###### Abstract  
The stable operation of electrical equipment is critical for industrial safety, yet traditional anomaly detection methods often suffer from limitations, such as high resource demands, dependency on expert knowledge, and lack of real-world capabilities. To address these challenges, this article proposes an unsupervised anomaly detection method for electrical equipment, utilizing audio latent representation and a parallel attention mechanism. The framework employs an autoencoder to extract low-dimensional features from audio signals and introduces a phase-aware parallel attention block to dynamically weight these features for an improved anomaly sensitivity. With adversarial training and a dual-encoding mechanism, the proposed method demonstrates robust performance in complex scenarios. Using public datasets (MIMII and ToyADMOS) and our collected real-world wind turbine data, it achieves high AUC scores, surpassing the best baselines, which demonstrates our framework design is suitable for industrial applications. anomaly detection; mechanical fault diagnosis; unsupervised learning; audio signal analysis; electrical safety 25 June 2025 28  
To address these issues, many researchers have also explored various types of signals for anomaly detection, including vibration signals, force signals, audio signals and others. Vibration signals [4; 5] have been widely used for condition monitoring and fault diagnosis in rotating machinery, such as motors, turbines, and bearings. These signals provide rich information about the mechanical health of equipment, enabling the detection of imbalances, misalignments, and wear. By using this signal, techniques such as time-domain analysis, frequency-domain analysis, and time-frequency analysis have been employed to extract meaningful features from vibration data. Similarly, force signals, which measure the interaction forces between components, are particularly useful in detecting anomalies in mechanical systems subjected to dynamic loads [6]. For instance, force signals can reveal issues such as excessive friction, impact forces, or structural deformations. Using force signals, signal processing methods, including wavelet transforms [7] and empirical mode decomposition [8], have been applied to analyze these signals effectively. In the field of anomaly detection for electrical equipment, audio signals, as an important monitoring tool, offer advantages such as non-invasiveness, real-time capabilities, and rich information. Electrical equipment produces different sound characteristics during normal operation and abnormal states. By analyzing these audio signals, the abnormal states of equipment can be effectively identified. However, audio signals are typically characterized by high dimensionality, nonlinearity, and time-varying properties [9], making it challenging to achieve effective anomaly detection using traditional signal processing methods. Therefore, how to extract effective feature representations from audio signals has become a key issue in this field. Recently, deep learning technologies have made significant progress in audio signal processing. Specifically, representation learning methods based on autoencoders [10] can extract low-dimensional latent representations from high-dimensional data in an unsupervised manner, effectively reducing data complexity while retaining important feature information. Additionally, the attention mechanism [11], as an emerging deep learning technology, can automatically learn the important parts of data, enhancing the model's representation capability and generalization performance. Integrating autoencoders with attention mechanisms is expected to achieve better results in anomaly detection for electrical equipment. In this work, we propose an unsupervised anomaly detection method based on audio latent representation and a parallel attention mechanism, aiming to improve the accuracy and robustness of anomaly detection for electrical equipment.