Fault diagnosis method for HVAC sensors based on improved 1-D CNN model and wavelet clustering analysis

Introduction Heating, Ventilation and Air Conditioning (HVAC) sensor fault diagnosis is essential for ensuring the reliability and energy efficiency of intelligent building systems. However, existing diagnostic methods suffer from insufficient adaptability to multi-scale features, weak temporal dependency modeling, and poor generalization under small samples, and are highly sensitive to Gaussian noise. Method To address these limitations, this study proposes a fault diagnosis method that integrates an improved one-dimensional convolutional neural network (1-D CNN) with wavelet packet clustering. First, a multi-scale convolution module is designed using parallel 3/5/7 convolution kernels and residual connections to extract temporal features across different receptive fields. Then, wavelet packet decomposition is used to divide the original signal into eight frequency bands and construct energy feature vectors. K-means clustering is performed in an unsupervised manner, and Softmax-based weight fusion is used to realize end-to-end diagnosis with low computational overhead. Results Experimental results show that the proposed method achieves a diagnostic accuracy of 97.84% and an F1-score of 0.97. Under 30% Gaussian white noise, the area under the curve decreases by only 4%, and the instantaneous robustness drop increases by 0.01 within the 10%-30% noise range, demonstrating strong noise resistance and generalized learning capability. Discussion and Conclusion The proposed method effectively balances feature-scale adaptability, temporal modeling, and robustness under noisy and small-sample conditions. With low inference complexity and high diagnostic stability, it provides a feasible paradigm for real-time fault detection and reliable operation and maintenance in intelligent building HVAC systems.