Sound AI quality inspection and predictive maintenance built on machine anomaly sound analysis, from motor quality inspection and connector engagement sound to equipment fault diagnosis

Yes. Listen AI Industrial is designed for environments above 85 dB where impact wrenches, air guns, metal friction, and conveyor belts overlap. Deeply’s proprietary Foundation Model is built on more than 2.1 million hours of real-world factory noise data and more than 10 million process event data points, which lets it separate the faint engagement sounds workers previously judged by ear or fingertip from the surrounding noise.

The three core Listen AI Industrial solutions are rotating-equipment noise inspection, assembly-line connector engagement sound inspection, and equipment aging detection. Deployed cases also cover glass breakage and scratch detection, semiconductor component wear diagnosis, robot equipment aging analysis, large engine drive-unit aging analysis, and gas and pressure valve sound analysis.

Listen AI Industrial reports 99.87% accuracy on assembly-line connector engagement sound inspection, measured on global automaker Company H production lines in Korea and Mexico and published in a June 11, 2026 press release. Rotating-equipment noise inspection is separately reported at 99.78% accuracy with sub-second inspection time in earlier industrial coverage. The two figures cover different inspection types and measurement scopes, so they are not combined into a single claim.

It decides through Frequency-band Analysis. Listen AI Industrial distinguishes the Primary Lock in the 3–16 kHz band and the Secondary Lock in the 7–16 kHz band, then returns an OK or NG result. This is what catches half-clicks and soft connections that never fully engaged.

Yes. Listen AI Industrial inspects in about one second per product to run in-line 100% inspection, judging 6,000 products an hour and 4.3 million a month fully in-line. At the EOL stage it inspects every motor and actuator 24/7. Inspection time and judgement volume shift with the inspection type and the process conditions, so whether a given line’s Cycle Time is met is confirmed during the PoC.

Yes. Listen AI Industrial links inspection history to a MES (Manufacturing Execution System) or PLC so results are retained as traceable data. Deployment is supported as an edge analysis server setup or on-premise.

The setup pairs microphones chosen for the target sound and the site with an analysis server. Microphones are selected from fixed directional microphones, wristband microphones worn by workers, and array microphones for high-noise areas. The analysis server is supported as an edge analysis server setup or on-premise, and OK/NG results are linked in real time with an existing MES (Manufacturing Execution System) or PLC so they are retained as traceable data. In the connector engagement press release, installation is described as requiring no change to the existing line’s equipment, tools, or layout.

Yes. Rather than waiting weeks or months while site-specific defect data is collected, Listen AI Industrial begins initial evaluation on Deeply’s Foundation Model from the moment the hardware is installed. Collecting the engagement or operating sounds of the target equipment is enough to evaluate on top of it. This is scoped to the connector engagement press release and its manufacturing-specific Foundation Model, so it does not mean every deployment can proceed without data collection. PoC results then carry into production-line deployment.

They judge different things at different moments. Quality inspection judges the product being made: on the production line it detects continuous connector and bolt engagement work in real time, and at the EOL stage it inspects every motor and actuator before shipment to block defect escapes. Predictive maintenance (PdM) judges the equipment that makes the product rather than the product itself. Its purpose is to catch equipment anomaly signals such as bearings or gas leak sound early enough to prevent downtime, and it judges from continuous monitoring of the equipment rather than at one point in a process. Because the two cover different measurement scopes, accuracy figures are not carried from one to the other.