US 11,747,780 B2
Monitoring laundry machine operation using machine learning analysis of acoustic transducer signal information
Richard Baazi, Weston, FL (US); Mathias Takahashi Albert, Chicago, IL (US); Dattaprabodh Narhar Godbole, Plymouth, MN (US); John Boyle, Tempe, AZ (US); Joseph Weedon Hainline, III, Kirkwood, MO (US); Nilesh Jayantilal Nahar, Rotterdam (NL); Stephane Serge Jean-Pierre Bodnar, Montaulin (FR); Angie Katherinne Sandoval Lopez, Miami, FL (US); David Vernon Neitzke, Omro, WI (US); Andrew Michael LaPoint, Oshkosh, WI (US); Mitchell Wayne Zastrow, Fond Du Lac, WI (US); Julie Michele Schmidt, Palatine, IL (US); Mark Allen Kovscek, Brownsville, PA (US); Garry Maurice Rosenfeldt, Buffalo Grove, IL (US); and Nancy Ann Haring, Glen Ellyn, IL (US)
Assigned to Alliance Laundry Systems LLC, Ripon, WI (US)
Filed by Alliance Laundry Systems LLC, Ripon, WI (US)
Filed on Nov. 16, 2021, as Appl. No. 17/527,444.
Claims priority of provisional application 63/114,878, filed on Nov. 17, 2020.
Prior Publication US 2022/0155739 A1, May 19, 2022
Int. Cl. G05B 19/042 (2006.01); G06N 20/00 (2019.01)
CPC G05B 19/042 (2013.01) [G06N 20/00 (2019.01); G05B 2219/2633 (2013.01)] 16 Claims
OG exemplary drawing
 
9. A method carried out by a system comprising:
an acoustic sensor data interface; and
a machine learning-based processing system;
wherein the method comprises:
receiving, by the acoustic sensor data interface, digital acoustic signal data corresponding to sensed sound from components of a laundry machine during operation of the laundry machine, wherein 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; and
rendering, by the machine learning-based processing system, a reason code indicative of a current operational status of the laundry machine,
wherein the processing system comprises a processor and a non-transitory computer readable medium including computer-executable instructions that, when executed by the processor, facilitate carrying out a method during the rendering that comprises:
receiving an acoustic data set rendered from the transduced electronic signal;
rendering functional metric parameter values indicative of an operational status of the laundry machine by applying machine learning models to the acoustic data set;
identifying, by applying a set of conditions to a set of predictive maintenance indicators derived from the functional metric parameter values, the reason code corresponding to a degraded operational status of the laundry machine; and
issuing, in accordance with the identifying, an electronic maintenance alert relating to a remedial operation for the laundry machine,
wherein the machine learning models define acoustic signatures for corresponding normal functions performed by the laundry machine, and
wherein the machine learning models define a percentage of a total operational time for processing a laundry load by the laundry machine where an identified normal function is acoustically sensed and identifiable using a corresponding acoustic signature of the machine learning models.