The core objective of Active Vibration and Noise Control (AVNC) is to enhance system performance by generating real-time counter-phase signals of equal amplitude to cancel out vibration and noise interference from mechanical or structural systems. As the demand for low-noise, low-vibration environments grows in fields such as new energy vehicles (NEVs), aerospace, and high-precision manufacturing, traditional AVNC methods—which rely on precise linear models and have poor adaptability to nonlinear and time-varying conditions—struggle to meet the dynamic requirements of complex engineering scenarios. However, with advancements in artificial intelligence (AI) technology, AI-driven Active Vibration and Noise Control (AI-AVNC) technology has garnered significant attention from both industry and academia. Based on a thorough investigation into the state-of-the-art AI-AVNC methods, this survey has made the following contributions: (1) Introducing the theoretical foundations of AVNC and its historical development; (2) Classifying existing AI-AVNC methods from the perspective of control strategies; (3) Analyzing engineering applications of AI-AVNC; (4) Discussing current technical challenges and future development trends of AI-AVNC.

In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential for ensuring driving safety and system reliability. Although considerable progress has been made in fault diagnosis techniques related to IWMs, a systematic review in this area is still lacking. To address this gap, this paper provides a comprehensive review of fault diagnosis techniques for IWMs. First, typical faults in IWMs are analyzed with a focus on their unique structural and failure characteristics. Then, the applications and recent research progress of three major categories of fault diagnosis approaches—model-based, signal-based, and knowledge-based methods—in the context of IWMs are critically reviewed. Finally, key challenges and pain points in IWM diagnosis are discussed, along with promising future research directions.