Abstract Details
212 / 2025-06-14 14:48:03
A Multidimensional Data-Driven Algorithm for Operational Failure Prediction
Fault prediction,remote operation and maintenance,joint knowledge and data-driven
Final Paper
Data resource wastage, low equipment utilization, traffic overloads during bursts, memory leaks, and network device bandwidth saturation constitute major challenges hindering the intelligent development and operation of data centers worldwide. To enhance data resource utilization, detect potential equipment failures, and improve operational efficiency within aerospace flight control systems, this paper proposes a multidimensional data-driven fault prediction algorithm. A prediction model is constructed utilizing ARIMA and ConvLSTM techniques. This approach optimizes feature selection while simultaneously enhancing the algorithm's adaptability to complex data patterns and sudden anomalies. The hybrid spatiotemporal convolutional model effectively accommodates diverse monitoring metrics and demonstrates robust predictive capabilities for highly dynamic resource operation patterns. Experiments were conducted using a multidimensional dataset derived from the equipment. The results demonstrate that the proposed fault prediction method offers flexible adaptability across multiple application scenarios and achieves high overall fault prediction accuracy.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
