The rise of AI-powered classification techniques has ushered in a new era for
data-driven Fault Detection and Diagnosis in smart building systems. While
extensive research has championed supervised FDD approaches, the real-world
application of unsupervised methods remains limited. Among these, cluster
analysis stands out for its potential with Building Management System data.
This study introduces an unsupervised learning strategy to detect faults in
terminal air handling units and their associated systems. The methodology
involves pre-processing historical sensor data using Principal Component
Analysis to streamline dimensions. This is then followed by OPTICS clustering,
juxtaposed against k-means for comparison. The effectiveness of the proposed
strategy was gauged using several labeled datasets depicting various fault
scenarios and real-world building BMS data. Results showed that OPTICS
consistently surpassed k-means in accuracy across seasons. Notably, OPTICS
offers a unique visualization feature for users called reachability distance,
allowing a preview of detected clusters before setting thresholds. Moreover,
according to the results, while PCA is beneficial for reducing computational
costs and enhancing noise reduction, thereby generally improving the clarity of
cluster differentiation in reachability distance. It also has its limitations,
particularly in complex fault scenarios. In such cases, PCA's dimensionality
reduction may result in the loss of critical information, leading to some
clusters being less discernible or entirely undetected. These overlooked
clusters could be indicative of underlying faults, and their obscurity
represents a significant limitation of PCA when identifying potential fault
lines in intricate datasets.

基于 AI 分类技术的智能建筑系统中，研究了一种无监督学习策略以检测终端空气处理单元及其相关系统中的故障。使用主成分分析对历史传感器数据进行预处理，并与 k-means 进行对比。结果表明，优于 k-means 的 OPTICS 在准确性上在各个季节中保持一致，并且具有可视化功能。然而，PCA 的降维可能会导致丢失关键信息，从而在复杂故障情况下可能产生一些无法识别的聚类。