<p>Accurately evaluating the reliability of hydraulic excavators has long posed a significant challenge in construction machinery. Existing approaches have difficulty integrating failure information generated throughout the entire product lifecycle, including design, manufacturing, testing, operation, and maintenance. To address this limitation, this paper introduces a multi-phase failure data fusion method for reliability assessment of construction machinery systems, integrating simulation outputs, experimental results, historical failure records, and expert knowledge. An adaptive Markov chain Monte Carlo (MCMC) algorithm is applied to estimate model parameters and support system-level reliability evaluation. Case studies involving hydraulic excavators show that the proposed method enables quantitative reliability assessment for both complete machines and critical subsystems while effectively identifying components with low reliability. The approach provides a theoretical basis and practical framework for enterprises to rapidly and accurately assess the reliability of complex mechanical products, improve design quality, and enhance maintenance strategies, thereby delivering substantial engineering value.</p>