Surgical Robot Predictive Maintenance Using Failure Forecasting

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Solution Overview

Problem

Robotically assisted surgical systems face challenges in predicting and preventing mechanical failures and degradations, which can impact the scheduling and outcome of medical procedures.

Innovation Solution

A predictive maintenance system utilizing machine learning techniques to analyze operational data and predict future failures, determining if the likelihood meets an action threshold to initiate preventative maintenance actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If mechanical components are used in robotically assisted surgical systems, then the system can perform complex medical procedures, but the components are prone to degrading or failing over time

Engineering Contradiction:

Improvecapability to perform complex medical proceduresVSAvoidcomponent failure rate

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary maintenance actions by predicting future failures using machine learning models analyzed from operational data, kinematics data, and sensor information. The predictive maintenance module identifies components likely to fail and schedules maintenance before actual failure occurs, preventing disruptions to complex medical procedures while maintaining component reliability

Inventive Principle:

Principle #10Preliminary action

2Productivity

If traditional maintenance scheduling is used, then maintenance activities can be planned, but failures can still occur between scheduled maintenance intervals

Engineering Contradiction:

Improvemaintenance scheduling efficiencyVSAvoidfailure prevention capability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements continuous feedback by monitoring operational data, sensor readings, and system performance in real-time. The predictive maintenance module uses machine learning models to analyze this feedback data and adjust maintenance scheduling dynamically, transitioning from fixed interval maintenance to condition-based predictive maintenance that responds to actual component states

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-diagnosis and self-monitoring through automated data collection from sensors and operational logs. The machine learning models enable the system to autonomously identify degradation patterns and predict failures without external intervention, allowing the surgical robot to service its own maintenance needs proactively

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250072979A1Predictive Maintenance for Robotically Assisted Surgical System

Publication Date: 2025.03.06 AURIS HEALTH INC

AI summary

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.