# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 1 Introduction  
BMUs are typically installed atop skyscrapers to transport maintenance personnel for exterior wall servicing [1]. As an aerial work platform, any safety mechanism failure, operational anomaly, or unexpected shutdown could endanger workers' lives and incur catastrophic consequences [2, 3]. Therefore, real-time monitoring and fault diagnosis are critical to minimize downtime and ensure operational reliability [4, 5]. However, evolving architectural designs of commercial high-rises and diversified maintenance requirements have led to increasingly complex BMU configurations and structures. This complexity generates intricate multi-source data relationships, including the relationship between equipment structure and operating data, the relationship between operating data and fault status, and the relationship between fault status and fault causes [6]. Accurately modeling these multidimensional relationships and enabling rapid diagnostic outputs have become pivotal research challenges in fault diagnosis. In recent years, with the maturation of multi-source information sensing and AI-assisted data mining technologies, knowledge-driven (KD) and data-driven (DD) approaches have been widely adopted in equipment condition monitoring (CM) and fault detection and diagnosis (FDD) [7; 8; 9]. DD fault diagnosis methods employ machine learning or deep learning algorithms to extract and identify fault patterns from operational datasets, though they often suffer from poor interpretability. In contrast, KD approaches primarily rely on textual rules, enabling networked and structured representation of domain knowledge. These methods equip machines with the capability to comprehend textual information and expert knowledge, thereby enhancing data interpretability, particularly for addressing the fundamental challenge of enabling computers to acquire domain expertise and understand the physical significance of data [10]. Beyond mere knowledge representation, KD techniques can also perform querying and reasoning based on established facts through semantic rules, thereby deriving new conclusions. Consequently, current research focuses on hybridizing DD and KD methodologies to strengthen a system's ability to accurately diagnose faults [11; 12]. For example, [PERSON] et al. [13] proposed a hybrid knowledge-based DD method for the distributed fault diagnosis of heterogeneous multi-agent systems, improving the decision-making ability of heterogeneous MAS. [PERSON] et al. [14] combined first principle knowledge with data for fault detection in HVAC systems to improve the performance of different clustering algorithms, outperforming purely DD methods. [PERSON] et al. [15] combined a data-driven symbol correction network with a model-driven orthogonal approximate message-passing network (OAMP Net) to correct detection errors in each iteration of the model-driven approach. However, this hybrid driving method requires the input of rich domain knowledge, accurate theoretical models, and massive system operation datasets, which are often very difficult to obtain. This relies on the long-term operation of the equipment and data accumulation [16; 17; 18]. In the case of insufficient public data samples, IoT technology is an effective method for obtaining big data samples [19; 20]. For example, [PERSON] et al. [21] used IoT technology to obtain real-time data of photovoltaic systems to monitor their operating status and evaluate potential faults. IoT technology integrates physical information sensing, ultra-long-distance data transmission, and powerful cloud computing capabilities, effectively overcoming the coverage and real-time limitations of traditional data acquisition methods. Particularly suitable for remotely distributed systems like BMUs, it enables comprehensive data collection from all networked devices. The collected data are stored and processed through servers for model training and knowledge base updates. Real-time data acquisition further enhances diagnostic efficiency while significantly reducing equipment downtime.