SmartQuality AI Acoustics · Redefining Industrial Quality Inspection
Multi-dimensional acoustic metrics and an AI deep-learning dual engine turn ear-dependent quality inspection into a reproducible, traceable, continuously evolving acoustic quality system beside the line — cycle-time optimization, abnormal noise traceability.
100%
100% line inspection
500ms
Abnormal noise decisions
7×24
Never tires
Tailored
Models developed on demand
Industry Challenge
Judging abnormal noise by ear is falling further and further behind the demands of scale and high-quality traceability.
01 · Labor Dependence
They tire, they leave, they retire
Training a listening inspector takes 3–6 months, and after 2 hours of continuous listening high-frequency sensitivity drops sharply; when the veteran retires, the standard for the whole line retires with them.
02 · Offline Sampling
A data "island" in the sound-proof room
A sound-proof room costs 500,000–1,000,000 CNY each and turns inspection into an offline island — sampling covers only 5%, so some products reach the market without acoustic inspection and the data asset is lost.
03 · Subjective Drift
Up and down, never aligned
"Abnormal noise" is inherently subjective — standards differ by listener, shift and plant, leaving no evidence when a customer complaint arrives.
04 · Data Black Hole
Can't say what's wrong
"Sounds off" but no one can say why: quality data stays locked in the ear, never digitized, never fed back to the process.
Four Capabilities
Beyond OK / NG decisions, it classifies and locates abnormal noise — turning what you hear into a traceable quality conclusion you can feed back to the process.
01 · Accuracy
Objective Metrics + AI Cross-Validation
Backed by objective numbers and time-frequency images, conclusions align with mainstream evaluation methods and cut both false calls and misses.
02 · Coverage
100% Every-Unit Inspection
No sampling, no misses: every unit goes through acoustic inspection without adding to cycle time.
03 · Classification
More Than OK / NG
Identifies the type of abnormal noise — friction, impact, squeal, resonance — each with its own sensitive-metric system.
04 · Localization
Pinpoint Position and Station
Noise type → station → process root cause: not just sorting out defects, but helping you improve yield.
Core Engine
The mechanism-based model is fast, accurate and explainable; deep learning sees the whole picture and catches hidden anomalies. They complement each other — you need both.
Layer 1 · Mechanism-Based Engine
Objective metrics you can explain and benchmark
Breaks sound into a set of objective numbers and time-frequency images, as reviewable as a lab report.
Layer 2 · AI Deep-Learning Engine
End-to-end perception that catches hidden anomalies
Instead of relying on any single metric, it perceives acoustic features end to end, catching subtle anomalies that neither single metrics nor the human ear can.
Pipeline
01
Perception
Contact + non-contact
4 channels, 16-bit
44.1kHz synchronous capture
02
Inference
Mechanism-based metrics + AI perception
Dual engine reads in parallel
03
Explanation
Objective metrics + plots
Reviewable and report-ready
04
Action
OK / NG decision
PLC interlock + MES reporting
05
Evolution
Data feedback → iteration
Models keep improving
In-House Hardware
The contact sensor couples directly to structural vibration and isolates ambient airborne noise at the physical level — the core hardware capability that sets us apart from competitors.
VH23 · Contact Sensor
An electronic "stethoscope"
Piezoelectric accelerometer (IEPE) mechanically coupled to capture vibration directly, suppressing ambient airborne noise at the physical level.
SV30 · Data Acquisition Unit
Four-Channel Synchronous Capture
16-bit / 44.1kHz sampling with mixed contact and non-contact inputs; multiple units run synchronously on the line.
HP AIPC · Edge Computing
Data Never Leaves the Plant
Processed locally on the edge node, meeting IATF 16949 traceability and audit requirements.
Target Products · Applications
Each product maps to its own sensitive metrics, covering auto parts plus broader industrial and consumer-electronics scenarios.
Cooling Fan · CFM
Cooling Fan Module
Blade-pass noise, imbalance, rubbing, electromagnetic squeal. Multi-speed sweep separates airborne and structural contributions.
Power Liftgate · PLG
Power Liftgate Drive
Start/stop impact, squeal, friction, sticking. Left and right drives synchronized, full travel segmented, latch event detection.
Steering Motor · EPS
Electric Power Steering Motor
Electromagnetic orders, bearings, imbalance. Current/speed/load synchronized, forward-reverse and control squeal detection.
Door Module · DM
Door Module
Friction, rail noise, resonance. Window-travel position, seal load and carrier-plate resonance detection.
Side Door Drive · SDD
Power Side Door Drive
Motor noise, brake friction, impact. Open/close direction, braking, end-stop and impact detection.
Seat · SEAT
Seat-Specific
Friction, impact, sticking, resonance. Multi-channel + stage + position signals; for complex mechanisms we recommend a phase-2 rollout.
More Applications · Beyond Automotive
Vehicle Acoustics
NVH · Cabin
Vehicle road tests, cabin noise localization and component traceability, covering vehicle-level acoustic quality.
OEM / Tier1
Seats · Air Vents · Headrests
Abnormal noise inspection for multiple part families, from a single product to full coverage.
3C Consumer Electronics
Laptops · Touchpads
High-speed cooling-fan noise, touchpad click noise, keyboard travel noise — acoustic quality inspection for consumer electronics.
