理想TOP2·2025-10-17 13:44Core Viewpoint - The article discusses the challenges and advancements in identifying abnormal noises in vehicles, emphasizing the complexity of vehicle components and the innovative use of AI for diagnostics [2][3]. Group 1: Challenges in Noise Diagnosis - The complexity of components: Over 200 parts in vehicles can be sources of abnormal noises, each producing unique sound characteristics that require precise analysis [3]. - Environmental interference: Normal operational sounds overlap with abnormal noises, making it difficult to isolate specific signals [3]. - Dynamic diagnosis issues: Many abnormal noises are intermittent, complicating the identification process for technicians [3]. Group 2: Technological Solutions - Step 1: Sound digitization: Utilizing Fourier transform and signal processing techniques to convert chaotic sound waves into clear time-frequency graphs, creating unique "waveform fingerprints" for each noise [4]. - Step 2: Massive data training: The development of a self-owned NVH model that incorporates decades of diagnostic experience into an algorithm, allowing real-time analysis and continuous self-optimization [5]. - Step 3: Real-time fault diagnosis: The system operates in real-time on the vehicle, using edge computing to complete diagnostics within one minute and monitor multiple components simultaneously [6]. Group 3: Impact and Benefits - The deployed model helps identify over 30 hidden faults monthly with a diagnostic accuracy of 100%, saving over 3 million yuan in claims costs annually [7]. - The NVH diagnostic model reduces the time cost for after-sales technical support in resolving noise issues by 99%, enhancing customer service experiences [7].