating the action data may comprise, for example, outputting a notification for display on an output device, outputting an application programming interface (API) message to trigger an action in a platform connected to the robot, and/or initiating an automated remedial action associated with the robot. [0012] In further embodiments, a non-transitory computer-readable storage medium stores instructions executable by a processor for performing any of the methods described above. The methods may be employed in a surgically assisted robotic system including one or more robots and associated electronics for assisting various medical procedures. DETAILED DESCRIPTION [0013] The Figures (FIGS.) and the following description describe certain embodiments by way of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. Reference will now be made to several embodiments, examples of which are illustrated in the accompanying fig