## 1 Introduction  
With the advancement of information and communication technology, the era of the Internet of Things (IoT) has arrived. As a typical representative of this era, consumer electronics continue to develop rapidly [1, 2]. Certain consumer electronics, including smart TVs, smart air conditioners, and smart light bulbs, demonstrate substantial potential in the consumer market [3]. As consumer electronics increasingly advance toward greater intelligence, their automation and complexity continue to rise, which also gradually increases the likelihood of malfunctions. When faults occur, they can lead to significant economic losses and, in severe cases, even result in injury or loss of life. Table 1 lists the occurrence frequency of common faults in consumer electronics. Faults in consumer electronics are more challenging to detect than in most mechanical systems and structures due to their micro- to nano-scale complex architectures. Faults within electronic products do not necessarily lead to outright failure or loss of specified electrical performance or functionality, making it difficult to quantify the progression from fault occurrence to complete failure [4]. As shown in Figure 1, latent faults in consumer electronics are often difficult for users to identify. Fault diagnosis methods for consumer electronics primarily include user experience-based diagnostics and model-based diagnostics [5]. While these methods are capable of handling fault diagnosis tasks in most areas, the accuracy and stability of the diagnosis are often susceptible to external influences. Experience-based diagnostics are highly subjective, as users' experience, knowledge, and observational abilities are limited, which can lead to inaccuracies in the diagnosis and even the potential to overlook critical fault information [6]. Traditional model-based fault diagnosis methods often utilize fault information insufficiently and incompletely. Typically, models rely on single-modal information for product fault diagnosis or use knowledge graphs alone to query product faults. These approaches often struggle to produce accurate diagnostic results when faced with complex fault scenarios in consumer electronics. Methods that use multimodal information for fault diagnosis are significantly superior to traditional approaches. For example, [PERSON] et al. [7] proposed a deep learning-based multimodal sensor fusion method that detects equipment faults by integrating data from various sensors. The study specifically highlights the advantages of multimodal fusion over single-sensor data in detecting equipment anomalies, as multimodal fusion greatly enhances diagnostic accuracy. [PERSON] et al. [8] proposed a multimodal learning framework within an IoT environment that improves the accuracy of equipment fault prediction by integrating both low-quality and high-quality monitoring data. [PERSON] et al. [9] proposed an attention-based fault diagnosis method for multimodal data in chemical processes. By applying attention mechanisms to multimodal data, the model enhances its ability to select and integrate features from different modalities, thereby improving fault diagnosis accuracy. Compared to single-modal methods, multimodal fault diagnosis approaches offer significant advantages. In summary, diagnosing faults in consumer electronics can be achieved by integrating multiple modalities of information with knowledge graphs. We propose a multimodal knowledge graph-based method for fault diagnosis that effectively combines different types of information to achieve reliable results. Compared to traditional methods, our approach fully utilizes multimodal data, enabling comprehensive and multi-level diagnosis, which addresses the challenge of accurately identifying complex faults in consumer electronics. The main contributions of this paper are as follows:  
1. We propose an innovative fault diagnosis method for consumer electronics that fully utilizes multimodal fault information.