TIDI - portable Thermoacoustic Imaging Diagnostic Instrument

Next-generation AI-powered acoustic imaging sensor integrated with DCNN neural network, achieving sound-thermal-visible light multimodal fusion diagnosis for precise localization of partial discharge, gas leakage and mechanical abnormal noise

AI Acoustic Imaging Sensor

Product Overview

TIDI (Portable Thermoacoustic Imaging Intelligent Defect Diagnostic Instrument) integrates acoustic imaging, infrared thermal imaging, and visible light imaging technologies into a single portable device.

Locates abnormal sound sources via microphone arrays, displays real-time temperature distribution with infrared video, and combines multi-modal fault diagnosis (sound/temperature/visual) with automated report generation.

Accurately identifies discharges, gas leaks, thermal defects, and visual anomalies. Significantly improves diagnostic efficiency and accuracy in complex scenarios, enabling rapid decision-making and enhanced equipment safety.

Core Features

Portable & Efficient

Portable & Efficient

Replaces traditional separate devices (acoustic imager, IR camera, photo/video recorder) with one-click operation, significantly improving inspection efficiency

Cross-Identification

Multi-Sensor Cross-Identification of Defects

Combines acoustic localization, thermal distribution detection, and visible light for multi-modal fault diagnosis, enhancing accuracy and reliability

One-Click Reporting

AI-Assisted One-Click Reporting

Auto-identifies equipment/parts via AI after imaging, rapidly locates anomalies, and generates defect reports with one click

Environmental Adaptability

Strong Environmental Adaptability

IR works in all lighting; acoustic imaging penetrates smoke/rain/fog. Effective day/night and in harsh weather conditions

Application Scenarios

Electric Power Inspection

Electric Power Inspection

Partial Discharge & Overheating Monitoring: Detects partial discharge and thermal anomalies in substations and transmission equipment

Intelligent O&M: AI-powered defect classification and risk prioritization for critical issues

Petrochemical Safety Inspection

Petrochemical Safety Inspection

Gas Leakage Detection: Identifies VOCs such as methane and benzene through infrared thermal imaging, enabling non-contact remote scanning

Acoustic Verification: Captures gas escape acoustic waves through ultrasonic for dual verification to avoid misjudgment

Railway Maintenance Inspection

Railway Maintenance Inspection

Mechanical Fault Diagnosis: Identifies abnormal noises from bearings combined with thermal imaging for early warning

Brake System Inspection: Acoustic detection of pneumatic leaks with thermoacoustic linkage for derailment risk warning

Thermoacoustic vs Single-Mode Detection

Detection Aspect Acoustic Imaging Infrared Imaging Thermoacoustic (TIDI) Advantages
Detection Accuracy Medium Limited Excellent Dual verification to improve accuracy
Detection Speed Limited Fast Excellent Rapid imaging with balanced depth and efficiency
Anti-interference Capability Medium Limited Excellent High-temp/low-temp/EMI resistant
Defect Type Coverage Medium Medium Excellent Acoustic, infrared and fusion detection
User-friendliness Medium Medium Excellent No coupling agent required, AI-powered automatic analysis
Cost-Benefit Medium Medium Excellent Single device integrates multi-technology to reduce cost

Technical Specifications

Imaging Modes
Acoustic+Thermal+Visual
Multi-modal Fusion
Detection Capability
PartialDischarge
Gas Leakage Detection
AI Processing
DCNNModel
Deep Neural Network
Noise Reduction
DSPMulti-Stage
Real-time Processing
Report Generation
OneClick
AI-Assisted Output
Operation Mode
AllWeather
Day/Night Capable

Key Technologies

Multimodal Fusion Excitation Technology

By integrating the acoustic source localization capability of acoustic imaging systems, temperature distribution detection function of infrared thermal imagers, and visible light photography/video recording capabilities, this approach enables intelligent multimodal defect diagnosis, enhancing inspection accuracy.

DCNN Deep Convolutional Neural Network Model

By combining photo capture with the DCNN deep convolutional neural network model, the system autonomously identifies equipment/part component types, enabling rapid anomaly detection while generating defect reports with a single click.

DSP Multi-Stage Noise Reduction

The system employs DSP-based multi-stage noise reduction technology to eliminate noise in infrared and acoustic signals in real time, thereby enhancing defect detection accuracy and improving signal-to-noise ratio.

Adaptive Environmental Compensation Algorithm

Infrared technology is not limited by light conditions, while acoustic imaging can penetrate smoke and fog. The combination of these technologies with adaptive environmental compensation enables efficient detection during daytime, nighttime, and adverse weather conditions.

Inspection Workflow

  • Mobile Operations: Portable design with wireless data transmission significantly enhances operational efficiency
  • AI Real-time Diagnosis: Scanning automatically completes defect classification with real-time automated diagnosis
  • Batch Processing: Supports simultaneous scanning of multiple detection points for automatic archiving
  • Cloud Collaboration: Detection data automatically synchronized to management platform with automated task scheduling
  • Defect Classification Warning: Identifies risk level of defects and prioritizes critical issues
Inspection Workflow

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