Paper Feed Friction Sound Sensing for Early Jam Prediction
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Solution Overview
Problem
Existing techniques for detecting paper jams in paper feed devices, such as those using ultrasonic transmitters, are inadequate for early detection and prone to errors, making it difficult to estimate the occurrence of paper jams effectively.
Innovation Solution
A paper jam indication estimation device that collects friction sounds using a sound collector and employs a trained machine learning model to estimate the presence or absence of a paper jam, utilizing a passive ultrasonic sensor to identify friction sounds between sheets of paper, thereby preventing jams and paper damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasonic transmitters are used to detect paper lifting, then paper jam detection capability is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary detection function from the ultrasonic system, using a simple ultrasonic receiver without the complex transmitter component. The friction sound detection isolates the essential acoustic information needed for paper jam prediction, eliminating unnecessary system complexity while maintaining detection reliability.
Solution Approach 2:
The patent uses acoustic copying by capturing friction sounds as electrical signals that represent the physical paper feeding process. This acoustic copy allows analysis of paper conditions without direct mechanical intervention, simplifying the detection system while preserving diagnostic accuracy.
2Measurement precision
If friction sound collection is used to estimate paper jams, then detection accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces mechanical paper jam detection methods with acoustic field-based friction sound analysis. By substituting mechanical sensors with acoustic sensing and machine learning analysis, the system achieves higher detection precision while the automated analysis handles the complexity, maintaining ease of operation through intelligent processing.
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing friction sound patterns and predicting paper jams without manual intervention. The system self-adjusts and self-diagnoses paper feeding conditions, improving detection accuracy while requiring minimal operational input from users.
3Productivity
If machine learning models are employed for paper jam estimation, then productivity is improved, but device complexity increases
Solution Approach 1:
The machine learning model is trained in advance with extensive paper feeding data to learn friction sound patterns associated with paper jams. This preliminary training action enables the model to rapidly predict paper jams during actual operation, improving productivity by eliminating the need for real-time complex analysis while the model handles the computational complexity internally.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device accurately predicts paper jams before they occur, reducing the likelihood of jams and minimizing paper damage through a simpler configuration that does not require active ultrasonic transmitters.
Implementation Method 1
a sound collector that collects a friction sound produced when paper is fed into the paper feed device from a holder holding a plurality of sheets of the paper
Implementation Method 2
utilizing a passive ultrasonic sensor to identify friction sounds between sheets of paper
Data Source
AI summary
A paper jam indication estimation device estimates an indication that a paper jam will occur in a paper feed device, and includes: a sound collector that collects a friction sound produced between sheets of paper when the paper is fed into the paper feed device from a holder holding a plurality of sheets of the paper; an estimator that, based on an output result obtained by inputting information pertaining to the friction sound into a trained model that is a machine learning model which has been trained, estimates a presence or absence of an indication that a paper jam will occur in the paper feed device; and an outputter that, when the estimator estimates that the indication that a paper jam will occur is present, outputs, to the paper feed device, a signal that stops the paper from being fed into the paper feed device.