Abnormal Sound Diagnosis for Image Forming Unit Replacement
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Solution Overview
Problem
Existing systems struggle to detect and identify unknown abnormal sounds in image forming apparatuses, such as copying machines or laser printers, which can indicate the need for replacement of worn parts, leading to user discomfort and potential failure.
Innovation Solution
A sound diagnosis system using a MEMS microphone to collect sound data, process it through amplification and filtering, and analyze sound wave levels and actuator statuses to determine abnormal sounds and the specific replacement unit generating them, employing threshold values and statistical analysis to identify potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sound comparison is performed with known abnormal sounds, then detection accuracy for known abnormalities is improved, but the system cannot detect unknown abnormal sounds
Solution Approach 1:
The system changes the detection parameter from pattern matching against known sounds to statistical analysis of sound wave levels. By calculating statistic values (maximum, average, effective values) of sound wave levels and comparing them against thresholds, the system can detect any abnormal sound regardless of whether it was previously known, thus expanding detection range while maintaining accuracy
Solution Approach 2:
The patent replaces the mechanical approach of storing and comparing sound waveforms with a statistical field-based approach. Instead of storing actual sound patterns and performing waveform matching, the system uses statistical parameters (max value, average value, effective value) of sound wave levels, enabling detection of unknown abnormalities through threshold comparison rather than pattern recognition
2Reliability
If sound data is collected continuously, then detection capability is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts only the essential statistical parameters (maximum value, average value, effective value) from the continuous sound data, discarding the redundant raw waveform information. This extraction approach maintains detection capability while significantly reducing data processing complexity and storage requirements
Solution Approach 2:
The system performs preliminary statistical processing on sound data as it is collected, calculating maximum, average, and effective values in real-time. This preliminary action transforms raw data into condensed statistical parameters before storage or comparison, reducing subsequent processing complexity while maintaining full detection capability
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
Accurately detects and identifies unknown abnormal sounds with high precision, allowing for timely replacement of worn parts and preventing potential failures in image forming apparatuses.
Implementation Method 1
a reception unit configured to receive sound waves
Data Source
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AI summary
A sound diagnosis system includes: setting means (3012) configured to set a threshold for each of a plurality of time sections, based on a sound wave level in each of the plurality of time sections of a plurality of first sound data classified in a first group; and determination means (3013) configured to determine whether or not an abnormal sound is generated by calculating a comparison value for each of the plurality of time sections based on a sound wave level in each of the plurality of time sections of one or more second sound data classified in the first group, and comparing the comparison value for each of the plurality of time sections with the threshold value of the corresponding time section of the first group, the threshold value being set by the setting means.