Carriage Motion Monitoring for Predictive Maintenance in Image Forming
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Solution Overview
Problem
Existing image forming systems fail to predict when maintenance is required, leading to unexpected downtime due to operational abnormalities such as motor deterioration, ink soiling, and component wear.
Innovation Solution
An image forming system that includes a recording head, carriage moving mechanism, motor driver, encoder, and controller, which uses encoder signals to monitor carriage velocity and identify index values for predicting the end of service life based on time series data, allowing for timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system maintenance is performed based on known techniques, then the system can be continuously used, but the down time occurs or is prolonged because failure time cannot be predicted
Solution Approach 1:
The system performs preliminary diagnosis by monitoring vibration signals and calculating diagnostic values before actual failure occurs. The controller accumulates diagnostic values over time and compares them against thresholds to predict future failures, enabling maintenance to be performed proactively rather than reactively, thus preventing downtime.
Solution Approach 2:
The system implements continuous feedback monitoring of the carriage motor's vibration signals through the encoder. The controller constantly calculates diagnostic values from these signals and uses them to determine the motor's health status, creating a closed-loop system that provides real-time information about component degradation and predicts failure before it occurs.
2Difficulty of detecting and measuring
If frequency analysis is performed in real time to diagnose failures, then operational abnormalities can be detected, but the failure time prediction capability is insufficient
Solution Approach 1:
The system performs preliminary diagnosis by monitoring vibration signals and calculating diagnostic values before actual failure occurs. The controller accumulates diagnostic values over time and compares them against thresholds to predict future failures, enabling maintenance to be performed proactively rather than reactively, thus preventing downtime.
Solution Approach 2:
The system transitions from simple presence/absence detection of abnormalities to a multi-dimensional assessment by accumulating diagnostic values over time periods. This temporal dimension allows the system to track degradation trends and predict failure timing, transforming static detection into dynamic prediction.
Data Source
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AI summary
An image forming system (1) including: a recording head (20); a carriage moving mechanism (30) including a carriage (31), a motor (39), and a transmission (33); a motor driver (70); an encoder (40); and a controller (51) is provided. The controller is configured to: identify, regarding a certain period being each of the plurality of periods, an index value regarding abnormality of the image forming system based on time series data of a moving velocity, a physical amount related to the moving velocity, or an operation amount observed in a moving control of the carriage in the certain period; and determine an end of a service life of the image forming system or a remaining amount of the service life of the image forming system based on a change of the index value over the plurality of periods.