Q: Why is it necessary to “listen” when a motor leaves the factory? Isn’t a vibration sensor sufficient? A: Vibration is highly sensitive to imbalance and looseness; acoustics, on the other hand, are more sensitive to early defects such as bearing ball scratches, cavitation, electromagnetic whining, and abnormal noises from gear meshing—defects that generate very weak vibration signals but can be detected by the human ear or microphones. Q: Then just grab a microphone, record something, and run it through AI—wouldn’t that do the trick? A: The Three Major Pitfalls in Industrial Settings—
1. The workshop has a background noise level of 80–90 dB—did you record the sound of the motor or the press next door?
2. Holding the microphone, the distance is 2 cm today and 5 cm tomorrow, with a feature value difference of 20%.
3. When a person shakes, structural noise directly contaminates the signal. So, Shanghai Chuangdan Electronics’ industrial-grade AI voiceprint detection workstation is being reconfigured: no longer handheld—it’s now permanently “welded” onto the workstation.
·150×150×20mm rigid base, ≥800g counterweight;
Pan: 50mm / Tilt: ±30° / Rotation: ±45°, with full-scale locking;
🟥 Brass nickel-plated probe housing + cardioid directional microphone capsule; a windscreen/sound-absorbing cone can be added or removed at the front end.
🟥 Edge-side local inference; the results are directly sent to the PLC via IO or network port for sorting. Its acoustic feature extraction pipeline covers Mel spectrogram, cepstrum, and psychoacoustic parameters such as loudness and sharpness. Embedded signal chains based on CMSIS-DSP and models like SVM and lightweight CNN have already established mature precedents in industrial acoustic edge applications. The specific model and dimensions for this workstation will be determined based on on-site retesting during the POC phase.
The prototype has now been completed, and we are currently recruiting POC customers.