Monitoring laundry machine operation using machine learning analysis of acoustic transducer signal information
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Solution Overview
Problem
Current laundry machines lack reliable detection and diagnosis of failing components, leading to unexpected breakdowns and prolonged downtime due to the inability to provide timely warnings of imminent component failures.
Innovation Solution
A system utilizing acoustic sensors and machine learning to analyze sound data from laundry machines, generating operational status metrics and issuing maintenance alerts to prevent failures through predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional laundry machine operation monitoring is used, then the machine structure remains simple, but component failures cannot be detected early leading to prolonged downtime
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with acoustic field-based monitoring. Acoustic transducers capture sound waves generated by machine components during operation, and machine learning algorithms analyze these acoustic signals to detect component degradation and predict failures, substituting physical contact-based monitoring with non-contact acoustic sensing.
Solution Approach 2:
The patent introduces acoustic transducers as intermediary devices that mediate between the machine components and the monitoring system. These transducers convert mechanical vibrations and sounds from components into electrical signals that can be processed by machine learning algorithms, enabling indirect observation of component health without direct mechanical contact.
2Loss of time
If no monitoring system is implemented, then the device complexity remains low, but unexpected breakdowns occur leading to loss of time
Solution Approach 1:
The patent implements preliminary action by continuously monitoring acoustic signals from machine components and using machine learning models to predict potential failures before they occur. The system identifies patterns in acoustic data that indicate component degradation, allowing maintenance to be scheduled in advance and preventing unexpected breakdowns that would cause downtime.
3Measurement precision
If acoustic sensors and machine learning analysis are deployed, then component degradation can be detected early, but the system complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming raw acoustic signal data into meaningful diagnostic parameters through machine learning processing. The system extracts features from acoustic waveforms such as frequency spectra, amplitude modulations, and temporal patterns, converting complex sound wave data into interpretable metrics that indicate specific component conditions and degradation states.
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
Enables early detection of component degradation, reducing downtime by providing timely maintenance alerts and optimizing service schedules, thereby improving operational efficiency and reliability.
Implementation Method 1
The acoustic sensor includes at least a microphone configured to render a transduced electronic signal of sound waves sensed by the microphone during operation of the laundry machine
Data Source
AI summary
A system and method are described for carrying out machine learning-based automated laundry machine error/degraded status detection and maintenance by use of acoustic sensor data rendered by the laundry machines and applied to a plurality of machine learning models for use with acoustic data rendered by the laundry machines.