Electromechanical Equipment O&M
Bearings · Gears · Motors
Bearing defects, gear meshing, electromagnetic noise — online condition monitoring and fault pre-warning for production equipment.
Customers and Applications
From foreign-invested auto-parts makers to domestic OEMs, our customers include German, Japanese and US companies as well as SOEs and private firms, across auto parts and electromechanical equipment.
Sunshade · German firm
Motor / Rail
Motor noise and rail friction noise inspection.
Steering System · Japanese firm
Steering Actuator / Gears
Steering actuator noise and gear-meshing noise inspection.
Braking System · US firm
Brake Actuator / Friction
Brake actuator noise and friction noise inspection.
Power Headrest
Adjust Motor / Transmission
Adjustment motor noise and drivetrain noise inspection.
Actuator
Gear Drive / Impact
Gear-drive noise and impact noise inspection.
Window Regulator
Lift Motor / Rail
Lift motor noise and rail friction noise inspection.
Electronic Water Pump
Impeller / Bearing / Electromagnetic
Impeller noise, bearing defects and electromagnetic noise inspection.
Compressor
Mechanical Vibration / Running
Mechanical vibration and running noise inspection.
Delivery Models
From mechanism-based analysis to full-scale models, from line inspection to quality R&D
Mechanism-Based Inspection System
Tinia mechanism engine + all-in-one appliance (acquisition unit + contact sensor): fast and stable, ideal for getting a first line up quickly.
Mechanism-Based + AI
Adds a deep-learning engine on top of the mechanism layer to catch hidden anomalies and keep evolving — built for scaling across many lines.
Full-Service
Data labeling + algorithm tuning + end-to-end delivery + upgrades and maintenance: turnkey, built for lighthouse and benchmark projects.
Pricing per single node / full line / multi-node · tax included · subject to the tested solution and the contract
Why Us
| Dimension | Manual Listening | Simple Thresholds | Conventional AI | SmartQuality |
|---|---|---|---|---|
| Basis for decision | Veteran's "gut feel" | A single metric over limit | A single algorithm threshold | Dual-engine joint decision |
| Consistency | Differs by shift | Stable and consistent | Stable and consistent | Stable + cross-validated |
| Explainability | Can't say | Metrics are traceable | Black box | Metrics + plots + confidence |
| Coverage | By experience | Catches only out-of-limit | One algorithm | Each noise type has its own sensitive metrics |
| Unknown anomalies | Up to luck | None | Blind outside the training set | Feature distance flags for review |
| Continuous evolution | Retires eventually | Fixed threshold | Manual retraining required | Closed-loop feedback, sharper with use |
| Process feedback | Only sorts out defects | Knows over/under | Knows pass/fail | Type → station → yield improvement |
Inspection Report
No conclusion from a single number: multiple metrics and multiple engines combine into a traceable acoustic quality conclusion.
Behind every conclusion: metrics and plots
Objective metrics and time-frequency images from the mechanism model, plus end-to-end perception from the deep-learning engine, all feed into the joint decision mechanism.
Weighted fusion by the "sensitive-metric system" of each noise type delivers: whether there is abnormal noise, its type, its severity and improvement advice.
Every conclusion can be traced, reviewed and fed back to the process — not just sorting out defects, but helping you improve yield.
SmartQuality AI Inspection Report
No. SQ-2026-XXXX✓ Dual-engine joint decision · traceable
Value
Replacing manual labor is the hardest return; fewer customer complaints is the biggest lever — value varies by industry, line scale and NG rate.
Inspection Coverage
From 1–5% sampling to 100% every-unit inspection: miss risk disappears with coverage.
Labor Replacement
Dedicated listeners → part-time monitoring + review, manual inspection cost drops sharply.
Decision Consistency
Subjective ears and shift-to-shift drift → objective metrics + AI, stable, consistent, traceable.
Complaints and Recalls
100% inspection intercepts defects and the data feeds back to the process, so complaint and recall risk drops significantly.
Technical Moat
Algorithm
CNN+Transformer
Hybrid architecture, semantic feature space, known + unknown dual channels, closed-loop continuous learning.
Engineering
Contact-sensing integration
Sensor design and integration, adaptive line noise reduction, multi-system integration, large-scale deployment experience.
Data
Industry feature library
An industry abnormal-noise feature library and multi-industry deployment know-how built with leading customers.
Industry
Deep customer validation
Validated through deep work with leading customers, accumulated industry know-how and a growing customer track record.
Implementation Roadmap
Risk first, trust built step by step — a standardized delivery process from kickoff to final acceptance.
S0
Kickoff
Team setup
Roles confirmed
S1
Data
Data routing
Channel mapping
S2
Labeling
Defect dictionary
Ground-truth labeling
S3
Training
Mechanism tuning
Model validation
S4
Pre-Acceptance
Frozen blind test
Metric validation
S5
Trial Run
Shadow run
Compared with manual
S6
Final Acceptance
Mass-production validation
Acceptance signed
S7
Wrap-Up
Standardization
Scale-up advice
FAQ
Run your real samples through it first, let the objective metrics and the inspection report make the case, and then talk about rollout. Specific metrics and commercial terms are subject to the tested solution and the contract.
