Camera-Based Predictive Monitoring for Multi-Failure Machine Parts
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Solution Overview
Problem
Current machine maintenance methods, including periodic servicing and sensor-based predictive maintenance, often result in unnecessary visits and late detection of problems, leading to increased costs and potential failures.
Innovation Solution
A system and method utilizing a camera to capture images of machine parts, employing engines to identify failure modes and interrelationships, allowing for predictive maintenance actions to be taken before failures occur, thereby reducing unnecessary maintenance and early detection of hazardous situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic scheduled servicing is performed based on statistical data, then machine safety and functionality are maintained, but unnecessary maintenance visits and part replacements occur, increasing costs
Solution Approach 1:
The system performs preliminary detection of failure modes before they manifest as actual failures. By using computer vision to identify early signs of deterioration, the system enables maintenance to be scheduled based on actual device condition rather than predetermined intervals, preventing unnecessary part replacements while ensuring safety.
Solution Approach 2:
The monitoring system enables the device to essentially monitor itself for failure conditions. The computer vision system automatically detects failure modes and triggers maintenance alerts, eliminating the need for manual inspection and allowing maintenance to be performed only when actually needed, thus reducing unnecessary part replacements.
2Reliability
If frequent scheduled maintenance is performed with safety margins, then machine safety is ensured, but maintenance frequency increases, leading to higher costs and operational disruption
Solution Approach 1:
The system transitions from static, predetermined maintenance schedules to dynamic, condition-based maintenance scheduling. The maintenance interval automatically adjusts based on real-time detection of failure modes, allowing the system to extend intervals when devices are healthy and shorten them when deterioration is detected, thereby optimizing both safety and productivity.
Solution Approach 2:
The system implements continuous feedback through computer vision monitoring that detects failure modes and feeds this information back to the maintenance scheduling system. This closed-loop control enables maintenance decisions to be based on actual device condition rather than fixed schedules, reducing unnecessary maintenance visits while ensuring safety when needed.
3Measurement precision
If traditional sensors are used for predictive maintenance, then some failures can be detected earlier, but the system complexity increases and not all failure modes can be detected
Solution Approach 1:
The system replaces traditional mechanical and electrical sensors with an optical-based computer vision system. Instead of using multiple specialized sensors to detect different failure modes, the system uses cameras and image processing algorithms to visually identify failure conditions, reducing system complexity while expanding detection capabilities to cover multiple failure modes simultaneously.
Solution Approach 2:
The computer vision system serves multiple detection functions through a single unified platform. The same camera system can detect various types of failure modes across different device components by analyzing visual characteristics, eliminating the need for multiple specialized sensors and reducing overall system complexity while maintaining high detection accuracy.
4Ease of operation
If manual inspection and routine checks are performed, then maintenance can be scheduled based on experience, but labor costs increase and emergency situations may still be missed
Solution Approach 1:
The system enables automatic self-monitoring of device condition through computer vision technology. The system continuously detects failure modes and automatically generates maintenance alerts without requiring manual inspection, eliminating labor costs while ensuring that emergency situations are detected immediately, thus preventing downtime.
Solution Approach 2:
The computer vision monitoring system operates continuously without interruption, providing constant surveillance of device condition. Unlike periodic manual inspections that create gaps in detection, the automated system maintains continuous monitoring, ensuring that failure modes are detected immediately upon occurrence, thereby eliminating downtime caused by missed emergency situations.
Data Source
AI summary
A system, method and computer program product, the method comprising: receiving an image depicting two or more parts of a monitored device, a first part subject to a first failure mode, and a second part subject to a second failure mode; identifying in the image the first and second parts; detecting whether the first part is assumed to comply with the first failure mode, comprising using a first engine, and whether the second part is assumed to comply with the second failure mode, comprising using a second engine; verifying whether the first part complies with the first failure mode or not, and verify whether the second part complies with the second failure mode or not; and taking an action subject to the first part complying with the first failure mode or the second part complying with the second failure mode, the action aimed at avoiding a malfunction of the device.