This paper addresses the harsh operating conditions of high temperature, high dust, and heavy load in cement industry by proposing, an intelligent equipment fault diagnosis technology based on vibration monitoring and spectrum analysis. It systematically reviews typical cases of predictive,  maintenance for various key equipment in cement production analyzing them from four dimensions: fault phenomena, spectral characteristics, diagnostic conclusions, and maintenance feedback. Practical cases demonstrate that the intelligent diagnostic technology based on vibration monitoring and spectrum analysis can identify early faults such as bearing defects, misalignment, and gear damage in advance. Future research can further optimize fault feature extraction accuracy by combining deep learning algorithms and extend to the field of remaining useful life prediction for equipment, providing more comprehensive technical support for digital transformation of the cement industry. Furthermore, this methodology can be extended to heavy industries such as mining machinery and metallurgical equipment, facilitating high-quality development of the manufacturing industry.