数据收集难：采集标准不统一、缺乏结构化存储
08 / Solution detail
Manufacturing Acoustic Anomaly Detection Solution
A TDNN-driven, edge-deployed intelligent acoustic anomaly detection solution for manufacturing QC. Five-layer architecture (data source / edge / algorithm engine / recognition / integration) with millisecond inference and full-volume online inspection.
Solution contents
00
Overview
Deliverables
Manufacturing Acoustic Anomaly Detection Solution
1Microphone-array data acquisition layer
2Jetson Orin NX edge computing box
3TDNN algorithm service engine
4Anomaly recognition and barcode tracing system
5MES integration with full-process quality traceability
5
Deliverables
6
delivery chapters
01
Acoustic Anomaly Detection Technology Evolution and Industry Drivers
A three-stage evolution (manual to sensor to AI) driven by market competition, scale expansion, stricter standards, and rising labor costs.
Capability points
1三代技术演进：人工听诊 → 传感器应用 → AI智能检测
2市场竞争加剧：品牌质量竞争延伸至声学体验
3产量规模扩大：大批量生产带来漏检风险
4法规与标准趋严：异音检出指标要求日益严格
5人力成本攀升：声学质检人员短缺、培训周期长
02
Acoustic QC Pain Points and Construction Tasks
异常判断难：异响边界模糊、主观性强、背景噪声严重
效率瓶颈：单件检测耗时长、产线节拍受限
异响声音智能识别：实时分析、自动输出合格/不合格判断
声音数据结构化、标准化管理：建立数据资产体系
Three pain points (data collection, anomaly judgment, efficiency) and three construction tasks (smart recognition, data standardization, barcode tracing).
03
Edge-Cloud Collaborative System Architecture
A five-layer closed loop: data source to edge box to algorithm engine to recognition system to MES integration.
- 数据源层：麦克风阵列实时采集 + 音频片段自动切割
- 边缘计算层：本地推理、降低带宽、毫秒级响应
- 算法服务引擎层：TDNN深度学习推理服务
- 异响识别系统层：业务应用、可视化、条码追溯
- 系统对接层：MES集成、生成条码、全流程质量追溯
04
Edge Computing Box and Hardware Specifications
Jetson Orin NX delivering 70 TOPS, preinstalled with Ubuntu 20.04 LTS, 5x RJ45 PoE in an industrial-grade enclosure.
- Jetson Orin NX 核心板，70 TOPS AI算力
- 1024-Core NVIDIA Ampere GPU + 6-core Arm Cortex-A78AE CPU
- 8GB 128-bit LPDDR5 内存，102.4 GB/s 内存带宽
- 5× RJ45 千兆网口，PoE供电支持
- 预装 Ubuntu 20.04 LTS，工业级设计
05
TDNN Core Algorithm and Audio Feature Pipeline
TDNN (Time Delay Neural Network) with a pre-emphasis / framing / windowing / FFT / Mel / Fbank pipeline, with online learning and incremental training.
01
TDNN（时延神经网络）算法：时序建模、抗噪、轻量推理
Core
02
预处理流水线：预加重 → 分帧(25ms) → 加窗(汉明窗)
03
频域特征提取：FFT → Mel滤波 → Log运算 → Fbank特征
04
支持在线学习与增量训练，模型可持续迭代
Implementation flow
06
O&M Monitoring and Comprehensive Benefits
Capability points
01
服务运行监控：状态、排队数量、平均处理时长
02
服务调用统计：调用次数（总/成功/失败）、调用时长分析
03
服务调用日志：API请求/响应参数、原始数据留存、结果归档
04
检测效率 ≥ 实时，匹配产线节拍，支持全量在线检测
05
数据管理结构化，系统可用性持续监控
Three O&M capabilities (service monitoring, call statistics, call logs) delivering four-dimensional gains: efficiency, labor, data, and stability.
5
Capability points
06
Implementation analysis
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