Robust real-time anomaly detection for aerial building machines using adaptive signal decomposition

Aerial Building Machine (ABM) is an integrated system for high-rise construction that faces challenges in real-time anomaly detection during operation. This paper proposes an adaptive detection approach combining Variational Mode Decomposition (VMD) and Wavelet Packet Energy Spectrum (WPES) to enhance operational safety. A real case of a 74-floor, 356-m project in Shenzhen demonstrates feasibility and effectiveness. Results show that: (1) the method adaptively detects anomalies in construction and lifting phases, verified by 12 sets of operational data; (2) it exhibits strong robustness, with maximum variation rates of warning indicators of 3.51 % and 2.48 % under Gaussian noise (1 %–5 %), compared to 8.53 % and 8.59 % for traditional methods; and (3) the proposed method demonstrates significant suppression of baseline drift, confining the variation rates of warning indicators below 5% under random-walk disturbances. The contribution lies in using VMD-based Adaptive WPES (VMD–AWPES) for adaptive signal analysis, enabling robust, intelligent anomaly detection in ABM operation. This paper addresses a gap in real-time anomaly detection and promotes intelligent safety management in high-rise construction.