Semi-supervised learning for early detection and diagnosis of various air handling unit faults

• Published in: Energy and buildings Vol. 181; pp. 75 - 83
• Main Authors: Yan, Ke, Zhong, Chaowen, Ji, Zhiwei, Huang, Jing
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.12.2018; Elsevier BV
• Subjects: Air handling unit; Artificial intelligence; Diagnosis; Diagnostic systems; Fault detection; Fault detection and diagnosis; Fault diagnosis; HVAC; Learning algorithms; Machine learning; Semi-supervised learning; Support vector machine; Training; Support vector machine; Semi-supervised learning; Fault detection and diagnosis; Air handling unit

•The work introduces a novel semi-supervised approach to detect and diagnose faults for AHUs.•80% accuracy rate is reached using a training set with 8000 normal samples and only around 30 samples for each fault type.•This work addresses the tradeoff between the initial number of faulty samples and t...